
==== Front
BMC Cancer
BMC Cancer
BMC Cancer
1471-2407
BioMed Central London

39256698
12863
10.1186/s12885-024-12863-w
Research
Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation
Cui Jiaxin 12
An Zifen 15
Zhou Xiaozhou 13
Zhang Xi 1
Xu Yuying 1
Lu Yaping 2305509097@qq.com

4
Yu Liping yuliping@whu.edu.cn

15
1 https://ror.org/033vjfk17 grid.49470.3e 0000 0001 2331 6153 Center for Nurturing Care Research, Wuhan University School of Nursing, Wuhan University, No. 115 Donghu Road, Wuhan, Hubei province 430071 China
2 https://ror.org/04wjghj95 grid.412636.4 The First Affiliated Hospital of the China Medical University, No. 155 Nanjing Street, Heping district, Shenyang, Liaoning province China
3 https://ror.org/053v2gh09 grid.452708.c 0000 0004 1803 0208 Department of Clinical Nursing, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province China
4 https://ror.org/03ekhbz91 grid.412632.0 0000 0004 1758 2270 Renmin Hospital of Wuhan University, Hubei Zhang Road (formerly Ziyang Road) Wuchang District No. 99 Jiefang Road 238, Wuhan, Hubei province 430060 China
5 https://ror.org/01v5mqw79 grid.413247.7 0000 0004 1808 0969 Zhongnan Hospital of Wuhan University, No. 169, Donghu Road, Wuchang District, Wuhan, Hubei Province 430071 China
10 9 2024
10 9 2024
2024
24 11285 4 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.

Design

A retrospective study.

Method

The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.

Results

The CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.

Conclusions

CoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60–120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies.

Reporting method

This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.

Patient or public contribution

No patient or public contribution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-024-12863-w.

Keywords

Advanced lung cancer
Socioeconomic status
Prognosis
Machine learning
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

Lung cancer, a prevalent malignant tumor globally, poses a significant public health challenge due to its high incidence and mortality rates. According to the latest cancer data, approximately 2.2 million new cases of lung cancer were diagnosed in 2020 worldwide, and about 1.8 million deaths from lung cancer occurred, making it the leading cause of cancer-related death (accounting for 18% of all cancer deaths) [1]. In China, the lung cancer burden is particularly significant, with approximately one-third of all newly diagnosed patients with lung cancer worldwide, and a higher mortality rate compared to other countries [2]. Despite significant efforts made in early prevention, screening, and diagnosis, the majority of patients with lung cancer are still being detected at locally advanced stages [3]. The 5-year age-standardized net survival rate for patients with lung cancer in most countries remains at approximately 10–20% [4].

With the advancement of medical technology, the current focus in treating terminal illnesses has shifted from solely prolonging survival time to a patient-centered approach that aims to optimize quality of life (QOL), and this shift is also applicable to patients with lung cancer [5]. However, the extensive and complex treatment process, along with high medical expenses, imposes a significant economic burden on patients’ families and society. Notably, socioeconomic status (SES) often impacts cancer onset, progression, and outcomes [6, 7]. Low SES is associated with suboptimal cancer care and reduced survival rates, making it a significant determinant of disparities in health services for patients with cancer [8]. Even at the end of life, low SES often limits access to healthcare services for cancer patients. The findings by Lai et al. [9] highlight the adverse impact of low SES on access to end-of-life care services in Taiwan, China. Even in Canada, where end-of-life medical assistance is freely available and legal, patients with cancer with low SES received end-of-life medical assistance 39% less frequently compared to patients with high SES [10]. Furthermore, Sandström et al. [11] reported that patients with lung cancer had the lowest SES relative to patients with other types of cancer, including gastrointestinal tumors and prostate cancer. Two systematic reviews have also reported that low SES is associated with an increased risk of lung cancer incidence and mortality [7, 12]. Even among patients with advanced lung cancer, economic disadvantage has been associated with a 12% increased risk of death [13]. Therefore, it is crucial to prioritize optimizing hospice services for patients with advanced lung cancer with low SES to ensure they receive quality end-of-life care.

In this process, accurate prediction of disease survivorship in patients with advanced cancer with low SES is paramount. Prognostic information often drives patients and families to set realistic goals and make clinical decisions, thereby reducing the burden of unnecessary hospitalization and maintaining patients’ QOL at the end of life. Machine learning techniques, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) etc., with their ability to process vast biomedical and patient datasets flexibly and uncover complex relationships between variables, hold immense potential in this regard [14]. While machine learning has advanced the diagnosis, treatment, and prognosis of lung cancer [15], its application to predicting survival specifically in advanced cancer patients with low SES remains unexplored. Therefore, the primary aim of our retrospective analysis is to identify the most accurate survival prediction model among a traditional Cox proportional hazards (CoxPH) model and the four machine learning techniques (DT, RF, SVM, and XGBoost). By doing so, we aim to uncover the variables that contribute most significantly to accurate survival prediction, thereby facilitating the development of comprehensive, reproducible, and clinically applicable survival prediction models tailored to this vulnerable patient population.

Methods

Study design

This was a single-center retrospective study based on a cross-sectional design, in which medical records of patients registered and diagnosed with advanced lung cancer at a tertiary hospital in Wuhan, Hubei Province, China from January 2012 to December 2021 were selected.

Participant selection

Given the complexity of SES, there is no single optimal indicator that applies to all research objectives. Instead, SES is often influenced by multiple factors such as income, occupation, and education [16]. Among these factors, income is the best single indicator for assessing an individual’s material living standards and its direct impact on various material environments that shape health [17, 18]. Given the research scenario of this study was concerned with the survival response of patients with advanced lung cancer in fragile social settings, participants with low SES were defined as those belonging to low-income groups. The inclusion criteria for participants were as follows: (1) patients aged 18 or older; (2) histologically confirmed diagnosis of advanced lung cancer; (3) possession of a low-income certificate issued by the household registration authority (based on local poverty levels); and (4) complete follow-up data. The exclusion criteria were as follows: (1) primary tumor site involvement of two or more organs; (2) incomplete tumor information; (3) unknown sociodemographic characteristics, medical data, or disease-related symptoms that are essential for the analysis. This study was approved by the Ethics Committee of Wuhan University (approval number: 2021-YF-0061). All patients provided informed consent for the use of their personal information for subsequent scientific research when they registered for hospice care services. Ultimately, the medical records of 949 patients with advanced lung cancer with low SES were included in the analysis.

Definition and variables

The home care medical records were used to extract demographic characteristics, medical data, and disease-related symptoms of patients were extracted from the home care medical records. Demographic characteristics included age, sex, marital status, place of residence, education level, caregiver, and level of awareness about the disease. Medical data encompassed primary diagnosis, tumor metastasis, surgical history, chemotherapy history, radiotherapy history, hypertension, diabetes, phthisis history, chronic obstructive pulmonary disease (COPD), and cerebrovascular disease history. Disease-related symptoms included weight loss, insomnia, excessive sweating, edema, anorexia, nausea, constipation, cough, chest pain, headache, nutrition, positional, lymphadenopathy, pain source, and pain duration. The patient self-report scale information was obtained from the hospice care system, including the Numerical Rating Scale (NRS) scale, Karnofsky Performance Scale (KPS) scale, and QOL scale. On the first day of patient admission, doctors or nurses collected and evaluated all demographic characteristics, disease-related information, and related scale scores. Additionally, patient survival time, cause of death, survival status, and other data were obtained from the regular follow-up records of hospice care doctors and nurses. The primary endpoint was overall survival (OS), defined as the time interval from enrollment in-home care services until death or the last follow-up visit. The follow-up period ended on January 1st, 2022.

Numerical rating scale (NRS)

Pain response is a frequently reported symptom among patients with cancer. A recent systematic review and meta-analysis, which included data on the prevalence of pain in patients with cancer, demonstrated that the prevalence of pain among patients with advanced, metastatic, and terminal cancer was substantial, with 54.6% experiencing pain and 40.7% experiencing moderately severe pain [19]. The experience of pain and inadequate relief can have devastating consequences and may be associated with shorter survival in patients with cancer [20]. In this study, the NRS was used to assess the level of pain in patients with advanced lung cancer with low SES. The NRS is a commonly utilized tool for assessing pain levels, ranging from 0 (no pain) to 10 (worst possible pain), which provides a standardized means for patients to express their pain levels to healthcare professionals [21].

