Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma
ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overa...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
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| Online Access: | https://doi.org/10.1002/psp4.13294 |
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| _version_ | 1850038125753008128 |
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| author | Kevin Atsou Anne Auperin Jôel Guigay Sébastien Salas Sebastien Benzekry |
| author_facet | Kevin Atsou Anne Auperin Jôel Guigay Sébastien Salas Sebastien Benzekry |
| author_sort | Kevin Atsou |
| collection | DOAJ |
| description | ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model‐derived TK parameters versus RECIST and assessed the efficacy of nine TK‐OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double‐exponential model. TK parameters from the first line and maintenance (TKL1) or after four cycles (TK4) were used to predict PPS and post‐cycle 4 OS (OS4), combined with 12 baseline parameters. While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST‐based metrics. This model demonstrated unbiased OS4 prediction, suggesting its potential for improving HNSCC treatment evaluation. Trial Registration: ClinicalTrials.gov identifier: NCT02268695. |
| format | Article |
| id | doaj-art-7ac3f6613a66418c8f207ab41f77bbbe |
| institution | DOAJ |
| issn | 2163-8306 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj-art-7ac3f6613a66418c8f207ab41f77bbbe2025-08-20T02:56:39ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062025-03-0114354055010.1002/psp4.13294Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell CarcinomaKevin Atsou0Anne Auperin1Jôel Guigay2Sébastien Salas3Sebastien Benzekry4COMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis – Méditerranée Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105 Marseille FranceBiostatistical and Epidemiological Division Institut Gustave Roussy Villejuif FranceClinical Oncology, Centre Antoine Lacassagne Nice FranceCOMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis – Méditerranée Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105 Marseille FranceCOMPutational Pharmacology and Clinical Oncology Department, Inria Sophia Antipolis – Méditerranée Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105 Marseille FranceABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model‐derived TK parameters versus RECIST and assessed the efficacy of nine TK‐OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double‐exponential model. TK parameters from the first line and maintenance (TKL1) or after four cycles (TK4) were used to predict PPS and post‐cycle 4 OS (OS4), combined with 12 baseline parameters. While ML algorithms underperformed compared to the Cox model for PPS, a random survival forest was superior for OS prediction using TK4 and surpassed RECIST‐based metrics. This model demonstrated unbiased OS4 prediction, suggesting its potential for improving HNSCC treatment evaluation. Trial Registration: ClinicalTrials.gov identifier: NCT02268695.https://doi.org/10.1002/psp4.13294deep learninghead and neck squamous cell carcinomamachine learningsurvival analysistumor kinetics |
| spellingShingle | Kevin Atsou Anne Auperin Jôel Guigay Sébastien Salas Sebastien Benzekry Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma CPT: Pharmacometrics & Systems Pharmacology deep learning head and neck squamous cell carcinoma machine learning survival analysis tumor kinetics |
| title | Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma |
| title_full | Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma |
| title_fullStr | Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma |
| title_full_unstemmed | Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma |
| title_short | Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma |
| title_sort | mechanistic learning for predicting survival outcomes in head and neck squamous cell carcinoma |
| topic | deep learning head and neck squamous cell carcinoma machine learning survival analysis tumor kinetics |
| url | https://doi.org/10.1002/psp4.13294 |
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