Scholarlink

Background and objective <p>Genomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging.</p> Methods <p>A retrospective cohort of 14 mPCa patients who underwent [<sup>18</sup> F]PSMA-1007 PET/CT was analysed. Prostate and metastatic lesions were segmented, and radiomics features were extracted. Somatic genomic alterations were obtained from formalin-fixed paraffin-embedded tissue samples using FoundationOne CDx testing. Six ML algorithms - Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, Neural Networks, Random Forest, and Boosting - were trained using a 5-times repeated pipeline with 80/20 train/test split, LASSO feature selection, and 5-fold cross-validation. Model performance was assessed using accuracy, AUC, sensitivity, specificity, precision, and F-score.</p> Key findings <p>Fourteen patients with mPCa were included, and 46 lesions were analysed. Genomic alterations included mutations in TP53, TMPRSS2, PTEN, BRCA1/2, ATM, and others. Owing to data limitations, mutations other than TP53, TMPRSS2, and PTEN were grouped into a composite “OTHER” category. The best-performing clinical-radiomics ML models achieved AUCs of 91.11% (TP53), 84.44% (TMPRSS2), 80.00% (PTEN), and 77.78% (OTHER). Selected feature stability was consistent across repeated runs.</p> Conclusions and clinical implications <p>Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

查看全文
  • Anna Scavuzzo,
  • Giovanni Pasini,
  • Osvaldo G. Perez,
  • Miguel A. Jimenez Rios,
  • Marina Arenas Hernández,
  • Roberto Pedrero-Piedras,
  • Maria Delia Pérez Montiel,
  • Nora Sobrevilla Moreno,
  • Giorgio Russo,
  • Alessandro Stefano

摘要

Background and objective

Genomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging.

Methods

A retrospective cohort of 14 mPCa patients who underwent [18 F]PSMA-1007 PET/CT was analysed. Prostate and metastatic lesions were segmented, and radiomics features were extracted. Somatic genomic alterations were obtained from formalin-fixed paraffin-embedded tissue samples using FoundationOne CDx testing. Six ML algorithms - Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, Neural Networks, Random Forest, and Boosting - were trained using a 5-times repeated pipeline with 80/20 train/test split, LASSO feature selection, and 5-fold cross-validation. Model performance was assessed using accuracy, AUC, sensitivity, specificity, precision, and F-score.

Key findings

Fourteen patients with mPCa were included, and 46 lesions were analysed. Genomic alterations included mutations in TP53, TMPRSS2, PTEN, BRCA1/2, ATM, and others. Owing to data limitations, mutations other than TP53, TMPRSS2, and PTEN were grouped into a composite “OTHER” category. The best-performing clinical-radiomics ML models achieved AUCs of 91.11% (TP53), 84.44% (TMPRSS2), 80.00% (PTEN), and 77.78% (OTHER). Selected feature stability was consistent across repeated runs.

Conclusions and clinical implications

Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.