Karnofsky performance status (KPS)

The functional status of cancer patients is an important aspect of cancer care as it provides valuable insight into their overall health status, ability to engage in daily activities, and response to treatment. This study used the KPS to assess the functional status of patients with advanced lung cancer with low SES. The KPS scale, originally proposed by Karnofsky and Burchenal in 1948, has become a widely utilized tool for evaluating the functional status of patients with cancer [22]. It assesses the patient’s ability to engage in daily activities and their overall vitality on a scale ranging from 0 to 100. The higher the score, the better functional status and ability to independently engage in daily life activities.

Cancer pain and quality of life questionnaire for Chinese cancer patients (CPQLQ-CCP)

Assessing the QOL reported by patients with cancer is an essential aspect of cancer care, as it provides valuable insight into the patient’s overall well-being and response to treatment. In this study, the Cancer Pain and Quality of Life Questionnaire for Chinese Cancer Patients (CPQLQ-CCP) was used to evaluate the QOL of patients with advanced cancer with low SES. The CPQLQ-CCP was developed by Luo and Sun et al. and has been widely used in evaluating the needs of Chinese patients with cancer [23]. It covers three aspects of QOL related to patients with cancer, encompassing a total of 12 items that include physical, psychological, and social-interpersonal relationships. The CPQLQ-CCP has demonstrated good reliability and validity among Chinese patients with cancer, with a Cronbach’s α coefficient of 0.862 for the total scale [23].

Data preprocessing

Missing value handling

Since missing data may lead to the potential loss of valuable information and even cause instability in model implementation, we implemented a cautious strategy for handling missing values. Firstly, for variables with missing values exceeding 50%, the variable was removed from the dataset, as this helps to ensure that only variables with sufficient information were included in the analysis, preventing any potential bias that may arise from the interpolation of largely missing data. Secondly, a two-way confirmation process between patient electronic medical records and home care medical records was conducted for missing data. For example, if the disease stage of a patient was missing in the home care medical record, it could be confirmed based on the electronic medical record. This approach minimized the impact of missing values on the analysis while maintaining the integrity of the dataset.

Data outliers handling

To address potential outliers in continuous variables, we executed the following protocol: Initially, we constructed a box plot to visualize the discrete distribution of continuous variables, including the NRS score, KPS score, and QOL score, please see the Fig. 1. This graphical representation aided in the identification of any potential outliers or unusual data points. To prevent overfitting in the influencing factor analysis due to overly dispersed continuous data, we used X-tile 3.6.3 software (http://medicine.yale.edu) to determine the optimal cut-off points. X-tile software is an effective tool for outcome-based cut-point optimization and has been previously utilized in various studies, including survival analysis [24]. It takes each retrieved count range as a potential cut-off value and calculates its χ2 and P-value. Ultimately, the number with a maximum χ2 value and a minimum P-value is suggested as the optimal cut-off point. In accordance with the X-title software results, we identified the optimal cutoff values for the NRS scores as 4 and 5, resulting in three subgroups: score ≤ 4, score 5, and score > 5. For the KPS score, the optimal cutoff values were 40 and 50, and the subgroups were identified as score ≤ 40, score 50, and score > 50. For the QOL score, the optimal cutoff values were 30 and 36, and the subgroups were identified as score ≤ 30, score 30–36, and score > 36.

Fig. 1 Box plots of age, NRS, KPS, and QOL for patients in training and testing groups

Construction of prediction model of mortality risk for patients with advanced lung cancer with low SES

First, we used R software version 4.2.1 for statistical analysis and modeling, and randomly divided the data into a training group and testing group in a 3:1 ratio. The training group was used for statistical analysis, feature selection, and model training, while the testing group was used for model evaluation. Then to develop a prognostic prediction model for patients with advanced lung cancer with low SES, we used four machine learning algorithms including DT, RF, SVM, and XGBoost, alongside the traditional CoxPH regression model. The CoxPH regression model served as the baseline model, and a nomogram was constructed based on the risk factors influencing prognosis. The DT is an inductive learning algorithm that infers classification rules from unstructured and irregular data, presenting results in a tree structure. RF, an ensemble method based on DT, constructs an ensemble of decision trees in parallel to reduce the prediction error of a single decision tree. SVM is an efficient supervised machine learning model that can be used for both classification and regression tasks. Its core concept is to find the optimal hyperplane in a high-dimensional space to address binary classification problems while minimizing the number of misclassifications. XGBoost, a powerful gradient-boosting framework, utilizes weak classifiers to generate highly accurate predictions. In contrast to the RF algorithm, there is a strong interdependence between the base learners in XGBoost. Each base learner is created based on the previous base learners through a boosting algorithm, and all the weak learners are combined to form a strong learner.

Statistical analysis

Normally distributed metric data were expressed by mean ± standard deviation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline x \:$$\end{document}± s), and a t-test was used for intergroup comparisons. Non-normally distributed metric data were expressed by median (quartile) M (P25 ~ P75), and the Mann-Whitney U nonparametric test was used for intergroup comparison. Counting data were expressed as frequency and percentage (%), and the chi-square (χ2) test or Fisher’s exact probability test was used for intergroup comparison. Subsequently, the Kaplan-Meier method was utilized to generate a survival curve for both the training group and testing groups, and the Log-Rank test was applied to assess survival differences, univariate and multivariate CoxPH regression analyses were conducted on all variables in the training group to identify independent risk factors that impact the prognosis of these patients. A nomogram was further developed based on these risk factors to predict the prognosis of patients with advanced lung cancer with low SES at 60, 90, and 120 days. After that, the calibration curve and Area Under Curve (AUC) were utilized to evaluate the accuracy and discrimination of the prediction model in the training group and the testing group.

For the RF model, we utilized the randomForest and pROC package in R. Specifically, weemployed Bootstrap resampling to draw n training samples with replacement from the original dataset, using these samples to construct n trees (ntree). During tree generation, at each node, m variables (denoted as mtry) were randomly selected from the full feature set, and the variable with the highest discriminatory power was chosen for splitting the data. To assess variable importance, we utilized the varImpPlot package to rank and visualize the top 10 most influential variables.

Regarding the XGBoost model, we utilized the xgboost package in R, optimizing the model parameters via grid search and ten-fold cross-validation. The optimal settings included a maximum number of iterations (nrounds) set to 25, a maximum tree depth (max_depth) of 6, a random feature sampling ratio (colsample_bytree) maintained at the default value of 100%, a minimum child weight (min_child_weight) of 1, and a scale-positive weight (scale_pos_weight) set to 1, as appropriate for our dataset. These choices aimed to balance model complexity and predictive performance. For DT, we relied on the rpart package, while for SVM, we tuned the model to identify the optimal values for the cost parameter (C = 100) and the kernel parameter (γ = 0.01), which yielded the best predictive performance. By meticulously detailing these essential parameters, we have ensured the reproducibility of our machine learning models, facilitating future researchers in replicating and building upon our findings.

Finally, we evaluated and compared the performance of all five predictive models on the testing set, identifying the model with superior predictive performance at 60, 90, and 120 days, along with its 10 most important features. All statistical tests were two-tailed, and results were deemed statistically significant at P < 0.05.

Result

Patients and outcomes

A total of 949 patients with advanced lung cancer with low SES were finally included, with a median age of 63 years. Of these, 660 (69.5%) were male, 787 (82.9%) were married, 647 (68.2%) were well informed about their disease, 584 (61.5%) had pain lasting 6 to 12 months, 477 (50.3%) had bone pain, and 510 (53.7%) had received chemotherapy. The median survival time was 64 days, the median NRS score was 5, the median KPS score was 50, and the median QOL score was 35. The sociodemographic characteristics of the 949 participants are detailed in Table 1.

