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

MD-D-24-04087
00040
10.1097/MD.0000000000039659
3
6100
Research Article
Observational Study
Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach
Li Yang MM 994705283@qq.com
a
Xiao Qinyu MM 1455161644@qq.com
b
Chen Haiqi MD c
Zhu Enzhao PhD d
Wang Xin MD e
Dai Jianmeng MM 2976350744@qq.com
d
Zhang Xu MM 2351857@tongji.edu.cn
d
Lu Qiuyi MA 2481940462@qq.com
d
Zhu Yanming MD d
https://orcid.org/0009-0000-5669-5719
Yang Guangliang MD c*
a Heilongjiang University of Chinese Medicine, Harbin, China
b Zhejiang Chinese Medical University, Zhejiang, China
c Department of Oncology, Dongying District Hospital, Dongying, Shandong, China.
d School of Medicine, Tongji University, Shanghai, China
e College of Electronic and Information Engineering, Tongji University, Shanghai, China.
* Correspondence: Guangliang Yang, Department of Oncology, Dongying District Hospital, Dongying, Shandong 257000, China (e-mail: awssqdlg001@163.com).
13 9 2024
13 9 2024
103 37 e3965916 4 2024
19 8 2024
22 8 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.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63–0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96–14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83–21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.

causal inference
chemoradiotherapy
deep learning
head and neck squamous cell carcinoma
nonsurgical treatment
OPEN-ACCESSTRUE
SDCT
==== Body
pmc1. Introduction

Recognized as the 6th most prevalent cancer worldwide, head and neck squamous cell carcinoma (HNSCC) leads to approximately 450,000 deaths annually.[1] It is notable that over half of the cases of HNSCC are observed among the elderly individuals (defined as those aged 65 years and older).[2] As the global population continues to age, the treatment decisions made for elderly patients with head and neck squamous cell carcinoma assume an increasingly pivotal role.[3]

Typically, the treatment modalities for HNSCC involve a combination of surgery, chemotherapy, and radiotherapy.[4] However, in view of the lack of clarity regarding treatment guidelines, poor adherence,[5] significant surgical risks,[6] and reluctance to undergo surgery[7] among the elderly, there arises a pressing need for the development of streamlined and efficient nonsurgical therapies tailored to this demographic.

Definite radiotherapy (RT) serves as the primary nonsurgical therapy for HNSCC patients, while the addition of chemotherapy is a matter of ongoing debate out of the greater toxicity observed in the elderly population.[8,9] A number of studies have indicated that chemotherapy can confer a significant survival benefit to patients with nonmetastatic HNSCC.[10] However, this benefit is observed to diminish with age, becoming negligible for patients over the age of 70 years.[11] In addition to age, other factors such as subsite, stage and histopathologic features are equally important in the final treatment decision,[12] exemplified by the consensus on employing definite chemoradiotherapy (CRT) in organ preservation therapy for advanced laryngeal cancer.[13] It is therefore imperative that the intensity of treatment is not unduly reduced merely to mitigate complications. Instead, it is of paramount importance to integrate individual patient characteristics into the development of optimal treatment recommendations.[14]

Traditionally, the assessment of treatment heterogeneity relies on segmenting patients into multiple independent subgroups, employing clinical expertise to repetitively simulate individual differences, and implementing randomized controlled trials within each subgroup. Although this approach is thorough, its extensive costs and time-consuming nature have limited its broader application.[15] In response to these challenges, this study has introduced machine learning as the core analytical tool, particularly leveraging its unique advantages in predicting Individual Treatment Effects (ITE) through counterfactual reasoning.[16] Furthermore, recent studies have shown that treatment recommendation systems supported by deep learning (DL) technology can effectively discern varying treatment responses, thereby enabling the formulation of optimal treatment strategies for each patient.[17,18]

The objective of this study is to develop the most efficient nonsurgical therapy for elderly HNSCC patients, employing DL models to prevent unnecessary treatments and maximize survival duration.

2. Methods

2.1. Study design and setting

This was a population-based retrospective cohort study aimed to formulate personalized and nonsurgical treatment recommendations for elderly patients diagnosed with HNSCC through the utilization of DL methodologies. The comprehensive study encompassed participants sourced from the Surveillance, Epidemiology, and End Results 18 database, a comprehensive registry that monitors cancer trends across 18 distinct regions of the United States, collectively encompassing approximately 27.8% of the country’s population.[19] This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines.[20]

The study included elderly patients (aged 65 and above) diagnosed with HNSCC originating from 4 anatomical regions: the oral cavity, sinonasal cavity, pharynx, and larynx. These patients, diagnosed with a single primary malignant cancer between January 1, 2004, and December 31, 2015, and received definitive RT or CRT were considered for inclusion. Notably, salivary gland carcinomas were not evaluated due to differences in their pathology and treatment approaches. Exclusion criteria strictly adhered to: (1) lack of demographic data, (2) unspecified histologic grades or tumor types, (3) unconfirmed tumor locations, (4) ambiguity in laterality or cases of bilateral HNSCC, (5) unknown TNM staging, (6) unreported tumor size, (7) undefined treatment modality, (8) incomplete follow-up records, (9) patients who underwent surgical intervention, and (10) those with multiple malignancies. The inclusion process is visually represented in Figure 1A. It is essential to highlight that the study did not delve into the sequencing of chemotherapy, thus, the findings should serve as a preliminary guide for integrating chemotherapy with definitive RT. Further investigations are warranted to unravel the intricate relationship between the order of chemotherapy and RT in this patient population.

