
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-10771
00066
10.1097/MD.0000000000039294
3
5700
Research Article
Observational Study
Early death incidence and prediction in stage IV large cell neuroendocrine carcinoma of the lung
Xing Hongquan MM hongquanxing163@163.com
a
Wu Cong MM congwu002@163.com
b
Zhang Dongdong MM zhangxinyi80@163.com
a
https://orcid.org/0000-0002-4708-3435
Zhang Xinyi PhD a*
a Department of Respiratory Diseases, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
b Department of Pathology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
* Correspondence: Xinyi Zhang, Department of Respiratory Diseases, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China (e-mail: zhangxinyi80@163.com).
13 9 2024
13 9 2024
103 37 e3929430 11 2023
11 4 2024
23 7 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Nearly half of lung large cell neuroendocrine carcinoma (LCNEC) patients are diagnosed at an advanced stage and face a high early death risk. Our objective was to develop models for assessing early death risk in stage IV LCNEC patients. We used surveillance, epidemiology, and end results (SEER) databases to gather data on patients with stage IV LCNEC to construct models and conduct internal validation. Additionally, we collected a dataset from the Second Affiliated Hospital of Nanchang University for external validation. We used the Pearson correlation coefficient and variance inflation factor to identify collinearity among variables. Logistic regression analysis and least absolute shrinkage and selection operator analysis were employed to identify important independent prognostic factors. Prediction nomograms and network-based probability calculators were developed. The accuracy of the nomograms was evaluated using receiver operating characteristic curves. The goodness of fit of the nomograms was evaluated using the Hosmer–Lemeshow test and calibration curves. The clinical value of the models was assessed through decision curve analysis. We enrolled 816 patients from the surveillance, epidemiology, and end results database and randomly assigned them to a training group and a validation group at a 7:3 ratio. In the training group, we identified 9 factors closely associated with early death and included them in the prediction nomograms. The overall early death model achieved an area under the curve of 0.850 for the training group and 0.780 for the validation group. Regarding the cancer-specific early death model, the area under the curve was 0.853 for the training group and 0.769 for the validation group. The calibration curve and Hosmer–Lemeshow test both demonstrated a high level of consistency for the constructed nomograms. Additionally, decision curve analysis further confirmed the substantial clinical utility of the nomograms. We developed a reliable nomogram to predict the early mortality risk in stage IV LCNEC patients that can be a helpful tool for health care professionals to identify high-risk patients and create personalized treatment plans.

early death
LCNEC
nomogram
SEER
stage IV
Domestic Cooperation Project of the Science and Technology Department of Jiangxi Province20212BDH81021 Xinyi ZhangOPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Lung large cell neuroendocrine carcinoma (LCNEC), a highly aggressive neuroendocrine tumor, represents approximately 3% of all lung cancers.[1] A recent report showed that the incidence of LCNEC is rising year by year, rising from 0.16 per 100,000 individuals in 2000 to 0.41 per 100,000 individuals in 2015.[2] LCNEC is closely associated with smoking and primarily occurs in elderly patients, presenting with nonspecific clinical manifestations; unfortunately, nearly half of patients are diagnosed only when their disease has already reached an advanced stage, which poses considerable challenges for treatment and prognosis.[3,4]

Currently, the advancement of targeted therapy and immunotherapy has revolutionized the treatment approach for patients with non-small cell lung cancer (NSCLC).[5] However, the rarity of LCNEC presents a challenge when attempting to conduct randomized controlled trials (RCTs) and has resulted in limited progress in treating this cancer.[1] Chemotherapy remains a crucial treatment method for advanced LCNEC, but there are disparities in opinion regarding the most effective chemotherapy regimen.[6] Currently, there is a leaning toward using chemotherapy regimens akin to those employed for small cell lung cancer (SCLC), albeit with mixed results. While some studies have found that the efficacy of the etoposide plus cisplatin regimen is comparable to that observed in small cell lung cancer, other studies suggest that it may not be as effective.[7,8] Derks et al proposed a classification system for LCNEC, distinguishing it into 2 subgroups, SCLC-like and NSCLC-like, and suggested that tailoring chemotherapy based on pathological characteristics can lead to improved treatment outcomes.[9] However, more prospective clinical trials are needed to validate this finding. Given the heterogeneity of tumors and varied treatment approaches, survival time can differ substantially per patient. As LCNEC often demonstrates an aggressive progression of invasive disease, patients diagnosed in advanced stages face a high risk of early mortality.[4] Therefore, it is practical and necessary to identify risk factors for early death in LCNEC. Unfortunately, previous studies on stage IV LCNEC have tended to focus on the long-term survival of patients or risk factors associated with brain metastasis,[10–12] while studies investigating risk factors specifically associated with early death in stage IV LCNEC are currently limited.

