Body composition parameters were associated with response to abiraterone acetate and prognosis in patients with metastatic castration‐resistant prostate cancer

Abstract

Objective To investigate the predictive value of body composition parameters for biochemical response to abiraterone acetate (AA) in metastatic castration‐resistant prostate cancer (mCRPC) patients with prior chemohormonal therapy. Methods We retrospectively evaluated the clinicopathologic information of 132 mCRPC cases receiving AA treatment after chemohormonal therapy at hormone‐sensitive stage from July 2018 to June 2021. All patients were divided into AA responders and non‐responders according to the biochemical response to AA (prostate‐specific antigen (PSA) reduction ≥50% than pretreatment). Multivariate Logistic analysis was used to determine the independent predictors and develop predictive model of biochemical response to AA. Cox regression analysis was utilized to investigate the prognostic factors for time to biochemical progression (TTBP), radiological progression‐free survival (rPFS), failure‐free survival (FFS), and overall survival (OS) after AA treatment. Results There were 57 AA responders and 75 AA non‐responders. Periprostatic fat area/prostate area (PPFA/PA) was decreased and skeletal muscle index (SMI) was increased in AA responders compared with AA non‐responders. Multivariable logistic analysis demonstrated that ADT duration ≥12 months, bone metastasis only, high SMI and low PPFA/PA were independent predictors of biochemical response to AA treatment. The FFS, TTBP, rPFS, and OS of patients with lower SMI or higher PPFA/PA was decreased compared with that of patients with higher SMI or lower PPFA/PA, respectively. Combining SMI, PPFA/PA, ADT duration and metastatic sites performed well in differentiating AA responders from non‐responders. Conclusions High SMI and low PPFA/PA could predict biochemical response to AA treatment and preferable prognosis in mCRPC patients with prior chemohormonal therapy at hormone‐sensitive stage.

Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance

“…Although previous studies have explored predictive factors for the occurrence time of CRPC, a unified predictive factor has not yet been established. [28][29][30] For example, for patients receiving intermittent endocrine therapy, due to PSA fluctuations, especially for prostate cancer patients undergoing radical surgery or radiation therapy, the longer the interval between PSA rises, the longer it will take for the patient to develop CRPC.…”