Figure 1. Inclusion process and model architecture. (A) Selection and exclusion criteria for patient inclusion. (B) The model architecture of Self-Normalizing Balanced individual treatment effect for survival data.

Baseline demographics (gender, age, ethnicity, and marital status), comprehensive tumor characteristics (including anatomical location, size, laterality, histological grade, and TNM staging), and specific treatment details (particularly the administration of chemotherapy) were meticulously extracted from the Surveillance, Epidemiology, and End Results database for each case. Patients were rigorously screened, and those with any missing or unconfirmed clinical characteristic data were excluded from the study. The primary endpoint of this research endeavor was overall survival (OS), defined as the interval extending from the date of diagnosis to the occurrence of death from any cause. For patients who were still alive as of December 31, 2020, their survival data were right-censored. Tumor staging was meticulously assigned in accordance with the 7th edition of the American Joint Committee on Cancer Staging Manual, ensuring uniformity and precision in the assessment of disease severity.

2.2. Algorithms

The T-learner employed 2 separate models to estimate the ITE, defined as: ITE= μ1x- μ0x, where μ1 and μ0 represent the models trained exclusively on the respective treatment populations.[21] While the T-learner effectively mitigates certain confounding factors, it is not without limitations. Specifically, it may encounter inconsistencies in predictive performance[16] and introduce bias in treatment allocation.[22] These drawbacks stem primarily from the inherent differences in patient populations and imbalanced baseline characteristics between the 2 distinct treatment groups.

Balanced Individual Treatment Effect for Survival data (BITES)[22] is an innovative semi-parametric DL survival regression model that leverages Integral Probability Metrics to maximize the p-Wasserstein distance between different treatment arms. This approach demonstrates BITES’ exceptional ability to mitigate imbalance in both the covariate space[23] and latent representations.[24] By integrating a unified model architecture with dual output heads, BITES replaces the individual estimators traditionally used in the T-learner framework. Through end-to-end training, BITES effectively addresses the issue of inconsistent patient numbers across treatment groups, thereby enhancing its robustness and reliability.

The Self-Normalizing Balanced individual treatment effect for survival data (SNB)[25] is a recently introduced DL model, which is depicted in Figure 1B. SNB comprises a shared network and 2 risk networks. In the shared network adopts a balanced representation generation strategy akin to BITES. And each risk network represents the corresponding treatment group, akin to T-learner, which is comprised with self-normalizing neural network. The SNB has better feature extraction ability and better treatment recommendation efficacy.

2.3. Calculation of individual treatment effect

In the context of estimating the ITE, a fundamental constraint lies in the fact that only a single outcome can be observed per patient. Consequently, to assess the potential outcomes under alternative treatment scenarios, predictive models are indispensable. These models generate the individual survival distribution by leveraging predicted log hazard ratios and treatment-specific baseline hazards. This approach enables a nuanced understanding of how survival probabilities evolve over time, providing valuable insights into the dynamic interplay between treatment and patient outcomes.

We defined the potential outcome in a clinically meaningful manner, utilizing a threshold-based metric to quantify the time it takes for an individual patient’s mortality risk to reach a critical level of 50%. This pivotal point, termed time at risk (TaR), offers a straightforward and interpretable way to assess the impact of treatment on patient survival. The ITE calculation formula can be described as: ITETaRX;P;T=TaRX; P=50%;T=1- TaRX; P=50%;T=0, where T = 1 indicates definite CRT, T = 0 indicates definite RT, x indicates the covariates, P indicates the mortality ratio of a specific patient. Individualized treatment recommendations can then be derived based on the value of ITE.

2.4. Model development, validation, and treatment recommendation

Five models in total: SNB, BITES Cox Mixtures with Heterogeneous Effects (CMHE),[26] DeepSurv,[27] and Cox proportional hazards model were trained. The DeepSurv and Cox proportional hazards model were trained and implemented with T-learner structure.