Studies that focus on early death have been conducted in a wide range of cancer types,[13–15] demonstrating considerable clinical relevance. Nomograms are commonly used prognostic tools that play a crucial role in identifying risk factors and personalizing treatment.[16] The objective of this study was to create a predictive model for early mortality in stage IV LCNEC patients.

2. Methods

2.1. Patients

The surveillance, epidemiology, and end results (SEER) database covers data pertaining to approximately 28% of cancer patients in the United States.[17] Once approved, we obtained data on patients with stage IV LCNEC by downloading the SEER database version 8.4.2. For this study, we included LCNEC patients diagnosed from 2010 to 2019, as the SEER database began collecting information on organ metastases starting in 2010. We applied the following criteria in our study: an International Classification of Diseases for Oncology, Third Edition code of 8013/3 and a site code of C34, along with histological confirmation of the primary tumor as LCNEC. Moreover, we implemented the following exclusion criteria in our study: (1) patients with stage Tis, T0, Tx, or NX; (2) unclear metastatic information; (3) unclear race; (4) unclear marital status; (5) unclear primary site and laterality; and (6) incomplete radiotherapy data. Figure 1 presents the inclusion and exclusion criteria.

Figure 1. Flow chart of patient enrollment. ICD-O-3 = International Classification of Diseases for Oncology, Third Edition; LCNEC = lung large cell neuroendocrine carcinoma.

The variables extracted from the SEER database encompassed the following factors: age (<65 years, 65–74 years, >74 years), sex, race (white, nonwhite), marital status, average household income, laterality, primary site, AJCC grade 8, stage T, stage N, presence of bone, brain, liver, and lung metastasis at initial diagnosis, radiation therapy, chemotherapy, surgery, survival duration (in months), vital status recode, cause of death, and SEER cause-specific death classification. Drawing from prior knowledge, we classified patients who passed away within 3 months after the initial diagnosis as experiencing early death.[14,18] The primary outcome measures encompassed early death from any cause and early death specifically attributed to cancer.

We enrolled 25 patients diagnosed with LCNEC from The Second Affiliated Hospital of Nanchang University for this study. The retrospective study conducted on the hospital cohort received approval from the Ethics Committee of The Second Affiliated Hospital of Nanchang University, adhering to the ethical standards set forth by the Helsinki Declaration.

2.2. Statistical analysis

The total cohort was divided randomly at a 7:3 ratio into 2 separate cohorts: the training cohort consisting of 571 patients and the internal validation cohort comprising 245 patients. The demographic and clinical factors were presented using a numerical format, accompanied by corresponding percentages. Furthermore, a pie chart was utilized to visually represent the comprehensive distribution of data. The clinical and pathological variables were evaluated between the training group and internal validation group using Pearson Chi-square test. In the training cohort, univariate logistic regression was employed to assess factors associated with early mortality, followed by the utilization of the least absolute shrinkage and selection operator method to select relevant predictive features. Subsequently, multivariate logistic regression analysis was conducted to further evaluate the chosen features, with the results presented in forest plots. Based on the identified risk factors, practical nomograms were developed to predict early mortality in LCNEC patients.

We assessed the performance of the nomogram by calculating its area under the receiver operating characteristic curve (AUC),[19] while using the Hosmer–Lemeshow test and a calibration curve generated from 1000 repeated samples to examine consistency between the model and real-world situations. Finally, we evaluated the clinical usefulness of the model through decision curve analysis, which assesses the net benefit of intervention measures based on the models.[20] We evaluated the potential for multicollinearity among the variables within our study by calculating tolerance and variance inflation factor (VIF) values. To determine this, we deemed tolerance values < 0.1 and VIF values > 10 as indicative of multicollinearity.[21] To further investigate potential collinearity, we also calculated Pearson correlation coefficients. A coefficient of <0.7 between 2 independent variables was considered indicative of no multicollinearity.[22] To provide a practical application of these findings for clinical practice, we developed an interactive web-based dynamic nomogram. Its incorporation can greatly facilitate the prediction of outcomes for patients. All analyses were conducted using Empower Stats (www.empowerstats.com) and R (http://www.R-project.org). Statistical significance was defined as P < .05.

3. Results

3.1. Patient characteristics and incidence of early death

After applying the inclusion criteria, our study included a total of 816 patients from the SEER database, as indicated in Table 1. The mean age of stage IV LCNEC patients was 66 ± 11 years. Among the patients, a majority were male (n = 485, 59.44%), white (n = 688, 84.31%), and married (n = 438, 53.68%). The most prevalent site of metastasis was revealed to be brain (n = 291, 35.66%), followed by bone (n = 269, 32.97%), liver (n = 259, 31.74%), and lung (n = 154, 18.87%). Moreover, a considerable proportion of patients (n = 487, 59.68%) received chemotherapy, and those who underwent chemotherapy experienced a lower incidence of early deaths.

