Explainable Deep Learning Model for Predicting Overall Survival in Patients Receiving Palliative Radiotherapy for Bone Metastases
TL;DR
An explainable deep learning model was developed and evaluated, which predicted survival during the first year after radiotherapy with discrimination and calibration comparable to those of a Cox proportional hazards model, indicating that this prognostic information can be provided as a simple bedside score rather than requiring artificial intelligence software.
Abstract
Simple Summary Predicting overall survival after palliative radiotherapy for bone metastases plays a critical role in determining appropriate treatment strategies, including adjusting treatment intensity according to individual patient prognosis. Although machine learning-based survival prediction has been investigated in this setting, explainable deep learning models remain underexplored. Prognostic scores used in this setting add up risk points for individual factors, but whether this simple additive form misses prognostic information had not been tested. This study developed and evaluated an explainable deep learning model, which can capture more complex patterns, using data from 472 patients. The model predicted survival during the first year after radiotherapy with discrimination and calibration comparable to those of a Cox proportional hazards model, indicating that this prognostic information can be provided as a simple bedside score rather than requiring artificial intelligence software. The explainability analyses identified poor performance status as by far the strongest predictor, followed by male sex, high-risk primary tumor type, the burden of metastatic disease, and the planned radiation dose; a higher planned dose was the only leading factor associated with lower predicted mortality. These findings support explainable, individualized survival estimation as an aid to decisions on treatment goals and radiation schedules.