Initially, we identified patients diagnosed in 2004 as an external testing cohort, deliberately excluding them from the model training process. This cohort was selected due to the alignment of critical variable definitions, such as TNM staging, with those used in subsequent years, ensuring consistency in our evaluation framework. Moreover, the extended follow-up period of the 2004 cohort provided a robust basis for assessing the long-term predictive accuracy and effectiveness of our models. For the remaining dataset, we adopted a stratified random sampling approach, assigning 70% of the patients to a training set for model development and reserving the remaining 30% as a test set, ensuring their independence from the training process. This setup enabled an unbiased evaluation of our models’ performance. To refine our models, we implemented a rigorous fivefold cross-validation strategy during the training phase. This approach involved repeatedly partitioning the training set into 5 equal subsets, with each subset serving as the validation set in 1 iteration while the remaining 4 subsets were used for training. This cycle was repeated until the validation loss failed to improve over 1000 iterations, ensuring the optimal configuration of our models’ hyperparameters.

To evaluate the consequences of adhering to our model’s recommendations, we stratified patients into 2 distinct groups: the “Consistent” (Consis.) group, whose actual treatment adhered to the model’s guidance, and the “Inconsistent” (Inconsis.) group, whose treatment deviated from the recommended course. To quantify the protective benefits conferred by our models, we computed key metrics including the multivariate hazard ratio (HR), the 10-year risk difference (RD), and the difference in the 10-year restricted mean survival time (DRMST) between these 2 groups. To mitigate any potential biases arising from baseline imbalances between the Consis. and Inconsis. groups, we applied inverse probability treatment weighting (IPTW). This approach ensured that our analyses were robust and unbiased, enabling us to accurately assess the impact of our model’s recommendations. Across all models employed, we maintained a consistent methodology for estimating ITE, ensuring comparability and reliability in our findings.

2.5. Statistical analyses

All statistical analyses were conducted using R version 4.1.3 and Python version 3.8. Models were built with Python packages Pytorch 2.0.0 and scikit-survival 0.19.0 with main codes were provided by original papers cited above in the Github. Metrics were calculated with R packages survival and rms. IPTW was conducted using R package ipw. The mixed-effects linear regression was developed using R package lme4. We have made some improvements and integrations to the source codes, which is open source in Github: https://github.com/Guang770/MyPublication. This repository serves as a comprehensive documentation hub for our model codes, ITE calculations, and associated methodologies. For clarity and precision, we present continuous variables using median values alongside their interquartile range (IQR), while categorical variables are reported in a straightforward manner as counts and percentages (%). To assess and compare survival outcomes, we have leveraged the log-rank test on Kaplan–Meier curves, ensuring a rigorous and statistically sound evaluation of our findings.

3. Results

3.1. Patients

A total of 4276 elderly patients with HNSCC, who possessed complete follow-up records and met the stringent inclusion criteria, were enrolled in this comprehensive study. The overall mortality rate observed was 76.1%, with a 95% confidence interval (CI) from 74.8% to 77.3%, over a median follow-up period of 30 months, accompanied by an IQR of 10 to 77 months. The median age of the patients was 72 years, with an IQR spanning from 68 to 78 years, while the median tumor size measured 30 mm, with an IQR of 21 to 42 mm. Regarding treatment modalities, 1395 patients (32.6%) underwent RT alone, whereas 2881 patients (67.4%) received concurrent CRT. The baseline clinical characteristics of the entire patient cohort are comprehensively outlined in Table S1, Supplemental Digital Content, http://links.lww.com/MD/N544 providing a detailed snapshot of the study population.

3.2. Model performance

The testing set consisted of 1204 patients, while an additional 263 patients diagnosed in 2004 comprised the external testing sets. To evaluate the models’ performance, we calculated various indicators within both the testing and external testing sets, adhering to a predefined 10-year time horizon. To ensure fairness in comparisons and address potential biases stemming from superior prognostic factors in the Consis. group, we employed IPTW to adjust for baseline imbalances between the Consis. and Inconsis. groups. This adjustment encompassed demographic and tumor-related characteristics such as age, sex, marital status, tumor location, laterality, grade, TNM stage, and tumor size. Notably, treatment variables were left unadjusted, as they were assessed postexposure (i.e., after the treatment recommendation) and could potentially introduce unmeasured confounding factors.[28] The comprehensive model performance, including these adjustments, is presented in Table 1.

Table 1 Performance of the models.

