A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

Zhang, Xin; Liu, Guihong; Peng, Xingchen

doi:10.3390/jcm12155015

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…In a study by Xin Zhang, Guihong Liu and Xingchen Peng [19], we addressed the scarcity of integrated survival prediction tools for head and neck non-squamous cell carcinoma (HNnSCC), which is less common compared to squamous cell carcinoma. Leveraging data from 4458 HNnSCC patients obtained from the SEER database, we developed a novel prediction model for overall survival (OS) and disease-specific survival (DSS) at 3 and 5 years.…”

Section: Literature Survey 1) Introductionmentioning

confidence: 99%

Cancer Risk Identification using Machine Learning

Sondur

2024

IJSREM

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In this comprehensive study, we explore the SEER (Surveillance, Epidemiology, and End Result) dataset to meticulously craft a predictive model aimed at understanding patient survivability, focusing keenly on outcomes like patient survival diagnosis and treatments. Employing a good approach involving rigorous data cleaning, intricate feature extraction, and detailed correlation analysis, we identify the dominant attributes that significantly influence patient outcomes. Our methodology integrates classical machine learning models, including decision trees and random forests, with modern techniques such as neural networks. This mix of methodologies allows us to accurately predict patient survivability. Furthermore, our study delves into the intricate relationships between attributes, models, and algorithms, aiming to identify the dominant factors that influence the outcomes. Our groundbreaking findings underscore the enormous potential of integrating these dominant attributes, paving the way for the creation of exceptionally robust predictive models. These models will substantially enhance medical decision-making processes and, in turn, elevate the overall quality of patient care. Keywords - SEER dataset, patient survivability, machine learning, decision trees, random forests, neural networks, data cleaning, feature extraction, clinical data, medical decision-making.

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Section: Literature Survey 1) Introductionmentioning

confidence: 99%

Cancer Risk Identification using Machine Learning

Sondur

2024

IJSREM

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“…13 Zhang et al established a random forest model using machine learning to accurately predict the risk in patients with non-squamous cell carcinoma of the head and neck. 14 By evaluating three distinct ML algorithms, Fan et al ultimately formulated a CoxBoost model with the best performance for prognosticating survival in patients with spinal and pelvic Ewing's sarcoma. 15 Therefore, our study aims to construct six models for survival analysis and evaluate their performance using diverse operational methods, with the objective of identifying an accurate predictive model that can effectively guide the selection of clinical diagnosis and treatment strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. established a random forest model using machine learning to accurately predict the risk in patients with non‐squamous cell carcinoma of the head and neck 14 . By evaluating three distinct ML algorithms, Fan et al.…”

Section: Introductionmentioning

confidence: 99%

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on theSEERdatabase and external validation in China

Wang,

Li,

Zeng

et al. 2024

Cancer Medicine

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ObjectiveWe have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.MethodsWe collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component‐wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C‐index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox–Snell residual plot. We also used time‐dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model‐agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model.ResultsThe final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS.ConclusionsThe ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

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Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami,

Azimian Zavareh,

Watson

et al. 2024

PLoS ONE

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Background This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. Methods A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70–0.79 vs. 0.66–0.76, and for all sub-sites including oral cavity (0.73–0.89 vs. 0.69–0.77) and larynx (0.71–0.85 vs. 0.57–0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75–0.97, with an F1-score of 0.65–0.89 for HNC; AUROC of 0.61–0.91 and F1-score of 0.58–0.86 for the oral cavity; and AUROC of 0.76–0.97 and F1-score of 0.63–0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. Conclusions ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

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A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

Cited by 4 publications

References 37 publications

Cancer Risk Identification using Machine Learning

Cancer Risk Identification using Machine Learning

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on theSEERdatabase and external validation in China

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

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