Table 1 Characteristics of patients with stage IV LCNEC (N = 816).

Characteristic	Overall (N = 816)	No early death (N = 507)	Total early death (N = 309)	Cancer-specific early death (N = 279)	
Years	
 2010–2014	369 (45.22%)	232 (45.76%)	137 (44.34%)	127 (45.52%)	
 2015–2019	447 (54.78%)	275 (54.24%)	172 (55.66%)	152 (54.48%)	
Age	
 <65	378 (46.32%)	258 (50.89%)	120 (38.83%)	106 (37.99%)	
 65–74	272 (33.33%)	171 (33.73%)	101 (32.69%)	92 (32.97%)	
 >74	166 (20.34%)	78 (15.38%)	88 (28.48%)	81 (29.03%)	
Sex	
 Male	485 (59.44%)	292 (57.59%)	193 (62.46%)	174 (62.37%)	
 Female	331 (40.56%)	215 (42.41%)	116 (37.54%)	105 (37.63%)	
Race	
 White	688 (84.31%)	423 (83.43%)	265 (85.76%)	243 (87.10%)	
 Not white*	128 (15.69%)	84 (16.57%)	44 (14.24%)	36 (12.90%)	
Marital	
 Married	438 (53.68%)	280 (55.23%)	158 (51.13%)	143 (51.25%)	
 Single	163 (19.98%)	99 (19.53%)	64 (20.71%)	58 (20.79%)	
 Widowed/divorced/separated	215 (26.35%)	128 (25.25%)	87 (28.16%)	78 (27.96%)	
Median household income	
 <$50,000 USD	162 (19.85%)	93 (18.34%)	69 (22.33%)	62 (22.22%)	
≥$50,000 USD	507 (62.13%)	414 (81.66%)	240 (77.67%)	217 (77.78%)	
Laterality	
 Left	309 (37.87%)	194 (38.26%)	115 (37.22%)	103 (36.92%)	
 Right	507 (62.13%)	313 (61.74%)	194 (62.78%)	176 (63.08%)	
Primary site	
 Upper lobe	476 (58.33%)	305 (60.16%)	171 (55.34%)	156 (55.91%)	
 Middle lobe	39 (4.78%)	24 (4.73%)	15 (4.85%)	13 (4.66%)	
 Lower lobe	225 (27.57%)	131 (25.84%)	94 (30.42%)	84 (30.11%)	
 Others†	76 (9.31%)	47 (9.27%)	29 (9.39%)	26 (9.32%)	
Stage T	
 T1	146 (17.89%)	105 (20.71%)	41 (13.27%)	38 (13.62%)	
 T2	210 (25.74%)	143 (28.21%)	67 (21.68%)	60 (21.51%)	
 T3	202 (24.75%)	114 (22.49%)	88 (28.48%)	80 (28.67%)	
 T4	258 (31.62%)	145 (28.60%)	113 (36.57%)	101 (36.20%)	
Stage N	
 N0	199 (24.39%)	147 (28.99%)	52 (16.83%)	47 (16.85%)	
 N1	86 (10.54%)	60 (11.83%)	26 (8.41%)	24 (8.60%)	
 N2	345 (42.28%)	188 (37.08%)	157 (50.81%)	143 (51.25%)	
 N3	186 (22.79%)	112 (22.09%)	74 (23.95%)	65 (23.30%)	
Surgery	
 No	721 (88.36%)	421 (83.04%)	300 (97.09%)	272 (97.49%)	
 Yes	95 (11.64%)	86 (16.96%)	9 (2.91%)	7 (2.51%)	
Radiation	
 No	402 (49.26%)	225 (44.38%)	177 (57.28%)	154 (55.20%)	
 Yes	414 (50.74%)	282 (55.62%)	132 (42.72%)	125 (44.80%)	
Chemotherapy	
 No	329 (40.32%)	118 (23.27%)	211 (68.28%)	186 (66.67%)	
 Yes	487 (59.68%)	389 (76.73%)	98 (31.72%)	93 (33.33%)	
Bone metastasis	
 No	547 (67.03%)	356 (70.22%)	191 (61.81%)	171 (61.29%)	
 Yes	269 (32.97%)	151 (29.78%)	118 (38.19%)	108 (38.71%)	
Brain metastasis	
 No	525 (64.34%)	343 (67.65%)	182 (58.90%)	160 (57.35%)	
 Yes	291 (35.66%)	164 (32.35%)	127 (41.10%)	119 (42.65%)	
Liver metastasis	
 No	557 (68.26%)	383 (75.54%)	174 (56.31%)	153 (54.84%)	
 Yes	259 (31.74%)	124 (24.46%)	135 (43.69%)	126 (45.16%)	
Lung metastasis	
 No	662 (81.13%)	418 (82.45%)	244 (78.96%)	222 (79.57%)	
 Yes	154 (18.87%)	89 (17.55%)	65 (21.04%)	57 (20.43%)	
LCNEC = lung large cell neuroendocrine carcinoma.