Table 1 Clinical characteristics of patients (N = 949)

Patient characteristic	Level	Total (n = 949)		Training group
(n = 712)	Testing group (n = 237)		
Case (%)	MST	P-value		Case (%)	Case (%)	P-value	
Median survival time(day)			64			123	113	0.41	
Sex (%)	Male	660 (69.5)	61	0.002*		495 (69.5)	165 (69.6)	0.15	
	Female	289 (30.5)	82			217 (30.5)	72 (30.4)		
Age Group (%)	≥ 60	343 (36.1)	78	0.09		452 (63.5)	154 (65.0)	0.74	
	< 60	606 (63.9)	64			260 (36.5)	83 (35.0)		
Marital (%)	Married	787 (82.9)	68	0.90		592 (83.1)	195 (82.3)	0.84	
	Single	162 (17.1)	66			120 (16.9)	42 (17.7)		
Area of residence (%)	Urban	684 (72.1)	70	0.30		518 (72.8)	166 (70.0)	0.47	
	Rural	265 (27.9)	64			194 (27.2)	71 (30.0)		
Education (%)	≤ 6years	94 (9.9)	61.5	0.80		72 (10.1)	22 (9.3)	0.21	
	6years-9years	161 (17.0)	73			114 (16.0)	47 (19.8)		
	9years-12years	361 (38.0)	69			264 (37.1)	97 (40.9)		
	12years-15years	245 (25.8)	61			189 (26.5)	56 (23.6)		
	>15years	88 (9.3)	77			73 (10.3)	15 (6.3)		
Caregiver (%)	Yes	782 (82.4)	69	0.10		588 (82.6)	194 (81.9)	0.87	
	No	167 (17.6)	65			124 (17.4)	43 (18.1)		
Awareness of the disease (%)	No Understanding at all	164 (17.3)	74	0.80		125 (17.6)	39 (16.5)	0.93	
	Partial understanding	138 (14.5)	61			103 (14.5)	35 (14.8)		
	Fully understand	647 (68.2)	65			484 (68.0)	163 (68.8)		
Metastasis (%)	Yes	791 (83.4)	64	0.07		590 (82.9)	201 (84.8)	0.55	
	No	158 (16.6)	80			122 (17.1)	36 (15.2)		
Operation (%)	Yes	188 (19.8)	84	0.08		140 (19.7)	48 (20.3)	0.92	
	No	761 (80.2)	64			572 (80.3)	189 (79.7)		
Chemotherapy (%)	Yes	510 (53.7)	76	0.08		382 (53.7)	128 (54.0)	0.98	
	No	439 (46.3)	62			330 (46.3)	109 (46.0)		
Radiotherapy (%)	Yes	422 (44.5)	74	0.10		321 (45.1)	101 (42.6)	0.56	
	No	527 (55.5)	64			391 (54.9)	136 (57.4)		
Duration of pain (%)	> 12month	113 (11.9)	62	0.006*		80 (11.2)	33 (13.9)	0.56	
	6month-12month	584 (61.5)	61			437 (61.4)	147 (62.0)		
	1month-6month	157 (16.5)	72			123 (17.3)	34 (14.3)		
	≤ 1month	95 (10.0)	129			72 (10.1)	23 (9.7)		
Diabetes (%)	Yes	68 (7.2)	79	0.50		47 (6.6)	21 (8.9)	0.31	
	No	881 (92.8)	65			665 (93.4)	216 (91.1)		
Hypertension (%)	Yes	180 (19.0)	65	0.60		126 (17.7)	54 (22.8)	0.10	
	No	769 (81.0)	69			586 (82.3)	183 (77.2)		
Phthisis (%)	Yes	35 (3.7)	76	0.80		24 (3.4)	11 (4.6)	0.48	
	No	914 (96.3)	67			688 (96.6)	226 (95.4)		
COPD (%)	Yes	39 (4.1)	70	0.30		31 (4.4)	8 (3.4)	0.64	
	No	910 (95.9)	68			681 (95.6)	229 (96.6)		
Cerebrovascular disease (%)	Yes	18 (1.9)	83	0.80		14 (2.0)	4 (1.7)	0.12	
	No	931 (98.1)	67			698 (98.0)	233 (98.3)		
Weight Loss (%)	Yes	782 (82.4)	63	< 0.001*		582 (81.7)	200 (84.4)	0.41	
	No	167 (17.6)	88			130 (18.3)	37 (15.6)		
Insomnia (%)	Yes	647 (68.2)	59	< 0.001*		492 (69.1)	155 (65.4)	0.33	
	No	302 (31.8)	87			220 (30.9)	82 (34.6)		
Excessive sweating (%)	Yes	101 (10.6)	52	0.70		59 (8.3)	16 (6.8)	0.54	
	No	848 (89.4)	70			653 (91.7)	221 (93.2)		
Edema (%)	Yes	773 (81.5)	41	< 0.001*		76 (10.7)	25 (10.5)	0.45	
	No	176 (18.5)	73			636 (89.3)	212 (89.5)		
Anorexia (%)	Yes	166 (17.5)	59	< 0.001*		580 (81.5)	193 (81.4)	0.32	
	No	783 (82.5)	132			132 (18.5)	44 (18.6)		
Nausea (%)	Yes	143 (15.1)	54	0.07		130 (18.3)	36 (15.2)	0.33	
	No	806 (84.9)	71			582 (81.7)	201 (84.8)		
Constipation (%)	Yes	441 (46.5)	60	0.01*		329 (46.2)	112 (47.3)	0.84	
	No	508 (53.5)	75			383 (53.8)	125 (52.7)		
Cough (%)	Yes	368 (38.8)	53	< 0.001*		283 (39.7)	85 (35.9)	0.32	
	No	581 (61.2)	80			429 (60.3)	152 (64.1)		
Chest pain (%)	Yes	395 (41.6)	71	0.20		301 (42.3)	94 (39.7)	0.53	
	No	554 (58.4)	63			411 (57.7)	143 (60.3)		
Headache (%)	Yes	86 (9.1)	51	0.50		71 (10.0)	15 (6.3)	0.12	
	No	863 (90.9)	70			641 (90.0)	222 (93.7)		
Nutrition (%)	Cachexia	21 (2.2)	165	< 0.001*		16 (2.2)	5 (2.1)	0.63	
	Malnutrition	424 (44.7)	80			321 (45.1)	103 (43.5)		
	Moderate malnutrition	478 (50.4)	59			353 (49.6)	125 (52.7)		
	Good nutrition	26 (2.7)	21			22 (3.1)	4 (1.7)		
Postural (%)	Forced position	68 (7.2)	49	< 0.001*		44 (6.2)	24 (10.1)	0.12	
	Involuntary position	223 (23.5)	46			170 (23.9)	53 (22.4)		
	Voluntary position	658 (69.3)	82			498 (69.9)	160 (67.5)		
Lymphadenectasis (%)	Yes	133 (14.0)	52	0.09		109 (15.3)	24 (10.1)	0.06	
	No	816 (86.0)	71			603 (84.7)	213 (89.9)		
Source of pain (%)	Somatic pain	173 (18.2)	74	0.30		123 (17.3)	50 (21.1)	0.51	
	Visceral pain	45 (4.7)	54			36 (5.1)	9 (3.8)		
	Neuralgia	254 (26.8)	69			190 (26.7)	64 (27.0)		
	Bone pain	477 (50.3)	63			363 (51.0)	114 (48.1)		
NRS Group (%)	<4	362 (38.1)	52	0.04*		279 (39.2)	83 (35.0)	0.11	
	4	272 (28.7)	73			210 (29.5)	62 (26.2)		
	>4	315 (33.2)	80			223 (31.3)	92 (38.8)		
KPS Group (%)	>50	380 (40.0)	118	< 0.001*		284 (39.9)	96 (40.5)	0.46	
	50	371 (39.1)	57			273 (38.3)	98 (41.4)		
	≤ 40	198 (20.9)	33			155 (21.8)	43 (18.1)		
QOL Group (%)	>36	204 (21.5)	35	< 0.001*		152 (21.3)	52 (21.9)	0.85	
	30–36	371 (39.1)	51			282 (39.6)	89 (37.6)		
	<30	374 (39.4)	96			278 (39.0)	96 (40.5)		
Note. Abbreviations: MST = median survival time COPD = chronic obstructive pulmonary disease; NRS = Numeric Rating Scale; KPS = Karnofsky Performance Scale; QOL = quality of life; *P < 0.05

Comparison of baseline characteristics of the patients in the training and testing groups

Patients were randomly assigned to the training group (n = 712) and testing group (n = 237) using a computerized random number generator with a random seed of 2022, resulting in a 3:1 ratio. The univariate data analysis revealed no statistically significant differences in the demographic characteristics, medical data, disease-related symptoms, and patient-reported outcomes between the two groups (P > 0.05), are shown in Table 1.