Model	IBSa	IBSb	HR	IPTW-adjusted HR	RD (%)	IPTW-adjusted RD (%)	DRMST (month)	IPTW-adjusted DRMST (month)	
Performance in the testing set	
SNB	0.12 (0.11–0.13)	0.18 (0.17–0.19)	0.76 (0.64–0.89)	0.74 (0.63–0.87)	10.90 (5.94–15.80)	9.92 (4.96–14.90)	15.50 (10.09–20.92)	16.42 (10.83–21.22)	
BITES	0.12 (0.11–0.13)	0.17 (0.16–0.18)	0.80 (0.69–0.93)	0.84 (0.71–0.98)	9.38 (4.38–14.40)	9.72 (4.81–14.60)	13.40 (7.96–18.84)	12.28 (6.70–18.02)	
CMHE	0.13 (0.11–0.14)	0.19 (0.18–0.19)	1.02 (0.77–1.36)	0.68 (0.46–1.00)	2.39 (‐4.93–9.71)	16.40 (11.70–21.10)	‐3.67 (‐11.31–3.96)	‐2.13 (‐10.57–6.76)	
DeepSurv	0.20 (0.17–0.23)	0.28 (0.26–0.31)	0.80 (0.69–0.92)	0.85 (0.73–0.98)	4.92 (‐0.06–9.90)	4.02 (‐0.85–8.89)	6.19 (0.81–11.57)	5.25 (1.18–11.16)	
CPH	0.14 (0.12–0.15)	0.17 (0.16–0.18)	0.76 (0.65–0.89)	0.76 (0.65–0.90)	8.30 (3.10–13.50)	6.75 (1.57–11.90)	12.73 (7.02–18.44)	13.56 (8.46–17.87)	
NCCN	–	–	0.63 (0.50–0.79)	0.78 (0.48–1.24)	0.39 (‐6.08–8.95)	1.43 (‐6.08–8.95)	13.81 (5.47–22.15)	14.23 (7.79–21.37)	
Performance in the external testing set	
SNB	0.10 (0.08–0.13)	0.16 (0.14–0.17)	0.68 (0.48–0.97)	0.64 (0.44–0.93)	6.30 (3.50–9.62)	6.42 (2.13–10.20)	15.97 (5.83–26.11)	17.00 (8.65–26.61)	
BITES	0.11 (0.08–0.13)	0.14 (0.12–0.16)	0.72 (0.52–1.00)	0.80 (0.57–1.11)	‐0.20 (‐6.62–6.22)	2.65 (‐3.18–8.49)	13.05 (2.64–23.46)	13.59 (3.79–19.53)	
CMHE	0.08 (0.06–0.10)	0.15 (0.13–0.16)	Ref.	Ref.	0.00 (‐3.15–3.15)	0.00 (‐3.08–3.06)	3.39 (‐7.55–14.32)	2.93 (‐2.43–6.20)	
DeepSurv	0.13 (0.10–0.17)	0.23 (0.19–0.28)	0.98 (0.71–1.34)	0.99 (0.72–1.37)	4.13 (‐1.72–9.99)	4.50 (‐1.38–10.40)	3.39 (‐7.55–14.32)	7.72 (‐3.28–17.91)	
CPH	0.12 (0.11–0.13)	0.14 (0.13–0.15)	0.91 (0.66–1.25)	1.02 (0.73–1.41)	2.62 (‐3.53–8.76)	‐3.54 (‐2.20–9.27)	7.88 (‐2.78–18.54)	7.18 (‐3.54–16.67)	
NCCN	–	–	1.10 (0.69–1.76)	0.87 (0.48–1.58)	1.80 (‐5.66–9.25)	‐1.53 (‐5.96–9.02)	10.43 (1.86–19.00)	12.51 (0.71–24.45)	
BITES = Balanced Individual Treatment Effect for Survival data, CMHE = Cox Mixtures with Heterogeneous Effects, CPH = Cox proportional hazards model, DRMST = the difference in the 10-year restricted mean survival time, HR = hazard ratio, IBS = integrated Brier score, NCCN = National Comprehensive Cancer Network treatment guidelines, RD = 10-year risk difference, SNB = Self-Normalizing Balanced individual treatment effect for survival data; a, integrated Brier score in the radiation group; b, integrated Brier score in the chemoradiation group. Ref., the associated statistical model was not successfully fitted. Bolded font indicates that the model performs best in this metric. Without regard to surgery, NCCN guidelines recommend patients with T1N0 hypopharynx cancer, T1–2N0 nasopharynx cancer, T1–2N0 larynx cancer or T1–2 ethmoid sinus cancer to receive RT, and patients with T2–4a or T1N+ hypopharynx cancer, T3–4 or N2–3 nasopharynx cancer, T3–4a or N+ larynx cancer, T3–4 ethmoid sinus cancer, T4b or M1 of all subsites to receive CRT. It is important to note that we have listed only those patient groups that are explicitly recommended in the NCCN guidelines.

Integrated Brier score (IBS) was calculated to measure the discrepancy between the predicted and actual survival distributions within both factual and counterfactual scenarios, as it can holistic view of prediction accuracy across the whole-time horizon[29] and is also considered as the phenotyping purity for individualized causal inference model with time-to-event outcome.[26] In both the testing (IBS in the radiotherapy group [IBSa]: 0.12, 95% CI, 0.11–0.13; IBS in the chemoradiation group [IBSb]: 0.17, 95% CI, 0.16–0.18) and the external testing sets (IBSa: 0.11, 95% CI, 0.08–0.13; IBSb: 0.14, 95% CI, 0.12–0.16), BITES exhibited the highest discrimination. Following BITES, SNB (IBSa in the testing set: 0.12, 95% CI, 0.11–0.13), demonstrated the best discrimination in radiotherapy group in the testing set, while CMHE (IBSa in the external testing set: 0.08, 95% CI, 0.06–0.10) that performed best in radiotherapy group in the external testing set.