* Not white, Black, Asian or Pacific Islander and American Indian/Alaska Native.

† Others, main bronchus, overlapping lesion of lung.

Among the 816 patients diagnosed with stage IV LCNEC, a substantial number of 309 individuals (37.87%) died within a mere 3-month period. Out of these fatalities, 279 (34.19%) were attributed to primary cancer, while 30 (3.68%) were caused by noncancer-related factors (Fig. 2A). Noncancer early deaths resulted from various causes, including heart disease (n = 7, 23.33%), pneumonia and influenza (n = 2, 6.67%), diabetes mellitus (n = 2, 6.67%), and infectious and parasitic diseases, including HIV (n = 2, 6.67%) (Fig. 2B). As the number of metastatic organ sites increased, the likelihood of early mortality escalated (Fig. 3A). Among the different types of metastasis, the highest overall early mortality rate occurred in individuals with liver metastasis (47.31%), followed by brain metastasis (37.35%), lung metastasis (32.14%), and bone metastasis (29.90%) (Fig. 3B).

Figure 2. Distribution of the incidence of cancer-specific early death (A) and all-cause early death (B) among stage IV LCNEC patients. LCNEC = lung large cell neuroendocrine carcinoma.

Figure 3. Distribution of early death among stage IV LCNEC patients stratified by the number of metastatic organs (A) and metastatic site (B). LCNEC = lung large cell neuroendocrine carcinoma.

3.2. Risk factors for early death

The study involved 816 patients, with 571 patients assigned at random to the training group and 245 patients to the internal validation group. In addition, 25 patients with stage IV LCNEC were included in the external validation cohort at the Second Affiliated Hospital of Nanchang University. Table 2 and Table S1, Supplemental Digital Content, http://links.lww.com/MD/N531 present additional intricacies regarding the clinical and pathological data of the patients. Importantly, there were no significant differences in patient characteristics between the training and internal validation groups (P > .05). Therefore, both groups are suitable for further follow-up research. In the training group, we observed significant correlations between various factors, such as age, T stage, N stage, treatment (including surgery, radiotherapy, and chemotherapy), the presence of cancer spread to bones, brain, or liver, and early death (Table 3). Then, we included the aforementioned 9 variables in the least absolute shrinkage and selection operator analysis (Fig. S1, Supplemental Digital Content, http://links.lww.com/MD/N531). Further analysis using multivariate logistic regression analysis showed that age, T stage, N stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, and liver metastasis were identified as significant independent risk factors for early death (Fig. 4). The absence of significant multicollinearity problems among the independent variables was indicated by the fact that all correlation coefficients between variable pairs were <0.7, and the VIF values were close to 1 (Fig. 4 and Fig. S2, Supplemental Digital Content, http://links.lww.com/MD/N531).

Table 2 Demographic information of patients with stage IV LCNEC in training and internal validation cohorts.

