The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis

• Published in: Journal of imaging Vol. 11; no. 8; p. 250
• Main Authors: Bekou, Eleni, Seimenis, Ioannis, Tsochatzis, Athanasios, Tziagkana, Karafyllia, Kelekis, Nikolaos, Deftereos, Savas, Courcoutsakis, Nikolaos, Koukourakis, Michael I., Karavasilis, Efstratios
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.07.2025; MDPI
• Subjects: Algorithms; Artificial intelligence; Biomarkers; Biopsy; biparametric magnetic resonance imaging; Classification; Comparative analysis; Computer-aided medical diagnosis; Data mining; Datasets; Diagnosis; Feature extraction; Feature selection; Machine learning; Magnetic resonance imaging; Medical imaging; Oncology; Physiological aspects; Prostate cancer; prostate cancer diagnosis; prostate-specific antigen; Radiomics; Support vector machines; Technology application; Tumors; Greece; radiomics; prostate cancer; biparametric magnetic resonance imaging; machine learning; prostate cancer diagnosis; prostate-specific antigen
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging11080250

Prostate cancer (PCa) is the most common malignancy in men. Precise grading is crucial for the effective treatment approaches of PCa. Machine learning (ML) applied to biparametric Magnetic Resonance Imaging (bpMRI) radiomics holds promise for improving PCa diagnosis and prognosis. This study investigated the efficiency of seven ML models to diagnose the different PCa grades, changing the input variables. Our studied sample comprised 214 men who underwent bpMRI in different imaging centers. Seven ML algorithms were compared using radiomic features extracted from T2-weighted (T2W) and diffusion-weighted (DWI) MRI, with and without the inclusion of Prostate-Specific Antigen (PSA) values. The performance of the models was evaluated using the receiver operating characteristic curve analysis. The models’ performance was strongly dependent on the input parameters. Radiomic features derived from T2WI and DWI, whether used independently or in combination, demonstrated limited clinical utility, with AUC values ranging from 0.703 to 0.807. However, incorporating the PSA index significantly improved the models’ efficiency, regardless of lesion location or degree of malignancy, resulting in AUC values ranging from 0.784 to 1.00. There is evidence that ML methods, in combination with radiomic analysis, can contribute to solving differential diagnostic problems of prostate cancers. Also, optimization of the analysis method is critical, according to the results of our study.