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Background <p>Multiparametric MRI (mpMRI) and ^68&#xa0;Ga-PSMA PET/CT are widely used for prostate cancer (PCa) diagnosis but remain limited by false positives and modest specificity, particularly in distinguishing benign prostate diseases (BPDs) and clinically significant PCa (csPCa). Existing studies often rely on small, single-center cohorts with limited generalizability. This study aimed to develop and externally validate a multimodal radiomics model integrating PET/CT and mpMRI for automated PCa diagnosis, and to evaluate the impact of prostate VOI delineation strategies.</p> Methods <p>A total of 488 patients with suspected PCa who underwent both ^68&#xa0;Ga-PSMA PET/CT and mpMRI (T2 and DWI) followed by biopsy were retrospectively enrolled from two centers (366 for model development and ten-fold internal validation; 41 for external validation cohort 1; 81 for external validation cohort 2). Radiomics features were extracted from both modalities, and six classical machine learning classifiers (LR, SVM, Random Forest, Extra Trees, XGBoost, LightGBM) were trained for three tasks: (1) csPCa diagnosis, (2) overall PCa detection, and (3) comparison between expert-drawn and deep learning generated prostate VOIs. Model performance was assessed using AUC, sensitivity, specificity, accuracy, PPV, and NPV.</p> Results <p>Among 407 patients, 137 had BPD, 25 had clinically insignificant PCa, and 250 had csPCa. The multimodal PET/mpMRI radiomics model achieved the best performance with LightGBM (AUC = 0.91 internally; 0.825 externally). Automatically segmented VOIs achieved comparable diagnostic accuracy to expert annotations, with AUC differences within 3–8%.</p> Conclusions <p>The proposed multimodal PET/CT and mpMRI ML-based model enables accurate risk stratification for prostate cancer, with strong external generalizability. Automated prostate segmentation provides comparable diagnostic performance to expert manual delineation, facilitating clinical scalability.</p> Graphical Abstract <p></p>

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Non-invasive diagnosis strategy integrating PSMA PET/CT and mpMRI for patients with suspected prostate cancer: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Yuhang Wang,
  • Yongxiang Tang,
  • Jian Chen,
  • Haoran Sha,
  • Lin Qi,
  • Shuo Hu,
  • Minfeng Chen,
  • Di Gu,
  • Shouzhen Chen,
  • Yi Cai

摘要

Background

Multiparametric MRI (mpMRI) and ^68 Ga-PSMA PET/CT are widely used for prostate cancer (PCa) diagnosis but remain limited by false positives and modest specificity, particularly in distinguishing benign prostate diseases (BPDs) and clinically significant PCa (csPCa). Existing studies often rely on small, single-center cohorts with limited generalizability. This study aimed to develop and externally validate a multimodal radiomics model integrating PET/CT and mpMRI for automated PCa diagnosis, and to evaluate the impact of prostate VOI delineation strategies.

Methods

A total of 488 patients with suspected PCa who underwent both ^68 Ga-PSMA PET/CT and mpMRI (T2 and DWI) followed by biopsy were retrospectively enrolled from two centers (366 for model development and ten-fold internal validation; 41 for external validation cohort 1; 81 for external validation cohort 2). Radiomics features were extracted from both modalities, and six classical machine learning classifiers (LR, SVM, Random Forest, Extra Trees, XGBoost, LightGBM) were trained for three tasks: (1) csPCa diagnosis, (2) overall PCa detection, and (3) comparison between expert-drawn and deep learning generated prostate VOIs. Model performance was assessed using AUC, sensitivity, specificity, accuracy, PPV, and NPV.

Results

Among 407 patients, 137 had BPD, 25 had clinically insignificant PCa, and 250 had csPCa. The multimodal PET/mpMRI radiomics model achieved the best performance with LightGBM (AUC = 0.91 internally; 0.825 externally). Automatically segmented VOIs achieved comparable diagnostic accuracy to expert annotations, with AUC differences within 3–8%.

Conclusions

The proposed multimodal PET/CT and mpMRI ML-based model enables accurate risk stratification for prostate cancer, with strong external generalizability. Automated prostate segmentation provides comparable diagnostic performance to expert manual delineation, facilitating clinical scalability.

Graphical Abstract