Comparison of survival analysis between training and testing groups

There was no significant difference in OS between the two groups (P = 0.35), as shown in Fig. 2.

Fig. 2 Analysis of the difference in survival between patients in the training and testing groups

Univariate and multivariate analysis for risk factors

Applying CoxPH regression for univariate and multivariate analysis on the data in the training group, we found that sex, edema, anorexia, cough, nutrition, KPS score, and QOL score significantly influenced the survival for patients with advanced lung cancer with low SES. The hazard ratios for the variables that were found to be statistically significant in both the univariate and multivariate analyses are shown in Table 2.

Table 2 Univariate and multivariate analysis of factors influencing survival in the training group of patients (N = 712)

Variables	Level	Univariate analysis	Multivariate analysis	
HR (95%CI)	P-value	HR (95%CI)	P-value	
Sex (vs. Male)	Female	0.81 (0.68–0.96)	0.015*	0.73 (0.62–0.87)	< 0.001*	
Age Group (vs. <60)	≥ 60	0.87 (0.74–1.02)	0.098			
Marital (vs. Single)	Married	1.04 (0.85–1.28)	0.678			
Area of residence (vs. Urban)	Rural	1.11 (0.93–1.32)	0.238			
Education (vs. >15years)	≤ 6years	1.06 (0.75–1.47)	0.497			
	6years-9years	1.10 (0.81–1.49)	0.834			
	9years- 12years	1.03 (0.78–1.35)	0.550			
	12years- 15years	1.10 (0.83–1.46)	0.754			
Caregiver (vs. Yes)	No	1.07 (0.87–1.31)	0.538			
Awareness of the disease (vs. Fully understand)	Partial understanding	1.02 (0.77–1.33)	0.914			
	No Understanding at all	1.05 (0.85–1.28)	0.655			
Metastasis (vs. Yes)	No	0.79 (0.64–0.97)	0.026*			
Operation (vs. Yes)	No	1.12 (0.92–1.36)	0.245			
Chemotherapy (vs. Yes)	No	1.08 (0.92–1.26)	0.334			
Radiotherapy (vs. Yes)	No	1.14 (0.98–1.33)	0.101			
Duration of pain (vs. >12 months)	6month- 12month	1.15 (0.90–1.49)	0.263			
	1month-6month	0.96 (0.71–1.29)	0.768			
	≤ 1month	0.79 (0.57–1.11)	0.181			
Diabetes (vs. Yes)	No	1.04 (0.85–1.27)	0.716			
Hypertension (vs. Yes)	No	1.14 (0.86–1.60)	0.303			
Phthisis (vs. Yes)	No	1.12 (0.72–1.75)	0.624			
COPD (vs. Yes)	No	1.06 (0.53–1.09)	0.135			
Cerebrovascular disease (vs. Yes)	No	1.01 (0.59–1.71)	0.983			
Weight Loss (vs. Yes)	No	0.68 (0.56–0.84)	< 0.001*			
Insomnia (vs. Yes)	No	0.74 (0.62–0.87)	< 0.001*			
Excessive sweating (vs. Yes)	No	0.86 (0.65–1.14)	0.313			
Edema (vs. Yes)	No	0.66 (0.51–0.84)	0.002*	0.72 (0.56–0.93)	0.012*	
Anorexia (vs. Yes)	No	0.53 (0.43–0.65)	< 0.001*	0.71 (0.56–0.89)	0.003*	
Nausea (vs. Yes)	No	0.84 (0.69–1.02)	0.082			
Constipation (vs. Yes)	No	0.84 (0.72–0.98)	0.032*			
Cough (vs. Yes)	No	0.80 (0.68–0.93)	0.005*	0.81 (0.69–0.95)	0.009*	
Chest pain (vs. Yes)	No	1.06 (0.91–1.24)	0.484			
Headache (vs. Yes)	No	0.92 (0.72–1.19)	0.530			
Nutrition (vs. Cachexia)	Malnutrition	0.53 (0.35–0.83)	0.005*	0.57 (0.36–0.88)	0.012*	
	Moderate malnutrition	0.37 (0.25–0.60)	< 0.001*	0.50 (0.32–0.79)	0.002*	
	Good nutrition	0.28 (0.14–0.54)	< 0.001*	0.48 (0.24–0.96)	0.040*	
Postural (vs. Forced position)	Involuntary position	1.18 (0.84–1.65)	0.342			
	Voluntary position	0.62 (0.45–0.85)	0.002*			
Lymphadenectasis (vs. Yes)	No	0.85 (0.69–1.05)	0.146			
Source of pain (vs. Somatic pain)	Visceral pain	1.56 (0.78–1.23)	0.067			
	Neuralgia	1.09 (0.86–1.38)	0.482			
	Bone pain	1.07 (0.87–1.33)	0.517			
NRS Group (vs. <4)	4	1.04 (0.85–1.27)	0.667			
	> 4	1.17 (0.97–1.41)	0.090			
KPS Group (vs. >50)	50	1.51 (1.26–1.80)	< 0.001*			
	≤ 40	2.96 (2.40–3.64)	< 0.001*	2.18 (0.56–0.93)	< 0.001*	
QOL Group (vs. >36)	30–36	1.75 (1.47–2.09)	< 0.001*	1.31 (0.56–0.93)	0.007*	
	<30	2.78 (2.25–3.43)	< 0.001*	1.72 (0.56–0.93)	< 0.001*	
Note. Abbreviations: COPD = chronic obstructive pulmonary disease; NRS = Numeric Rating Scale; KPS = Karnofsky Performance Scale; QOL = quality of life; HR = Hazard ratio; *P < 0.05. Table 2 presents the results of both univariate and multivariate analyses. All variables that were significant in the univariate analysis were included in the multivariate analysis. Only those variables that remained statistically significant in the multivariate analysis are specifically noted in the table for clarity

Evaluation of prediction nomogram

The nomogram integrated all 7 independent factors for OS in the training group, which predicts the 60-day, 90-day, and 120-day mortality Fig. 3. In the training group, the model-predicted AUC values at 60 days, 90 days and 120 days were 0.743 (95% CI: 0.706–0.779), 0.747 (95% CI: 0.708–0.781), and 0.751 (95% CI: 0.714–0.788), respectively; in the testing group, the model-predicted AUC values at 60, 90, and 120 days were 0.744 (95% CI: 0.681–0.808), 0.719 (95% CI: 0.649–0.788), 0.743 (95% CI: 0.673–0.812), respectively. The nomogram demonstrated that the CoxPH regression algorithm has significant discriminatory ability in both data sets (Fig. 4). Additionally, the calibration plots of nomograms for 60-, 90-, and 120-day survival in patients with advanced lung cancer with low SES in both data sets showed good agreement between the calibration curves of the training and testing groups (Fig. 5).

