European journal of nuclear medicine and molecular imaging · 2025
Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.
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Wang Yinzhao, Yang Yiquan, Qi Lin, Chen Minfeng, Hu Shuo, Gao Xiaomei, Tang Yongxiang, Cai Yi
Аннотация
BACKGROUND: Reliable tools for early prediction of treatment response to androgen deprivation therapy (ADT) plus novel androgen receptor pathway inhibitors (ARPIs) in metastatic hormone-sensitive prostate cancer (mHSPC) remain lacking. This study aimed to develop and validate an interpretable machine learning model integrating [⁶⁸Ga]Ga-PSMA PET/CT-derived imaging features to predict response before therapy initiation.
METHODS: In this real-world study, 212 de novo mHSPC patients undergoing [⁶⁸Ga]Ga-PSMA PET/CT were included. Eighteen imaging and eight clinical features were extracted. Three ensemble-based recursive algorithms were applied for feature selection, and six machine learning models were developed. SHAP analysis was used for interpretability. Model outputs were combined with CHAARTED-defined tumor burden for refined risk stratification.
RESULTS: Ten key features were identified, including metastatic total lesion PSMA uptake (m_TL_PSMA), whole-body peak standardized uptake value (w_SUVpeak), and primary lesion mean standardized uptake value (p_SUVmean). CatBoost achieved the best performance (training AUC = 0.908; test AUC = 0.904; Kappa = 0.717). In a temporally independent validation cohort, the model yielded an AUC of 0.875 (95% CI: 0.765-0.961), sensitivity of 0.72, and specificity of 0.89. Combined with CHAARTED classification, the model stratified patients into prognostic groups: responders with low-volume disease had the best metastatic castration-resistant prostate cancer (mCRPC)-free survival, while non-responders with high-volume disease had the worst outcomes.
CONCLUSIONS: The explainable CatBoost-based model integrating quantitative PSMA PET/CT features demonstrated promising accuracy in predicting response to ADT plus ARPIs in mHSPC. When combined with the CHAARTED classification, it facilitated risk stratification. Prospective multicenter validation is required.
METHODS: In this real-world study, 212 de novo mHSPC patients undergoing [⁶⁸Ga]Ga-PSMA PET/CT were included. Eighteen imaging and eight clinical features were extracted. Three ensemble-based recursive algorithms were applied for feature selection, and six machine learning models were developed. SHAP analysis was used for interpretability. Model outputs were combined with CHAARTED-defined tumor burden for refined risk stratification.
RESULTS: Ten key features were identified, including metastatic total lesion PSMA uptake (m_TL_PSMA), whole-body peak standardized uptake value (w_SUVpeak), and primary lesion mean standardized uptake value (p_SUVmean). CatBoost achieved the best performance (training AUC = 0.908; test AUC = 0.904; Kappa = 0.717). In a temporally independent validation cohort, the model yielded an AUC of 0.875 (95% CI: 0.765-0.961), sensitivity of 0.72, and specificity of 0.89. Combined with CHAARTED classification, the model stratified patients into prognostic groups: responders with low-volume disease had the best metastatic castration-resistant prostate cancer (mCRPC)-free survival, while non-responders with high-volume disease had the worst outcomes.
CONCLUSIONS: The explainable CatBoost-based model integrating quantitative PSMA PET/CT features demonstrated promising accuracy in predicting response to ADT plus ARPIs in mHSPC. When combined with the CHAARTED classification, it facilitated risk stratification. Prospective multicenter validation is required.