Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases.

Autor:	Cilla S; Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy., Rossi R; Palliative Care Unit, IRCCS Istituto Romagnolo Studio Tumori 'Dino Amadori', Meldola, Italy., Habberstad R; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.; Department of Oncology, St Olavs University Hospital, Trondheim, Norway., Klepstad P; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.; Department of Anesthesiology and Intensive Care Medicine, St Olavs University Hospital, Trondheim, Norway., Dall'Agata M; Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) 'Dino Amadori', Meldola, Italy., Kaasa S; Department of Oncology, Oslo University Hospital, Oslo, Norway., Valenti V; Palliative Care Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) 'Dino Amadori', Meldola, Italy., Donati CM; Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy., Maltoni M; Medical Oncology Unit, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum-University of Bologna, Bologna, Italy., Morganti AG; Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy.; Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2024 Jun; Vol. 8, pp. e2400027.
DOI:	10.1200/CCI.24.00027
Abstrakt:	Purpose: The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis. Materials and Methods: Data collected in the multicentric PRAIS trial were extracted for 574 eligible adults diagnosed with metastatic cancer. The primary end point was the overall survival (OS) at 1 year (1-year OS) after the start of RT. Candidate covariate predictors consisted of 13 clinical and tumor-related pre-RT patient characteristics, seven dosimetric and treatment-related variables, and 45 pre-RT laboratory variables. ML models were developed and internally validated using the Python package. The effectiveness of each model was evaluated in terms of discrimination. A Shapley Additive Explanations (SHAP) explainability analysis to infer the global and local feature importance and to understand the reasons for correct and misclassified predictions was performed. Results: The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively. The SHAP technique revealed that higher chance of 1-year survival is associated with low values of interleukin-8, higher values of hemoglobin and lymphocyte count, and the nonuse of steroids. Conclusion: An explainable ML approach can provide a reliable prediction of 1-year survival after RT in patients with advanced cancer. The implementation of SHAP analysis provides an intelligible explanation of individualized risk prediction, enabling oncologists to identify the best strategy for patient stratification and treatment selection.
Databáze:	MEDLINE
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