Characteristic	Training cohort (N = 571)	Internal Test cohort (N = 245)	P	
Years	.853	
 2010–2014	257 (45.01%)	112 (45.71%)		
 2015–2019	314 (54.99%)	133 (54.29%)		
Age	.122	
 <65	256 (44.83%)	122 (49.80%)		
 65–74	203 (35.55%)	69 (28.16%)		
 >74	112 (19.61%)	54 (22.04%)		
Sex	.135	
 Male	349 (61.12%)	136 (55.51%)		
 Female	222 (38.88%)	109 (44.49%)		
Race	.337	
 White	486 (85.11%)	202 (82.45%)		
 Not white*	85 (14.89%)	43 (17.55%)		
Marital	.808	
 Married	305 (53.42%)	133 (54.29%)		
 Single	112 (19.61%)	51 (20.82%)		
 Widowed/divorced/separated	154 (26.97%)	61 (24.90%)		
Median household income	.613	
 <$50,000 USD	116 (20.32%)	46 (18.78%)		
 ≥ $50,000 USD	455 (79.68%)	199 (81.22%)		
Laterality	.780	
 Left	218 (38.18%)	91 (37.14%)		
 Right	353 (61.82%)	154 (62.86%)		
Primary site	.664	
 Upper lobe	335 (58.67%)	141 (57.55%)		
 Middle lobe	25 (4.38%)	14 (5.71%)		
 Lower lobe	161 (28.20%)	64 (26.12%)		
 Others†	50 (8.76%)	26 (10.61%)		
Stage T	.985	
 T1	103 (18.04%)	43 (17.55%)		
 T2	146 (25.57%)	64 (26.12%)		
 T3	143 (25.04%)	59 (24.08%)		
 T4	179 (31.35%)	79 (32.24%)		
Stage N	.715	
 N0	140 (24.52%)	59 (24.08%)		
 N1	56 (9.81%)	30 (12.24%)		
 N2	241 (42.21%)	104 (42.45%)		
 N3	134 (23.47%)	52 (21.22%)		
Surgery	.401	
 No	501 (87.74%)	220 (89.80%)		
 Yes	70 (12.26%)	25 (10.20%)		
Radiation	.184	
 No	290 (50.79%)	112 (45.71%)		
 Yes	281 (49.21%)	133 (54.29%)		
Chemotherapy	.172	
 No	239 (41.86%)	90 (36.73%)		
 Yes	332 (58.14%)	155 (63.27%)		
Bone metastasis	.311	
 No	389 (68.13%)	158 (64.49%)		
 Yes	182 (31.87%)	87 (35.51%)		
Brain metastasis	.090	
 No	378 (66.20%)	147 (60.00%)		
 Yes	193 (33.80%)	98 (40.00%)		
Liver metastasis	.969	
 No	390 (68.30%)	167 (68.16%)		
 Yes	181 (31.70%)	78 (31.84%)		
Lung metastasis	.662	
 No	461 (80.7%)	201 (82.0%)		
 Yes	110 (19.3%)	44 (18.0%)		
LCNEC = lung large cell neuroendocrine carcinoma.

* Not white, Black, Asian or Pacific Islander and American Indian/Alaska Native.

† Others, Main bronchus, Overlapping lesion of lung.

Table 3 The univariable logistic regression analysis of all-cause and cancer-specific early death in the training cohort.

Variable	All-cause early death	Cancer-specific early death	
OR (95%CI)	P	OR (95%CI)	P	
Years	
 2010–2014	Reference		Reference		
 2015–2019	1.27 (0.90–1.78)	.178	1.17 (0.82–1.67)	.375	
Age	
 <65	Reference		Reference		
 65–74	1.24 (0.84–1.83)	.279	1.26 (0.84–1.89)	.262	
 >74	2.49 (1.58–3.93)	<.001***	2.61 (1.63–4.17)	<.001***	
Sex	
 Male	Reference		Reference		
 Female	0.71 (0.50–1.02)	.061	0.71 (0.49–1.02)	.068	
Race	
 White	Reference		Reference		
 Not white†	0.94 (0.58–1.52)	.807	0.84 (0.50–1.39)	.495	
Marital	
 Married	Reference		Reference		
 Single	1.18 (0.76–1.84)	.466	1.26 (0.80–2.00)	.318	
 Widowed/divorced/separated	1.26 (0.85–1.88)	.250	1.31 (0.87–1.98)	.196	
Median household income	
 <$50,000 USD	Reference		Reference		
 ≥ $50,000 USD	0.75 (0.50–1.14)	.175	0.79 (0.51–1.21)	.281	
Laterality	
 Left	Reference		Reference		
 Right	0.97 (0.69–1.38)	.871	1.00 (0.69–1.43)	.982	
Primary site	
 Upper lobe	Reference		Reference		
 Middle lobe	1.19 (0.52–2.74)	.675	1.17 (0.50–2.77)	.716	
 Lower lobe	1.15 (0.78–1.70)	.475	1.08 (0.72–1.61)	.719	
 Others‡	1.41 (0.77–2.57)	.265	1.33 (0.71–2.48)	.377	
Stage T	
 T1	Reference		Reference		
 T2	1.42 (0.80–2.53)	.234	1.48 (0.81–2.70)	.207	
 T3	2.98 (1.70–5.24)	<.001***	3.11 (1.73–5.59)	<.001***	
 T4	2.60 (1.51–4.48)	<.001***	2.60 (1.47–4.59)	<.001***	
Stage N	
 N0	Reference		Reference		
 N1	1.35 (0.67–2.72)	.402	1.35 (0.65–2.82)	.425	
 N2	3.18 (1.99–5.09)	<.001***	3.30 (2.02–5.38)	<.001***	
 N3	2.01 (1.19–3.40)	.010*	2.02 (1.16–3.51)	.013*	
Surgery	
 No	Reference		Reference		
 Yes	0.13 (0.06–0.31)	<.001***	0.10 (0.03–0.27)	<.001***	
Radiation	
 No	Reference		Reference		
 Yes	0.60 (0.43–0.85)	.004**	0.68 (0.48–0.97)	.031*	
Chemotherapy	
 No	Reference		Reference		
 Yes	0.16 (0.11–0.23)	<.001***	0.17 (0.12–0.25)	<.001***	
Bone metastasis	
 No	Reference		Reference		
 Yes	1.75 (1.22–2.50)	0.002**	1.79 (1.23–2.59)	0.002**	
Brain metastasis	
 No	Reference		Reference		
 Yes	1.83 (1.28–2.61)	<.001***	1.98 (1.37–2.85)	<.001***	
Liver metastasis	
 No	Reference		Reference		
 Yes	2.17 (1.51–3.11)	<.001***	2.25 (1.55–3.27)	<.001***	
Lung metastasis	
 No	Reference		Reference		
 Yes	1.24 (0.81–1.90)	.316	1.24 (0.80–1.92)	.338	
CI = confidence interval, OR = odds ratio.