The models predicted patients’ factual and counterfactual survival solely based on baseline covariates. Subsequently, the ITE and treatment recommendations were derived. The key metrics of interest revolve around assessing the extent of survival advantages attained by adhering to model recommendations, which is reflected in the evaluation of the protective effect of the Consis. group compared to the Inconsis. group. We designated the metrics that determine the model’s performance as those corrected with IPTW, as they were largely unaffected by other prognostic factors. Additionally, we compared the treatment recommendations provided by the National Comprehensive Cancer Network (NCCN) guidelines with those generated by the models. NCCN guidelines recommend patients with T1N0 hypopharynx cancer, T1–2N0 nasopharynx cancer, T1–2N0 larynx cancer or T1–2 ethmoid sinus cancer to receive RT, and patients with T2–4a or T1N + hypopharynx cancer, T3–4 or N2–3 nasopharynx cancer, T3–4a or N + larynx cancer, T3–4 ethmoid sinus cancer, T4b or M1 of all subsites to receive CRT. Notably, only patients clearly meeting the recommendations outlined in the NCCN guidelines were included in the analysis, while the remaining patients were excluded. Patients whose actual treatment adhered to the NCCN guidelines were compared with those whose treatment deviated from them.

In the testing set, adhering to SNB recommendations yielded the most significant enhancement in survival (IPTW-adjusted HR: 0.74, 95% CI, 0.63–0.87; IPTW-adjusted RD: 9.92, 95% CI, 4.96–14.90; IPTW-adjusted DRMST: 16.42, 95% CI, 10.83–21.22). CMHE (IPTW-adjusted RD: 16.40, 95% CI, 11.70–21.10) can significantly reduce patients’ 10-year mortality, however, other metrics did not reach statistical significance. Following NCCN guidelines resulted in the best HR (HR: 0.63, 95% CI, 0.50–0.79) and an extension of survival time within 10 years (DRMST: 13.81, 95% CI, 5.47–22.15), but this advantage disappeared after IPTW correction compared with SNB (IPTW-adjusted HR: 0.78, 95% CI, 0.48–1.24; IPTW-adjusted RD: 1.43, 95% CI, −6.08–8.95; IPTW-adjusted DRMST: 14.23, 95% CI, 7.79–21.37). Subsequently, we conducted a sensitivity analysis of SNB across different years of diagnosis (Table S2, Supplemental Digital Content, http://links.lww.com/MD/N544). Treatment recommendation performance of SNB was largely consistent across years.

In the external testing set, SNB demonstrated the best performance (IPTW-adjusted HR: 0.64, 95% CI, 0.44–0.93; IPTW-adjusted RD: 6.42, 95% CI, 2.13–10.20; IPTW-adjusted DRMST: 17.00, 95% CI, 8.65–26.61). Although NCCN exhibited a statistically significant increase in DRMST (DRMST: 10.43, 95% CI, 1.86–19.00; IPTW-adjusted DRMST: 12.51, 95% CI, 0.71–24.45), it failed to reduce patients’ mortality or remain protective in multivariate analysis. Thus, SNB emerged as the optimal treatment recommendation tool in both testing and external testing sets, surpassing other models and NCCN guidelines.

The Kaplan–Meier curves, depicting the survival outcomes for patients in the Consis. group as recommended by SNB versus those in the Inconsis. group, are presented in Figure 2A for the testing set and Figure 2B for the external testing sets, respectively. These visualizations offer a clear comparison of the survival patterns between the 2 groups across the different test cohorts. Better OS of the Consis. Group was observed in both the testing (P of Log-rank test = .1100; P of IPTW-adjusted Log-rank test = .0020) and external testing sets (P of Log-rank test = .0014; P of IPTW-adjusted Log-rank test < .0001).

Figure 2. The Kaplan–Meier curves of Consis. and Inconsis. groups. (A) Kaplan–Meier curves comparing the overall survival between the Consis. group and the Inconsis. group in the testing set. P values are derived from the Log-rank test (P = .11) and IPTW-adjusted Log-rank test (IPTW-adjusted P = .0020). (B) Kaplan–Meier curves comparing the overall survival between the Consis. group and the Inconsis. group in the external testing set. P values are from the Log-rank test (P = .0014) and IPTW-adjusted Log-rank test (IPTW-adjusted P < .0001). IPTW: inverse probability treatment weighting.