Fig. 3 Nomogram of 60, 90, 120 days probability of death

Fig. 4 Receiver operating characteristic (ROC) curve analysis for the nomogram model in the training group (a) and testing group (b)

Fig. 5 Calibration curves for predicting (a) 60-day, (b) 90-day, and (c) 120-day OS in the training group and testing group

Predicting prognosis using machine learning models (testing group)

We used the DT, RF, SVM, and XGBoost models to develop a prediction model. The performance evaluation is shown in Table 3, while Fig. 6 visualizes the ROC curves for each model at 60, 90, and 120 days. Notably, the XGBoost model exhibited superior performance with AUC values of 0.732 (95%CI: 0.668–0.796), 0.673 (95%CI: 0.598–0.747), and 0.694 (95%CI: 0.616–0.773) at 60, 90 and 120 days, respectively. Figure 7 highlight the most important features of the DT, RF, SVM, and XGBoost models that best predicted the prognosis of patients with advanced lung cancer with low SES at 60, 90, and 120 days.

Table 3 Performance of 60-day, 90-day, and 120-day survival prediction models in the testing group of patients (N = 237)

Model	Target class	Accuracy	Precision	Recall	F1-score	Sensitivity	Specificity	AUC (95% CI)	
CoxPH	60-day	-	-	-	-	-	-	0.744 (95%CI: 0.681–0.808)	
90-day	-	-	-	-	-	-	0.719 (95%CI: 0.649–0.788)	
120-day	-	-	-	-	-	-	0.743 (95%CI: 0.673–0.812)	
DT	60-day	0.567	0.744	0.277	0.407	0.277	0.893	0.692 (95%CI: 0.626–0.757)	
90-day	0.626	0.739	0.669	0.702	0.669	0.543	0.638 (95%CI: 0.566–0.710)	
120-day	0.723	0.746	0.930	0.828	0.930	0.194	0.641 (95%CI: 0.565–0.716)	
RF	60-day	0.639	0.689	0.579	0.629	0.579	0.705	0.681 (95%CI: 0.613–0.748)	
90-day	0.643	0.725	0.739	0.732	0.739	0.457	0.671 (95%CI: 0.599–0.742)	
120-day	0.744	0.786	0.883	0.832	0.883	0.388	0.685 (95%CI: 0.608–0.762)	
SVM	60-day	0.651	0.705	0.587	0.641	0.587	0.723	0.729 (95%CI: 0.665–0.794)	
90-day	0.626	0.730	0.688	0.708	0.688	0.506	0.664 (95%CI: 0.589–0.724)	
120-day	0.697	0.756	0.854	0.802	0.854	0.299	0.640 (95%CI: 0.559–0.721)	
XGBoost	60-day	0.672	0.740	0.587	0.655	0.587	0.768	0.732 (95%CI: 0.668–0.796)	
90-day	0.668	0.732	0.783	0.756	0.783	0.444	0.673 (95%CI: 0.598–0.747)	
120-day	0.773	0.780	0.953	0.858	0.953	0.313	0.694 (95%CI: 0.616–0.773)	
Note. CoxPH: Cox proportional hazards; DT: Decision Tree; RF: Random Forest, SVM: Support Vector Machine; XGBoost: Extreme Gradient Boosting

Fig. 6 ROC curves of the 60-day, 90-day, and 120-day survival prediction models for patients with advanced lung cancer with low SES

Fig. 7 Feature importance rank for DT, RF, SVM and XGBoost models

Discussion

To date, in order to improve the accuracy of survival time prediction for patients with advanced lung cancer, various methods have been employed to enhance the accuracy of survival time prediction for patients with advanced lung cancer, including logistic regression model, least absolute shrinkage and selection operator, CoxPH regression model, and machine learning models [25–28]. Among them, the CoxPH regression model has been one of the most widely utilized prognostic prediction models due to its simplicity and ease of parameter interpretation [29]. However, previous studies have suggested that machine learning methods, which are equipped with supervised functions, may be more suitable for predicting the survival of patients with lung cancer [26, 30]. This is because they can make meaningful predictions within a reasonable accuracy range, as supported by statistical results. Given this, we initially conducted CoxPH univariate and multivariate analysis on 712 patients with advanced lung cancer with low SES in the training group to identify independent predictors affecting their prognosis. The factors considered were sex, edema, anorexia, cough, nutrition, KPS score, and QOL score (P < 0.05, see Table 2 for details).

Utilizing the CoxPH regression analysis results, we developed five survival prediction models: nomogram, RF, DT, SVM, and XGBoost. These models were specifically tailored to assess the survival prospects of patients with advanced cancer and low SES at 60, 90, and 120 days. Our results indicate that all five models exhibited remarkable proficiency in predicting mortality risk among patients with advanced lung cancer with low SES (Table 3 and Fig. 6). Within the four machine learning models, the XGBoost model with boosting emerged as the most cost-effective, preceded by SVM, DT, and RF. This implies that in certain datasets, supervised learning models like the XGBoost and SVM models may outperform DT and RF—both of which are integrated tree-based approaches—owing to their enhanced capabilities in handling intricate data patterns, adaptable tuning, regularization techniques, and seamless integration with survival analysis. Furthermore, a notable congruency was observed among the four machine learning models (DT, RF, SVM, and XGBoost) regarding the top 10 influential features (Fig. 7). This consistency underscores their collective effectiveness in pinpointing factors that significantly impact the disease prognosis. This finding emphasizes the prowess of machine learning algorithms in survival analysis, particularly their adeptness in automatically deciphering nonlinear relationships and complex interaction effects within datasets without relying on predefined assumptions.

Interestingly, our analysis revealed that the nomogram surpassed all four machine-learning models in terms of predictive accuracy. This insight highlights the enduring relevance and superiority of the CoxPH regression analysis in specific predictive scenarios, despite the increasing complexity of machine learning methodologies. In alignment with this study, several researchers have observed that while machine learning methods excel at handling complex and nonlinear data, they do not consistently outperform traditional models. Lynch et al. [30] reported that in predicting the survival of patients with lung cancer using supervised machine learning classification techniques, the more advanced gradient-boosting machine model exhibited superior predictive performance. Notably, linear regression outperformed RF, DT, and SVM models in terms of predictive accuracy. Zhang et al. [31] examined the prognosis of patients with esophageal squamous cell carcinoma (ESCC) and found that the CoxPH regression model exhibited similar performance to Elastic Net and RF models in predicting the risk of death among patients with ESCC. Overall, these findings suggest that machine learning-based survival prediction methods for patients with advanced lung cancer with low SES are both feasible and effective. However, the classical CoxPH regression algorithm still possesses unique advantages and applications.

Incorporating the CoxPH regression analysis and four machine learning models, we identified KPS score, QOL score, and cough as significant predictors of 60-, 90-, and 120-day survival in patients with advanced lung cancer and low SES. KPS score, a widely recognized composite indicator of cancer patients’ functional capacity and self-care abilities, is a key factor in predicting survival in patients with cancer [32]. Previous studies have established the prognostic value of physical fitness status for patients with lung cancer across various treatment phases, with reduced physical fitness predicting poor outcomes for OS [28, 33–36]. Gupta et al. [37] reported that a 10-point improvement in physical functionality at three months among patients with stage IV non-small cell lung (NSCL) cancer lessened the risk of mortality by 8%. This evidence underscores the significance of physical fitness status in predicting patient survival, potentially due to its association with skeletal muscle composition and walking function [38]. In the present study, we further validate and expand upon these findings by demonstrating that a high KPS score is a significant predictor of longer survival in patients with advanced lung cancer with low SES. These results underscore the need for further exploration of interventions tailored to this population that aim to maintain patients’ functional status.

Patient-reported health-related QOL is a significant aspect of the overall well-being of patients with cancer. In this study, approximately 78.5% of the 949 patients with advanced lung cancer with low SES reported poor QOL, highlighting the need for attention. Numerous studies have shown that QOL serves as a valuable parameter for assessing the prognosis of patients with lung cancer, exhibiting strong predictive capabilities in prognostic model construction [28, 34, 36, 39–42]. Movsas et al. [40] reported that baseline QOL was the sole predictor of long-term survival in patients with NSCL cancer. Even after accounting for other well-known prognostic factors such as age, sex, disease stage, and physical status, QOL remains a significant, independent prognostic factor, providing valuable prognostic information for patients with lung cancer [28, 34, 36, 41]. Furthermore, the combination of QOL and comorbidity scores can yield more precise prognostic information. Trejo et al. [42] reported that chest pain and overall QOL had the strongest associations with survival. Specifically, for every 10% increase in chest pain, there was a 20% increase in the risk of death, while for every 10% improvement in overall quality of life, there was a 14% decrease in the risk of death. Similarly, Jacot et al. [39] found that combining comorbidity scores with QOL provided more precise prognostic information than traditional physical fitness assessments alone. Our findings also demonstrate that QOL exhibits robust predictive power in determining survival outcomes for patients with advanced lung cancer with low SES. Therefore, the integration of self-reported QOL into predictive models is essential for improving their survival outcomes, particularly for patients with lung cancer with low SES in the early stages of their disease. These models can enhance our ability to predict patient survival more accurately, ultimately leading to improved patient outcomes.

