Data from Automated Imaging as an Adjunct to Serum and Clinical Biomarkers: A New Validated Prediction Tool for Metastatic Castration-Resistant Prostate Cancer

作者：Michael J. Morris, Jessica Flynn, Binsheng Zhao, Aseem Anand, Karl Sjöstrand, Steven M. Larson, Ashley Marie Regazzi, Gary Borzillo, A. Park, Lawrence H. Schwartz, Glenn Heller · 年份：2026 · DOI：10.1158/1078-0432.c.8547600 · 研究领域：Prostate Cancer Treatment and Research、Prostate Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging

<div>AbstractPurpose:Contemporary prostate cancer prognostic models do not include imaging and generally are based on pretreatment parameters. We sought to develop an externally validated model that used novel quantification of soft-tissue and bone disease, integrated with standard clinical and serum biomarkers, at baseline and up to 6 months of treatment.Experimental Design:Two randomized phase 3 trials, Cougar COU-AA-302 (NCT00887198; for derivation) and Alliance A031201 (NCT01949337; for validation), were used to evaluate the added value of early on-treatment bone imaging and more than 1,000 radiomics features on CT, used in conjunction with clinical and serum biomarkers in first-line metastatic castration-resistant prostate cancer. Predictive accuracy measures were computed to determine whether these early on-treatment biomarkers could reliably sort patients into risk groups that inform overall survival (OS) and whether the patient-specific biomarker risk score could precisely predict their OS time.Results:Imaging improved patient risk stratification but did not improve individual survival predictions. The strongest risk prediction model was developed for patients with bone-only metastases. This model was also the least complex, relying on just 16 risk factors, whereas all other models were high-dimensional, incorporating approximately 1,100 intercept and 1,100 slope features from the early on-treatment biomarker trajec...