† Not white, Black, Asian or Pacific Islander and American Indian/Alaska Native.

‡ Others, main bronchus, overlapping lesion of lung.

Bolded values represent statistical significance.

*P < .05; **P < .01; ***P < .001.

Figure 4. Multivariate logistic regression analyses were conducted to identify predictors of overall early death (A) and cancer-specific early death (B) in stage IV LCNEC patients. CI = confidence interval; LCNEC = lung large cell neuroendocrine carcinoma; OR = odds ratio; VIF = variance inflation factor.

3.3. Nomogram development

Following analysis of the multivariate logistic regression results, we developed user-friendly nomograms. The results obtained from the nomogram prediction models indicate that chemotherapy exhibited the highest predictive value in determining early death, both in terms of overall mortality and cancer-specific mortality (Fig. 5). To enhance accessibility and convenience for researchers and physicians, we created online tools in the form of overall and cancer-specific early death (Fig. S3, Supplemental Digital Content, http://links.lww.com/MD/N531) probability calculators (https://mlcnec.shinyapps.io/MLCNECOFALL/ and https://mlcnec.shinyapps.io/MLCNECCANCERSPECIFIC/). By inputting relevant clinical features and examining the charts and tables generated by the web server, medical professionals can quickly calculate the estimated likelihood of early death in stage IV LCNEC patients. These calculators have been carefully designed to be intuitive and easy to use, providing vital support in clinical decision-making. Ultimately, our hope is that by leveraging these tools, health care professionals can make informed decisions that lead to improved outcomes for their patients.

Figure 5. The predictive nomogram for (A) all-cause early death and (B) cancer-specific early death of stage IV LCNEC patients in the SEER database. LCNEC = lung large cell neuroendocrine carcinoma.

3.4. Nomogram evaluation

In this study, we found that the AUC for early deaths across all causes in the training group was 0.850, with a specific AUC of 0.853 for cancer-specific early deaths. In the internal validation group, the corresponding AUCs were 0.780 for all-cause early deaths and 0.769 for cancer-specific early deaths (Fig. 6). As shown in Figure 7, the calibration curves and Hosmer–Lemeshow test demonstrated good concordance between the predicted and observed results in both the training and internal validation groups. Furthermore, the decision curve analyses (DCAs) showed good advantages in forecasting early mortality for all causes and cancer-specific instances across the entire patient population, highlighting the positive clinical importance of these nomograms (Fig. 8). During the external validation process, only the cancer-specific nomogram for early death was assessed, given that early deaths across all hospital cohorts were primarily associated with LCNEC and its related factors. According to the results, the nomogram displayed a notably high predictive ability during external validation, as the AUC curve (Fig. 9A) revealed a value of 0.860. Moreover, the calibration curve (Fig. 9B) demonstrated that the nomogram exhibited a consistent and reliable performance, while DCA (Fig. 9C) showed an ideal net benefit.

Figure 6. Receiver operating characteristic (ROC) curves for discrimination of the nomograms in predicting all-causes and cancer-specific early death in the training cohort (A, B) and the internal validation cohort (C, D). ROC = receiver operating characteristic.

Figure 7. Calibration curves for assessing the calibration of the nomogram in predicting all-causes and cancer-specific early death in the training cohort (A, B) and the internal validation cohort (C, D).

Figure 8. Decision curve analyses (DCA) for the nomograms in predicting all-cause early death and cancer-specific early death in the training cohort (A, B) and the internal validation cohort (C, D). DCA = decision curve analyses.

Figure 9. Validation in the hospital cohort (A) calibration plots for the nomogram; (B) receiver operating characteristic (ROC) curve for the nomogram; (C) decision curve analysis (DCA) for the nomogram. DCA = decision curve analyses; ROC = receiver operating characteristic.