Cough is a common respiratory symptom in patients with lung cancer, particularly those with advanced disease. In this study, the prevalence of cough was 38.8% among patients with advanced lung cancer with low SES. This strong predictive performance for 30-, 60-, and 90-day survival is consistent with prior research by Wang et al. [27]. Furthermore, the combination of cough with fatigue and dyspnea has been shown to be associated with survival in patients with lung cancer. Cheville et al. [43] reported a stable prevalence rate of approximately 12.9–15.4% for this respiratory symptom cluster (cough, fatigue, and dyspnea) among lung cancer survivors 1–5 years after diagnosis. This cluster was associated with a higher risk of death 1 to 2 years after diagnosis [43]. Additionally, Cheville et al. [44] found that individual symptoms (cough, fatigue, and dyspnea) and symptom pairs were equally or more predictive of survival compared to clusters of symptoms, and the predictive value of symptom clusters for survival decreased over time. A qualitative study also indicated that cough plays a central role in the respiratory symptom cluster, and often affects how patients experience dyspnea and fatigue [45]. Wang et al. [27] reported that moderate-to-severe cough at baseline had the most significant independent predictive value for low survival in patients with advanced NSCL cancer. In conclusion, cough (both as a single symptom and as part of a symptom cluster) is a significant predictor of survival in patients with lung cancer, particularly those with advanced disease and low SES. Therefore, timely assessment and management of cough by healthcare professionals may improve patient outcomes.

Strengths and limitations

To the best of our knowledge, this study is the pioneering effort to compare the performance of various machine learning algorithms in developing and validating survival prediction models for patients with advanced lung cancer with low SES. This comparison aids in a more comprehensive understanding of the strengths and weaknesses of different algorithms for a given problem. Additionally, we paid meticulous attention to missing values and data outliers, enhancing the reliability and accuracy of the results. Nevertheless, several limitations should be acknowledged. Firstly, the single-center design limits the generalizability of the sample and findings. Although we have extensively tested the development of each of the five predictive models through internal validation, the generalizability of the results to other populations remains uncertain. The generalized predictive ability of these models for multicenter data should be further improved in future studies. Secondly, due to the retrospective nature of the study, our dataset of patients with advanced lung cancer with low SES from medical records was incomplete, lacking potentially pertinent information that could affect survival outcomes, including recent radiologic, imaging, and biochemical markers, as well as details on emerging treatments. This limits the robustness and generalizability of our findings. Finally, our model may only aid in identifying the short-term survival prognosis of patients with advanced lung cancer with low SES without providing further insights into potentially life-threatening pathophysiologic mechanisms. This limitation restricts the model’s practical utility and interpretive power.

Conclusion

In patients with advanced lung cancer with low SES, the CoxPH regression analysis, DT, RF, SVM, and XGBoost models were effective in predicting the risk of death at 60, 90, and 120 days. Among those four machine-learning models, the XGBoost model demonstrated the most reliable performance in predicting survival events and guiding clinical decision-making for this vulnerable population. Notably, the nomogram based on the CoxPH regression superior performance in this specific dataset offers a valuable tool for interpretive studies. Based on our findings, we recommend that clinicians and nurses prioritize monitoring KPS score, QOL score, and cough symptoms. These indicators can assist in targeting individuals at higher risk for intensified care, thereby promoting personalized medicine and enhancing the prognosis of patients with advanced lung cancer of low SES.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The authors would like to thank the clinical team at the hospice ward of Zhongnan Hospital of Wuhan University for their help in collecting the original data.

Author contributions

Jiaxin Cui: Conceptualization, Methodology, Software, Formal analysis, Data curation, Investigation; Zifen An: Conceptualization, Methodology, Investigation, Data curation, Writing-Original draft; Xiaozhou Zhou: Conceptualization, Methodology, Data curation, Writing–Reviewing and Editing; Xi Zhang and Yuying Xu: Validation, Writing–Reviewing and Editing; Yaping Lu and Liping Yu: Writing–Reviewing and Editing, Supervision.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Data availability

The data related to the findings of this study are available from the first author (Jiaxin Cui) upon reasonable request.

Declarations

Ethical approval and consent to participate

This study was approved by the Ethics Committee of Wuhan University (approval number: 2021-YF-0061). All patients filled out a written informed consent for the use of their personal information for subsequent scientific research when they registered for hospice care services.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

SES Socioeconomic Status

QOL Quality of Life

COPD Chronic Obstructive Pulmonary Disease

OS Overall Survival

NRS Numerical Rating Scale

DT Decision Tree

RF Random Forest

SVM Support Vector Machine

XGBoost Extreme Gradient Boosting

CoxPH Cox Proportional Hazards

AUC Area Under Curve

ROC Receiver Operating Characteristic

NSCL Non-small Cell Lung

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jiaxin Cui, Zifen An, and Xiaozhou Zhou are Co-first authors and contributed equally to this work.
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References