To investigate whether the potential protective effect of SNB was influenced by any imbalance in treatment proportions, we employed the interventional natural direct effect (INDE) methodology, as introduced by Diaz et al,[30] which severs the effect of treatment variables on OS improvement. By treating surgical and adjuvant treatments as mediators and adjusting for baseline characteristics, we calculated the INDE and interventional natural indirect effect (INIE) for both the testing and external testing sets. The results are presented in Figure S1A and B, Supplemental Digital Content, http://links.lww.com/MD/N544, respectively, where the INDE and INIE are visualized as the slopes of linear regression models, providing a quantitative assessment of their magnitudes. SNB recommendation exhibited a direct effect on OS improvement (INDE in the testing set: ‐0.06, 95% CI, ‐0.06 to ‐0.04; INIE in the testing set: ‐0.02, 95% CI, ‐0.02 to ‐0.01; INDE in the external testing set: ‐0.08, 95% CI, ‐0.09 to ‐0.08; INIE in the external testing set: ‐0.04, 95% CI, ‐0.05 to ‐0.04), which remained unaffected by treatments.

The standardized mean differences before and after IPTW correction are displayed in Figure S2A, Supplemental Digital Content, http://links.lww.com/MD/N544 for the testing set and Figure S2B, Supplemental Digital Content, http://links.lww.com/MD/N544 for the external testing set. Notably, after applying IPTW correction, the covariates achieved a balanced state, with between-group standardized mean differences reduced to values <0.1, indicating effective mitigation of baseline imbalances and enhancing the validity of subsequent statistical comparisons.[31]

3.3. Treatment heterogeneity

Treatment heterogeneity manifests in the presence of significantly varied average treatment effects across different subgroups, highlighting that patients with distinct characteristics respond disparately to the same treatment. Patients were categorized into chemoradiotherapy recommended and CRT not recommended groups based on whether chemotherapy was recommended by SNB. Likewise, patients were divided into chemoradiotherapy recommended by NCCN (CRN) and RT recommended by NCCN (RTN) groups determined by whether the patients adhered to NCCN guidelines. This analysis was conducted on the combined population of testing and external testing sets. Figure 3 illustrates the HR and IPTW-adjusted HR of CRT compared with RT in these subgroups.

Figure 3. Average treatment effect and treatment heterogeneity. CNR = chemoradiotherapy not recommended; CRN = chemoradiotherapy recommended by NCCN; CRR = chemoradiotherapy recommended; HR = hazard ratio; IPTW = inverse probability treatment weighting; NCCN = National Comprehensive Cancer Network treatment guidelines; RTN = radiotherapy recommended by NCCN.

In overall population, CRT was a protective effect (IPTW-adjusted HR: 0.69, 95% CI, 0.58 0.83). This effect was marginally enhanced when SNB predicted a positive ITE of chemotherapy (IPTW-adjusted HR in the chemoradiotherapy recommended group: 0.65, 95% CI, 0.54–0.77). Conversely, CRT turned out to pose a risk effect in the CRT not recommended group (IPTW-adjusted HR: 1.44, 95% CI, 1.12–1.79).

The treatment heterogeneity was also acknowledged by NCCN (HR in the CRN group: 0.69, 95% CI, 0.53–0.91; HR in the RTN group: 1.77, 95% CI, 1.07–2.44). Nevertheless, the results turned negative after IPTW correction (IPTW-adjusted HR in the CRN group: 1.09, 95% CI, 0.43–1.79; IPTW-adjusted HR in the RTN group: 1.66, 95% CI, 0.98–2.33).

3.4. Deep learning-based treatment insights

The ITE values signify the disparity in TaR between CRT and RT, representing the additional time required for an individual patient’s mortality rate to reach 50% when treated with CRT compared to RT. To gain insights into the factors influencing these ITE values, we performed a multivariate linear regression analysis, leveraging the combined dataset from both the testing and external testing sets. In such case, the beta values obtained can be interpreted as follows: when other features remain constant, the presence of this covariate or an increase of 1 unit that causes the difference in the time for an individual patients’ mortality rate to reach 50% when receiving CRT over RT to averagely increase beta. These results are depicted in Figure 4A.

Figure 4. Model interpretation. (A) Interpretation of model recommendation behavior. (B) Interpretation of overall output using SurvSHAP(t).

It was observed that patients with larger tumor size (0.09, 95% CI, 0.09–0.15), stage IV disease (6.62, 95% CI, 4.31–8.93), presence of distant metastasis (4.87, 95% CI, 1.92–7.66), and tumors located in subsites including lip (6.07, 95% CI, 3.75–9.39), other parts of the tongue (5.73, 95% CI, 0.60–10.86), gum (7.99, 95% CI, 3.87–12.11), and sinus (5.17, 95% CI, 3.32–7.02) were more likely to benefit from CRT. Conversely, male (‐2.50, 95% CI, ‐3.96 to ‐1.05) HNSCC patients with older age (‐1.83, 95% CI, ‐1.92 to ‐1.75) or stage I disease (‐13.35, 95% CI, ‐24.04 to ‐2.67) were more inclined to be recommended for RT without chemotherapy, with the effectiveness of CRT diminishing with increasing age.

