A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression

• Published in: Cancers Vol. 17; no. 9; p. 1556
• Main Authors: He, Dianning, Zhuang, Haoming, Ma, Ying, Xia, Bixuan, Chatterjee, Aritrick, Fan, Xiaobing, Qi, Shouliang, Qian, Wei, Zhang, Zhe, Liu, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.05.2025; MDPI
• Subjects: Androgens; Artificial intelligence; Biopsy; Cancer; Care and treatment; Castration; Classification; Denervation; Endocrine therapy; Invasiveness; Machine learning; Magnetic resonance imaging; Mathematical models; Medical research; Medical screening; Medicine, Experimental; Patients; Prognosis; Prostate cancer; Radiomics; China; Germany; deep learning; radiomics; prostate cancer; castration-resistant prostate cancer; mpMRI
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers17091556

Objectives: The primary objective of this study was to identify whether patients with prostate cancer (PCa) could progress to denervation-resistant prostate cancer (CRPC) after 12 months of hormone therapy. Methods: A total of 96 PCa patients with baseline clinical data who underwent multiparametric magnetic resonance imaging (MRI) between September 2018 and September 2022 were included in this retrospective study. Patients were classified as progressing or not progressing to CRPC on the basis of their outcome after 12 months of hormone therapy. A dense multimodal fusion artificial intelligence (Dense-MFAI) model was constructed by incorporating a squeeze-and-excitation block and a spatial pyramid pooling layer into a dense convolutional network (DenseNet), as well as integrating the eXtreme Gradient Boosting machine learning algorithm. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic curves, area under the curve (AUC) and confusion matrices were used as classification performance metrics. Results: The Dense-MFAI model demonstrated an accuracy of 94.2%, with an AUC of 0.945, when predicting the progression of patients with PCa to CRPC after 12 months of hormone therapy. The experimental validation demonstrated that combining radiomics feature mapping with baseline clinical characteristics significantly improved the model’s classification performance, confirming the importance of multimodal data. Conclusions: The Dense-MFAI model proposed in this study has the ability to more accurately predict whether a PCa patient could progress to CRPC. This model can assist urologists in developing the most appropriate treatment plan and prognostic measures.