Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.
3/5 보강
TL;DR
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.
OpenAlex 토픽 ·
Medical Imaging Techniques and Applications
Cardiac Imaging and Diagnostics
Radiomics and Machine Learning in Medical Imaging
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 cla
- 95% CI 0.765-0.961
APA
Yinzhao Wang, Yiquan Yang, et al. (2026). Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.. European journal of nuclear medicine and molecular imaging, 53(5), 3007-3022. https://doi.org/10.1007/s00259-025-07652-x
MLA
Yinzhao Wang, et al.. "Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.." European journal of nuclear medicine and molecular imaging, vol. 53, no. 5, 2026, pp. 3007-3022.
PMID
41276643 ↗
Abstract 한글 요약
[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.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
같은 제1저자의 인용 많은 논문 (5)
- "I wanna look like the person in that picture": Linking selfies on social media to cosmetic surgery consideration based on the tripartite influence model.
- ZmSKIP enhances drought tolerance by reducing stomatal aperture in maize.
- c.7374_7375insAlu is a French-Canadian founder pathogenic variant associated with predisposition to pancreatic and breast cancer.
- Enhancing Node-RADS for preoperative assessment of cervical lymph node metastases in papillary thyroid carcinoma: validation and modification.
- Aging modulation of the immune system and immunotherapy efficacy in cancer.
🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
- A Phase I Study of Hydroxychloroquine and Suba-Itraconazole in Men with Biochemical Relapse of Prostate Cancer (HITMAN-PC): Dose Escalation Results.
- Self-management of male urinary symptoms: qualitative findings from a primary care trial.
- Clinical and Liquid Biomarkers of 20-Year Prostate Cancer Risk in Men Aged 45 to 70 Years.
- Diagnostic accuracy of Ga-PSMA PET/CT versus multiparametric MRI for preoperative pelvic invasion in the patients with prostate cancer.
- Comprehensive analysis of androgen receptor splice variant target gene expression in prostate cancer.
- Clinical Presentation and Outcomes of Patients Undergoing Surgery for Thyroid Cancer.