3.5. Model interpretation

We employed SurvSHAP(t), a pioneering methodology that offers a theoretically rigorous framework for time-dependent interpretations,[32] to decipher the functional output of SNB. As depicted in Figure 4B, we aggregated the 8 most influential variables, arranged in descending order based on their aggregated Shapley values across 500 observations. The horizontal bars represent the distribution of these variables’ ranking, with the color intensity indicating their frequency of occupying the top (first), second, and subsequent positions. Notably, the “treatment” variable encapsulates the effects stemming from the utilization of various risk networks and baseline hazards.

Age was deemed the most important prognostic factor in 341 samples, followed by histologic grade, location, and TNM stage.

In Figure S3, Supplemental Digital Content, http://links.lww.com/MD/N544 we showcase a case study where a patient from the external testing set was randomly selected and subjected to SNB analysis. This analysis vividly depicts the patient’s survival probability under different treatment plans. By leveraging the predicted survival distributions, we are able to compute various metrics that quantify survival benefits, such as the difference in mortality rates, TaR, and restricted survival time (specifically, survival within a 10-year period). These insights empower users to make well-informed decisions regarding the most suitable treatment plan for the individual patient.

4. Discussion

Given the complex medical conditions[33] and lower compliance[5] often observed in the elderly population, it is crucial to streamline treatment regimens as much as possible. However, the indiscriminate reduction of treatment to mitigate complications may result in some patients not receiving adequate therapy, which is instead detrimental.[34] Therefore, personalized treatment plans tailored to individual characteristics are essential for optimizing patient outcomes.[35]

SNB was meticulously evaluated in this study. It outperformed state-of-the-art models, real-world physician choices, and NCCN guidelines. After diligent efforts to correct for bias, following SNB recommendations was found to extend patients’ OS by an additional 15 to 17 months, a substantial improvement over those who did not follow the recommendations. Although the NCCN guidelines resulted in a positive HR and DRMST and successfully identified treatment heterogeneity, these results were significant only in univariate metrics or in those uncorrected by IPTW. Complex feature interactions often need to be considered in treatment selection rather than relying on fixed guidelines, and our study demonstrated that DL models are well suited to this task, as evidenced by the stronger protective effect of SNB compared to NCCN guidelines.

By analyzing model recommendation behaviors associated with ITE values, artificial intelligence-guided intervention studies offer profound insights into treatment methods based on DL. This research maintains other covariates constant, effectively considering and eliminating confounding factors that might influence treatment recommendations. Compared to traditional methods, the results obtained are not only unaffected by these confounders but are also quantifiable, providing a clear perspective and significant scientific basis for assessing the relationship between baseline characteristics and the relative efficacy of CRT treatments.

For every 1 mm increase in tumor size, it is revealed that CRT results in a slight extension of the time for the mortality rate to reach 50%, indicating that larger tumors may respond more robustly to CRT. This enhanced responsiveness could be attributed to the greater volume of cancer cells in larger tumors, which are typically undergoing rapid division, thus making them more susceptible to the cytotoxic effects of chemotherapy and radiation.[36] Additionally, the presence of distant metastasis and advanced stage disease significantly influences the decision-making process for CRT recommendations, as these conditions often indicate aggressive cancer behavior that may necessitate more intensive treatment regimens.[4] Significantly enhanced efficacy of chemotherapy was also found in tumors located in subsites such as the lip, other parts of the tongue, gums, and sinuses.[37]

Conversely, the prevailing medical consensus advises against the administration of CRT to patients with stage I disease.[4] This recommendation is driven primarily by the potential toxicity associated with chemotherapy, which may outweigh the benefits in the context of early-stage disease, particularly for elderly individuals.[38] Furthermore, the adverse effects of CRT on OS are intensified with advancing age. This trend is likely attributable to diminished physiological resilience in older adults, rendering them more vulnerable to the harsh effects of chemoradiation.[11] Such vulnerability often necessitates a more conservative treatment approach that prioritizes the patient’s quality of life and long-term health over aggressive cancer eradication strategies, which might offer only marginal benefits.[39] Additionally, findings suggest that RT, rather than CRT, should be considered for elderly male HNSCC patients. However, the evidence base supporting this specific practice remains underdeveloped, indicating a significant gap in the research. Further studies are necessary to validate this guideline and better understand the unique responses of different demographic groups to various treatment modalities, thus ensuring optimal treatment protocols.