1. Sung H Ferlay J Siegel RL Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 2021 71 209 49 10.3322/caac.21660 33538338
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. 10.3322/caac.21660.33538338 10.3322/caac.21660
2. Zhou M Wang H Zeng X Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the global burden of Disease Study 2017 Lancet 2019 394 1145 58 10.1016/S0140-6736(19)30427-1 31248666
Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2019;394:1145–58. 10.1016/S0140-6736(19)30427-1.31248666 10.1016/S0140-6736(19)30427-1
3. Visconti R Morra F Guggino G The between now and then of Lung Cancer Chemotherapy and Immunotherapy Int J Mol Sci 2017 18 1374 10.3390/ijms18071374 28653990
Visconti R, Morra F, Guggino G, Celetti A. The between now and then of Lung Cancer Chemotherapy and Immunotherapy. Int J Mol Sci. 2017;18:1374. 10.3390/ijms18071374.28653990 10.3390/ijms18071374
4. Allemani C Matsuda T Carlo VD Global surveillance of trends in cancer survival: analysis of individual records for 37,513,025 patients diagnosed with one of 18 cancers during 2000–2014 from 322 population-based registries in 71 countries (CONCORD-3) Lancet (London England) 2018 391 1023 10.1016/S0140-6736(17)33326-3 29395269
Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, Bonaventure A, et al. Global surveillance of trends in cancer survival: analysis of individual records for 37,513,025 patients diagnosed with one of 18 cancers during 2000–2014 from 322 population-based registries in 71 countries (CONCORD-3). Lancet (London England). 2018;391:1023. 10.1016/S0140-6736(17)33326-3.29395269 10.1016/S0140-6736(17)33326-3
5. Aragon KN Palliative care in lung cancer Clin Chest Med 2020 41 281 93 10.1016/j.ccm.2020.02.005 32402363
Aragon KN. Palliative care in lung cancer. Clin Chest Med. 2020;41:281–93. 10.1016/j.ccm.2020.02.005.32402363 10.1016/j.ccm.2020.02.005
6. Williams J, Allen L, Wickramasinghe K, Mikkelsen B, Roberts N, Townsend N. A systematic review of associations between non-communicable diseases and socioeconomic status within low- and lower-middle-income countries. J Global Health; 2018;8:020409. 10.7189/jogh.08.020409.
7. Sommer I, Griebler U, Mahlknecht P, Thaler K, Bouskill K, Gartlehner G, et al. Socioeconomic inequalities in non-communicable diseases and their risk factors: an overview of systematic reviews. BMC Public Health. 2015;15:914. 10.1186/s12889-015-2227-y.
8. Guadamuz JS, Wang X, Ryals CA, Miksad RA, Snider J, Walters J, et al. Socioeconomic status and inequities in treatment initiation and survival among patients with cancer, 2011–2022. JNCI Cancer Spectrum. 2023;7:pkad058. 10.1093/jncics/pkad058.
9. Lai Y-J Chen Y-Y Ko M-C Low socioeconomic status associated with lower utilization of hospice care services during end-of-life treatment in patients with cancer: a population-based cohort study J Pain Symptom Manag 2020 60 309 e3151 10.1016/j.jpainsymman.2020.03.015
Lai YJ, Chen YY, Ko MC, Chou YS, Huang LY, Chen YT, et al. Low socioeconomic status associated with lower utilization of hospice care services during end-of-life treatment in patients with cancer: a population-based cohort study. J Pain Symptom Manag. 2020;60:309–e3151. 10.1016/j.jpainsymman.2020.03.015.10.1016/j.jpainsymman.2020.03.015
10. Redelmeier DA, Ng K, Thiruchelvam D, Shafir E. Association of socioeconomic status with medical assistance in dying: a case–control analysis. BMJ Open. 2021;11:e043547. 10.1136/bmjopen-2020-043547.
11. Sandström N, Johansson M, Jekunen A, Andersén H. Socioeconomic status and lifestyle patterns in the most common cancer types-community-based research. BMC Public Health. 2023;23:1722. 10.1186/s12889-023-16677-6.
12. Sidorchuk A Agardh EE Aremu O Socioeconomic differences in lung cancer incidence: a systematic review and meta-analysis Cancer Causes Control 2009 20 459 71 10.1007/s10552-009-9300-8 19184626
Sidorchuk A, Agardh EE, Aremu O, Hallqvist J, Allebeck P, Moradi T. Socioeconomic differences in lung cancer incidence: a systematic review and meta-analysis. Cancer Causes Control. 2009;20:459–71. 10.1007/s10552-009-9300-8.19184626 10.1007/s10552-009-9300-8
13. Dalton SO Steding-Jessen M Jakobsen E Socioeconomic position and survival after lung cancer: influence of stage, treatment and comorbidity among Danish patients with lung cancer diagnosed in 2004–2010 Acta Oncol 2015 54 797 804 10.3109/0284186X.2014.1001037 25761702
Dalton SO, Steding-Jessen M, Jakobsen E, Mellemgaard A, Østerlind K, Schüz J, et al. Socioeconomic position and survival after lung cancer: influence of stage, treatment and comorbidity among Danish patients with lung cancer diagnosed in 2004–2010. Acta Oncol. 2015;54:797–804. 10.3109/0284186X.2014.1001037.25761702 10.3109/0284186X.2014.1001037
14. Goecks J Jalili V Heiser LM How machine learning will transform biomedicine Cell 2020 181 92 10.1016/j.cell.2020.03.022 32243801
Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181:92-101. 10.1016/j.cell.2020.03.022.32243801 10.1016/j.cell.2020.03.022
15. Li Y Wu X Yang P Machine learning for lung cancer diagnosis, treatment, and prognosis Genom Proteom Bioinform 2022 20 850 10.1016/j.gpb.2022.11.003
Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genom Proteom Bioinform. 2022;20:850-66. 10.1016/j.gpb.2022.11.003.10.1016/j.gpb.2022.11.003
16. Boles DB Socioeconomic status, a forgotten variable in lateralization development Brain Cogn 2011 76 52 7 10.1016/j.bandc.2011.03.002 21458903
Boles DB. Socioeconomic status, a forgotten variable in lateralization development. Brain Cogn. 2011;76:52–7. 10.1016/j.bandc.2011.03.002.21458903 10.1016/j.bandc.2011.03.002
17. Galobardes B Shaw M Lawlor DA Indicators of socioeconomic position (part 1) J Epidemiol Community Health 2006 60 7 12 10.1136/jech.2004.023531 16361448
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006;60:7–12. 10.1136/jech.2004.023531.16361448 10.1136/jech.2004.023531
18. Galobardes B Shaw M Lawlor DA Indicators of socioeconomic position (part 2) J Epidemiol Community Health 2006 60 95 101 10.1136/jech.2004.028092 16415256
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD. Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 2006;60:95–101. 10.1136/jech.2004.028092.16415256 10.1136/jech.2004.028092
19. Snijders RAH, Brom L, Theunissen M, van den Beuken-van Everdingen MHJ. Update on prevalence of pain in patients with cancer 2022: a systematic literature review and meta-analysis. Cancers (Basel). 2023;15:591. 10.3390/cancers15030591.
20. Zylla D Steele G Gupta P A systematic review of the impact of pain on overall survival in patients with cancer Support Care Cancer 2017 25 1687 98 10.1007/s00520-017-3614-y 28190159
Zylla D, Steele G, Gupta P. A systematic review of the impact of pain on overall survival in patients with cancer. Support Care Cancer. 2017;25:1687–98. 10.1007/s00520-017-3614-y.28190159 10.1007/s00520-017-3614-y
21. Karcioglu O Topacoglu H Dikme O A systematic review of the pain scales in adults: which to use? Am J Emerg Med 2018 36 707 14 10.1016/j.ajem.2018.01.008 29321111
Karcioglu O, Topacoglu H, Dikme O, Dikme O. A systematic review of the pain scales in adults: which to use? Am J Emerg Med. 2018;36:707–14. 10.1016/j.ajem.2018.01.008.29321111 10.1016/j.ajem.2018.01.008
22. Mor V, Laliberte L, Morris JN, Wiemann M. The Karnofsky Performance Status Scale: an examination of its reliability and validity in a research setting. Cancer. 1984;53:2002–7. 10.1002/1097-0142(19840501)53:93.0.co;2-w.
23. Luo J, Sun Y, Wu G et al. Development and test of quality of life questionnaire for cancer patients. J Practical Oncol 1996; 252–5.
24. Camp RL Dolled-Filhart M Rimm DL X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization Clin Cancer Res 2004 10 7252 9 10.1158/1078-0432.CCR-04-0713 15534099
Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10:7252–9. 10.1158/1078-0432.CCR-04-0713.15534099 10.1158/1078-0432.CCR-04-0713
25. Cui J, Tan L, Fang P, An Z, Du J, Yu L. Prediction of survival time in advanced lung cancer: a retrospective study in home-based palliative care unit. Am J Hosp Palliat Care. 2023;40:271–9. 10.1177/10499091221100501.
