What question did this study set out to answer?

The study aims to create and validate a multimodal radiomics model using PET/CT and mpMRI for diagnosing prostate cancer.

April 18, 2026Open Access

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

Key Points

The study aims to create and validate a multimodal radiomics model using PET/CT and mpMRI for diagnosing prostate cancer.
Retrospective enrollment of 488 patients suspected of prostate cancer.
Integration of imaging features from ^68 Ga-PSMA PET/CT and mpMRI.
Training of six machine learning classifiers for diagnosis and comparison of prostate VOI delineation methods.
Assessment of model performance using multiple diagnostic metrics.
The radiomics model demonstrated an AUC of 0.91 in internal validation and 0.825 in external validation.
Among analyzed patients, 250 had clinically significant prostate cancer.
Automated VOI segmentation achieved similar accuracy to expert-drawn methods, with AUC differences between 3-8%.

Abstract

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. 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. 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%. 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.

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