Effective communication regarding treatment options between clinicians and patients necessitates a visual platform. However, it has been challenging to provide personalized prognostic analyses assuming that patients undergo a specific treatment.[35,40] Most published models currently generate prognostic factors based on patient characteristics, which makes it difficult to avoid biases caused by different treatment modalities.[41] However, SNB demonstrates the potential to address this clinical dilemma and to better share individual outcomes following various treatment option. It enables crucial visualization of expected impacts, aiding essential healthcare decisions. As cost considerations become integral to treatment discussions, SNB could assist users in identifying cost-effective options. Particularly for patients unable to decide for themselves, SNB can support their families in objectively assessing the advantages and disadvantages of each option. These developments forecast an expanding role for DL models in clinical settings. Future enhancements in data accuracy and the integration of additional illnesses are expected to further refine these tools, strengthening the foundation of precision medicine. However, the practical application of the SNB model necessitates prospective, real-world evaluations that demand significant manpower and resources. Despite these challenges, our study can serve as a valuable preliminary tool, offering prior evidence and methodological insights that can effectively guide and inform future research in this area.

5. Limitations

An initial model was constructed in this study that can be used and evaluated in subsequent clinical trials. However, access to crucial variables was restricted by database limitations, including alcohol and tobacco use, sexual preference, previous employment, HPV infection, treatment details, emotional support, marital status, and the age at which therapy commenced. Moreover, the reliance on a single database for both training and testing restricts the generalizability of the findings. Biases may be introduced by this approach due to demographic and geographic variations that might not accurately represent the overall patient cohort. Despite efforts to mitigate biases, the use of retrospective data inherently limits control over unrecorded variables and interventions, which can lead to overlooked biases and inconsistent observation periods.[42] Future research should investigate the model’s effectiveness using methods such as randomized controlled trials, prospective cohort studies, or targeted trial emulation,[43] incorporating a broader set of variables to assess the impact of potential missing values and time-dependent factors through subgroup and sensitivity analyses. Additionally, balancing patient-specific considerations, such as potential complications, against survival benefits is crucial, anticipating that enhancements in database variables will enable more thorough analyses.

Based on the aforementioned considerations, research teams and healthcare institutions are encouraged to include the following in future datasets and registries: lifestyle factors[44] (such as jobs, alcohol and tobacco use, and emotional supports), detailed tumor characteristics[4,44,45] (HPV status, extranodal extension, perineural invasion, vascular invasion, lymphatic invasion and predictive biomarkers), other prognostic factors (such as potential complications and living quality) and treatment details.[46] These data are essential for the development of more accurate and personalized treatment recommendations, thereby enhancing the overall efficacy of patient care in the field of head and neck cancers.

6. Conclusions

To our knowledge, this is the first study to utilize DL to devise nonsurgical treatment plans for elderly HNSCC patients. SNB’s potential for aiding in clinical treatment decisions and providing quantitative treatment recommendations is confirmed. Benefits from CRT were found for elderly HNSCC patients with advanced, metastatic, or certain subsites involved.

Author contributions

Conceptualization: Yang Li, Yanming Zhu, Guangliang Yang.

Data curation: Yang Li, Qinyu Xiao, Qiuyi Lu, Guangliang Yang.

Formal analysis: Yang Li, Xin Wang, Xu Zhang, Haiqi Chen.

Funding acquisition: Yang Li.

Investigation: Yang Li, Qinyu Xiao.

Methodology: Yang Li, Enzhao Zhu, Yanming Zhu, Guangliang Yang.

Project administration: Yang Li, Qinyu Xiao.

Resources: Yang Li, Xin Wang, Jianmeng Dai.

Software: Enzhao Zhu, Xin Wang.

Supervision: Yang Li.

Writing – original draft: Yang Li.

Writing – review & editing: Guangliang Yang, Haiqi Chen.

Supplementary Material

Abbreviations:

BITES Balanced Individual Treatment Effect for Survival data

CI confidence interval

CMHE Cox Mixtures with Heterogeneous Effects

CRN chemoradiotherapy recommended by NCCN

CRT chemoradiotherapy

DL deep learning

DRMST difference in restricted mean survival time

HNSCC head and neck squamous cell carcinoma

HR hazard ratio

IBS Integrated Brier score

INDE interventional natural direct effect

INIE interventional natural indirect effect

IPTW inverse probability treatment weighting

ITE individual treatment effect

NCCN National Comprehensive Cancer Network

OS overall survival

RD risk difference

RT radiotherapy

RTN radiotherapy recommended by NCCN

SNB Self-Normalizing Balanced individual treatment effect for survival data

TaR time at risk

This study analyzed public datasets that can be found here: the Surveillance, Epidemiology, and End Results Program (https://seer.cancer.gov/index.html). The studies involving human participants were approved by the national cancer institution. Written informed consent for participation was not required for this study in accordance with national legislation and institutional requirements. Xinyi Yang had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Li Y, Xiao Q, Chen H, Zhu E, Wang X, Dai J, Zhang X, Lu Q, Zhu Y, Yang G. Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach. Medicine 2024;103:37(e39659).
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