4. Discussion

According to previous research reports, patients with stage IV LCNEC typically have a relatively short survival time, with a median survival of approximately 4 to 9 months.[1,23,24] This result is disheartening, but it is also important to note that individual circumstances may vary. Additionally, data collected from the SEER database reveal that, regrettably, 37.87% of patients experience mortality within 3 months of receiving their initial diagnosis. Therefore, gaining a deeper understanding of the factors that contribute to early patient mortality can help ensure that patients receive comprehensive, individualized treatment and care, thereby increasing their survival time. Considering the current limited research on early death in stage IV LCNEC, this study used the SEER database as its primary data source with the aim of identifying several risk factors that contribute to early mortality in patients. Furthermore, prediction nomograms were developed to predict all-cause early death and cancer-specific death for stage IV LCNEC patients.

In this study, 9 independent risk factors associated with overall early death and cancer-specific early death in stage IV LCNEC patients were identified, including age, T stage, N stage, surgery, radiotherapy, chemotherapy, bone metastasis, brain metastasis, and liver metastasis. Based on these 9 independent risk factors, 2 nomograms were developed to dynamically predict the probabilities of overall early death and cancer-specific early death in stage IV LCNEC patients, including those who were untreated at diagnosis and those who underwent treatment. These nomograms were evaluated in both the training and validation cohorts. Considering the AUC and calibration plots in both cohorts, these nomograms demonstrated reliable discriminative and calibration abilities. Furthermore, as shown by DCA, these nomograms also exhibited good clinical utility. To make these nomograms more convenient for clinical use, this study also developed online calculators associated with these nomograms. These tools aid clinical physicians involved in palliative care and oncologists engaged in clinical trial design to identify patients at risk of early death, thus enabling tailored treatment recommendations. This is crucial for balancing treatment efficacy, potential side effects, and hospitalization risks. Moreover, the simplicity and intuitiveness of the nomograms may effectively reduce medical-patient conflicts arising from premature death.

In this study, it was observed that the majority of stage IV LCNEC patients died within 3 months of initial diagnosis due to the primary tumor, with only 3.68% of early deaths attributed to noncancer-specific causes. Notably, cardiovascular disease emerged as the primary reason for noncancer-specific early mortality. This finding was similar to previous conclusions reported by Sun et al.[25] This may be related to the cardiotoxicity caused by cancer treatment received by cancer patients during diagnosis or the patient’s previous cardiovascular disease.[26,27] These findings indicate the importance of intensifying cardiovascular disease surveillance in patients with stage IV LCNEC. However, due to the inability to collect data on whether patients have concomitant cardiovascular diseases in the SEER database, the early deaths of 25 patients collected in the external validation set are also primarily associated with LCNEC and its related factors. Therefore, our findings are only preliminary and require careful interpretation. Further research is needed to explore the relationship between LCNEC patients and cardiovascular-related mortality.

In patients with stage IV LCNEC, aggressive treatment is crucial in reducing early death rates. Our study found that surgery, radiation therapy, and chemotherapy can all reduce the risk of premature death. It is noteworthy that an increasing number of scholars are suggesting expanding the scope of surgical intervention to include stage IV NSCLC patients.[28] Ren et al analyzed data from 122,650 stage IV NSCLC patients, conducted propensity score matching (PSM) to balance baseline characteristics, and found that primary tumor surgical intervention improved survival outcomes in stage IV LCNEC patients, with surgical patients having twice the median survival time of nonsurgical patients.[29] Our study further confirms that stage IV LCNEC patients may gain a survival advantage from surgical intervention, consistent with previous research findings.[23,30] However, the proportion of patients undergoing surgery in this study was only 11.6% (95 cases), so the results may be biased to some extent. Large sample sizes and randomized controlled trials are needed to validate the impact of surgery on the survival of patients with stage IV LCNEC.

The role of radiation therapy in the treatment of localized or advanced LCNEC patients remains unclear.[31] Cao et al gathered data on 1480 LCNEC patients, categorized them into cohorts receiving either no radiation therapy or radiation therapy, and performed PSM analysis to adjust for baseline characteristics, revealing that LCNEC patients in the radiation therapy group exhibited markedly extended overall survival (P < .05).[32] Similarly, our study observed that radiation therapy may be an important protective factor against early death in stage IV LCNEC patients.