26. Vesteghem C Szejniuk WM Brøndum RF Dynamic risk prediction of 30-day mortality in patients with advanced lung cancer: comparing five machine learning approaches JCO Clin Cancer Inf 2022 6 e2200054 10.1200/CCI.22.00054
Vesteghem C, Szejniuk WM, Brøndum RF, Falkmer UG, Azencott CA, Bøgsted M. Dynamic risk prediction of 30-day mortality in patients with advanced lung cancer: comparing five machine learning approaches. JCO Clin Cancer Inf. 2022;6:e2200054. 10.1200/CCI.22.00054.10.1200/CCI.22.00054
27. Wang XS Shi Q Lu C Prognostic value of symptom burden for overall survival in patients receiving chemotherapy for advanced non-small cell lung cancer Cancer 2010 116 137 45 10.1002/cncr.24703 19852033
Wang XS, Shi Q, Lu C, Basch EM, Johnson VE, Mendoza TR, et al. Prognostic value of symptom burden for overall survival in patients receiving chemotherapy for advanced non-small cell lung cancer. Cancer. 2010;116:137–45. 10.1002/cncr.24703.19852033 10.1002/cncr.24703
28. Zeng Y, Cao W, Wu C, Wang M, Xie Y, Chen W, et al. Survival prediction in home hospice care patients with lung cancer based on LASSO algorithm. Cancer Control. 2022;29:1–10. 10.1177/10732748221124519.
29. Randall RL Cable MG Nominal nomograms and marginal margins: what is the law of the line? Lancet Oncol 2016 17 554 6 10.1016/S1470-2045(16)00072-3 27301026
Randall RL, Cable MG. Nominal nomograms and marginal margins: what is the law of the line? Lancet Oncol. 2016;17:554–6. 10.1016/S1470-2045(16)00072-3.27301026 10.1016/S1470-2045(16)00072-3
30. Lynch CM, Abdollahi B, Fuqua JD, de Carlo AR, Bartholomai JA, Balgemann RN, et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inf. 2017;108:1–8. 10.1016/j.ijmedinf.2017.09.013.
31. Zhang K Ye B Wu L Machine learning–based prediction of survival prognosis in esophageal squamous cell carcinoma Sci Rep 2023 13 13532 10.1038/s41598-023-40780-8 37598277
Zhang K, Ye B, Wu L, Ni s, Li y, Wang Q, et al. Machine learning–based prediction of survival prognosis in esophageal squamous cell carcinoma. Sci Rep. 2023;13:13532. 10.1038/s41598-023-40780-8.37598277 10.1038/s41598-023-40780-8
32. Fairchild A Debenham B Danielson B Comparative multidisciplinary prediction of survival in patients with advanced cancer Support Care Cancer 2014 22 611 7 10.1007/s00520-013-2013-2 24136159
Fairchild A, Debenham B, Danielson B, Huang F, Ghosh S. Comparative multidisciplinary prediction of survival in patients with advanced cancer. Support Care Cancer. 2014;22:611–7. 10.1007/s00520-013-2013-2.24136159 10.1007/s00520-013-2013-2
33. Bowden JCS Williams LJ Simms A Prediction of 90&nbsp;day and overall survival after chemoradiotherapy for lung cancer: role of performance status and body composition Clin Oncol 2017 29 576 84 10.1016/j.clon.2017.06.005
Bowden JCS, Williams LJ, Simms A, Price A, Campbell S, Fallon MT, et al. Prediction of 90 day and overall survival after chemoradiotherapy for lung cancer: role of performance status and body composition. Clin Oncol. 2017;29:576–84. 10.1016/j.clon.2017.06.005.10.1016/j.clon.2017.06.005
34. Braun DP, Gupta D, Staren ED. Quality of life assessment as a predictor of survival in non-small cell lung cancer. BMC Cancer. 2011;11:353. 10.1186/1471-2407-11-353.
35. Sanders KJ, Hendriks LE, Troost EG, Bootsma GP, Houben RM, Schols AM, et al. Early weight loss during chemoradiotherapy has a detrimental impact on outcome in NSCLC. J Thorac Oncol. 2016;11:873–9. 10.1016/j.jtho.2016.02.013.
36. Sloan JA, Zhao X, Novotny PJ, Wampfler J, Garces Y, Clark MM, et al. Relationship between deficits in overall quality of life and non–small-cell lung cancer survival. J Clin Oncol. 2012;30:1498–504. 10.1200/JCO.2010.33.4631.
37. Gupta D, Braun Dp, Staren Ed. Association between changes in quality of life scores and survival in non-small cell lung cancer patients. EUR J Cancer Care 2012;21:614–622. 10.1111/j.1365-2354.2012.01332.x.
38. Dolan RD Daly LE Simmons CP The relationship between ECOG-PS, mGPS, BMI/WL grade and body composition and physical function in patients with advanced cancer Cancers (Basel) 2020 12 1187 10.3390/cancers12051187 32397102
Dolan RD, Daly LE, Simmons CP, Ryan AM, Sim WM, Fallon M, et al. The relationship between ECOG-PS, mGPS, BMI/WL grade and body composition and physical function in patients with advanced cancer. Cancers (Basel). 2020;12:1187. 10.3390/cancers12051187.32397102 10.3390/cancers12051187
39. Jacot W Colinet B Bertrand D Quality of life and comorbidity score as prognostic determinants in non-small-cell lung cancer patients Ann Oncol 2008 19 1458 64 10.1093/annonc/mdn064 18356134
Jacot W, Colinet B, Bertrand D, Lacombe S, Bozonnat MC, Daurès JP, et al. Quality of life and comorbidity score as prognostic determinants in non-small-cell lung cancer patients. Ann Oncol. 2008;19:1458–64. 10.1093/annonc/mdn064.18356134 10.1093/annonc/mdn064
40. Movsas B Moughan J Sarna L Quality of life supersedes the classic prognosticators for long-term survival in locally advanced non–small-cell lung cancer: an analysis of RTOG 9801 J Clin Oncol 2009 27 5816 22 10.1200/JCO.2009.23.7420 19858383
Movsas B, Moughan J, Sarna L, Langer C, Werner-Wasik M, Nicolaou N, et al. Quality of life supersedes the classic prognosticators for long-term survival in locally advanced non–small-cell lung cancer: an analysis of RTOG 9801. J Clin Oncol. 2009;27:5816–22. 10.1200/JCO.2009.23.7420.19858383 10.1200/JCO.2009.23.7420
41. Qi Y Schild SE Mandrekar SJ Pretreatment quality of life is an independent prognostic factor for overall survival in patients with advanced stage non-small cell lung cancer J Thorac Oncol 2009 4 1075 82 10.1097/JTO.0b013e3181ae27f5 19546817
Qi Y, Schild SE, Mandrekar SJ, Tan AD, Krook JE, Rowland KM, et al. Pretreatment quality of life is an independent prognostic factor for overall survival in patients with advanced stage non-small cell lung cancer. J Thorac Oncol. 2009;4:1075–82. 10.1097/JTO.0b013e3181ae27f5.19546817 10.1097/JTO.0b013e3181ae27f5
42. Trejo MJ Bell ML Dhillon HM Baseline quality of life is associated with survival among people with advanced lung cancer J Psychosoc Oncol 2020 38 635 41 10.1080/07347332.2020.1765065 32410506
Trejo MJ, Bell ML, Dhillon HM, Vardy JL. Baseline quality of life is associated with survival among people with advanced lung cancer. J Psychosoc Oncol. 2020;38:635–41. 10.1080/07347332.2020.1765065.32410506 10.1080/07347332.2020.1765065
43. Cheville AL Novotny PJ Sloan JA Fatigue, dyspnea, and cough comprise a persistent symptom cluster up to five years after diagnosis with lung cancer J Pain Symptom Manage 2011 42 202 12 10.1016/j.jpainsymman.2010.10.257 21398090
Cheville AL, Novotny PJ, Sloan JA, Basford JR, Wampfler JA, Garces YI, et al. Fatigue, dyspnea, and cough comprise a persistent symptom cluster up to five years after diagnosis with lung cancer. J Pain Symptom Manage. 2011;42:202–12. 10.1016/j.jpainsymman.2010.10.257.21398090 10.1016/j.jpainsymman.2010.10.257
44. Cheville AL Novotny PJ Sloan JA The value of a symptom cluster of fatigue, dyspnea, and cough in predicting clinical outcomes in lung cancer survivors J Pain Symptom Manage 2011 42 213 21 10.1016/j.jpainsymman.2010.11.005 21398089
Cheville AL, Novotny PJ, Sloan JA, Basford JR, Wampfler JA, Garces YI, et al. The value of a symptom cluster of fatigue, dyspnea, and cough in predicting clinical outcomes in lung cancer survivors. J Pain Symptom Manage. 2011;42:213–21. 10.1016/j.jpainsymman.2010.11.005.21398089 10.1016/j.jpainsymman.2010.11.005
45. Molassiotis A Lowe M Blackhall F A qualitative exploration of a respiratory distress symptom cluster in lung cancer: Cough, breathlessness and fatigue Lung Cancer 2011 71 94 102 10.1016/j.lungcan.2010.04.002 20439127
Molassiotis A, Lowe M, Blackhall F, Lorigan P. A qualitative exploration of a respiratory distress symptom cluster in lung cancer: Cough, breathlessness and fatigue. Lung Cancer. 2011;71:94–102. 10.1016/j.lungcan.2010.04.002.20439127 10.1016/j.lungcan.2010.04.002