Our study found that the lack of chemotherapy is the most significant factor leading to early death in stage IV LCNEC patients. Despite ongoing debate regarding the optimal treatment for stage IV LCNEC and the persistently unsatisfactory long-term treatment outcomes, chemotherapy continues to be viewed as the primary treatment choice for patients afflicted with stage IV LCNEC.[33] Recent studies have indicated that a thorough understanding of the subtypes and classification of LCNEC is beneficial for guiding chemotherapy regimen selection in stage IV LCNEC patients. Wang et al analyzed data from 39 late-stage pulmonary LCNEC patients at Peking Union Medical College Hospital from January 2000 to October 2021, revealing that those with treatment-consistent molecular subtypes had significantly longer overall survival compared to those with inconsistent subtypes (median OS: 37.7 months vs 8.3 months, P = .046), especially evident in type II LCNEC patients treated with SCLC-based protocols (median OS: 37.7 months vs 10.5 months, P = .039).[34] However, this study is a small-sample retrospective analysis, and further large-scale prospective studies are needed to confirm.

Our research provides a more detailed stratification by age compared to other studies, helping to analyze the impact of different ages on patient survival more accurately. We have found a strong correlation between age and the risk of early mortality in LCNEC patients, with increasing age being associated with an increasing risk of early mortality, which aligns with the findings of other studies.[10] This may be attributed to the fact that younger patients typically have better physical conditions and higher opportunities for treatment acceptance. In this study, 69.3% of young patients (<65 years old) received chemotherapy, 59.5% of middle-aged patients (65–74 years old) received chemotherapy, and only 37.9% of elderly patients (>74 years old) received chemotherapy. Cao et al collected data from 1619 patients with LCNEC from the SEER database, and the research findings indicate that aggressive and effective chemotherapy can still significantly improve survival rates in elderly patients with pulmonary LCNEC (≥ 65 years old).[35] Therefore, in the future, clinical doctors need to focus on strengthening the management of chemotherapy treatments for these patients, especially elderly ones.

Our study found that brain, bone, and liver metastases are the main risk factors leading to early death in stage IV LCNEC patients, which is consistent with the previous findings by Chen et al.[36] Their study analyzed the pattern of organ metastasis in LCNEC patients, revealing that brain metastasis is the most common, while liver metastasis demonstrates the worst prognosis. Additionally, our research unveiled a noteworthy trend in which the risk of early mortality displayed a gradual ascent in direct proportion to the number of metastatic sites. Therefore, for stage IV LCNEC patients, proactive implementation of multidisciplinary treatment is crucial for determining the optimal treatment regimen, alleviating patient suffering, and extending survival.

Although our study has advantages, we acknowledge that there are still some limitations. First, the SEER database lacks comprehensive data on influential factors such as lifestyle choices (such as alcohol consumption and smoking history) and comorbidities (such as heart disease). Second, specific details regarding treatment regimens, cycles, and dosages are not provided within the SEER database. Third, despite conducting external validation, the limited number of cases in the validation cohort necessitates further external validation through larger-scale multicenter studies. Last, as a retrospective study, potential selection bias may have impacted our findings.

5. Conclusions

In summary, our analysis of the SEER database found that over one-third of stage IV LCNEC patients died within just 3 months. To aid physicians in identifying high-risk individuals and making informed treatment decisions, we developed and validated nomograms for predicting the probability of early mortality.

Author contributions

Conceptualization: Hongquan Xing, Xinyi Zhang.

Data curation: Hongquan Xing, Cong Wu, Dongdong Zhang.

Formal analysis: Hongquan Xing, Cong Wu.

Funding acquisition: Xinyi Zhang.

Investigation: Hongquan Xing, Dongdong Zhang.

Methodology: Hongquan Xing, Cong Wu, Dongdong Zhang, Xinyi Zhang.

Project administration: Hongquan Xing, Cong Wu, Xinyi Zhang.

Resources: Hongquan Xing, Xinyi Zhang.

Software: Hongquan Xing, Cong Wu.

Supervision: Hongquan Xing, Xinyi Zhang.

Validation: Cong Wu, Dongdong Zhang, Xinyi Zhang.

Visualization: Hongquan Xing, Xinyi Zhang.

Writing – original draft: Hongquan Xing.

Writing – review & editing: Hongquan Xing, Cong Wu, Dongdong Zhang, Xinyi Zhang.

Supplementary Material

Abbreviations:

AUC area under the curve

DCA decision curve analysis

LCNEC lung large cell neuroendocrine carcinoma

NSCLC non-small cell lung cancer

SCLC small cell lung cancer

SEER surveillance, epidemiology, and end results

VIF variance inflation factor

This work was supported by the Domestic Cooperation Project of the Science and Technology Department of Jiangxi province (Grant/Award Number: 20212BDH81021).

This study was based on public use data from the SEER database. The study did not require informed consent from the SEER registered cases, and the authors obtained Limited-Use Data Agreements from SEER.

The authors have no conflicts of interest to disclose.

The data that support the findings of this study are available from a third party, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Xing H, Wu C, Zhang D, Zhang X. Early death incidence and prediction in stage IV large cell neuroendocrine carcinoma of the lung. Medicine 2024;103:37(e39294).
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