Multiparametric MRI‐Based Radiomics for Prostate Cancer Screening With PSA in 4–10 ng/mL to Reduce Unnecessary Biopsies

• Published in: Journal of magnetic resonance imaging 2020-06, Vol.51 (6), p.1890-1899
• Main Authors: Qi, Yafei, Zhang, Shuaitong, Wei, Jingwei, Zhang, Gumuyang, Lei, Jing, Yan, Weigang, Xiao, Yu, Yan, Shuang, Xue, Huadan, Feng, Feng, Sun, Hao, Tian, Jie, Jin, Zhengyu
• Format: Article
• Language: English
• Subjects: Antigens; Biopsy; Cancer screening; Continuity (mathematics); Decision making; Decision trees; Early Detection of Cancer; Feature extraction; Field strength; Humans; Levels; Magnetic Resonance Imaging; Male; Medical imaging; Medical screening; Multiparametric Magnetic Resonance Imaging; Patients; Prostate cancer; Prostate-Specific Antigen - analysis; prostate‐specific antigen; Prostatic Neoplasms - diagnostic imaging; Radiomics; Retrospective Studies; Risk analysis; Risk factors; Statistical analysis; Statistical tests
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.27008

Background Whether men with a prostate‐specific antigen (PSA) level of 4–10 ng/mL should be recommended for a biopsy is clinically challenging. Purpose To develop and validate a radiomics model based on multiparametric MRI (mp‐MRI) in patients with PSA levels of 4–10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. Study Type Retrospective. Subjects In all, 199 patients with PSA levels of 4–10 ng/mL. Field Strength/Sequence 3T, T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI. Assessment Lesion regions of interest (ROIs) from T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical‐radiological risk factors. Statistical Tests For continuous variables, variance equality was assessed by Levene's test and Student's t‐test, and Welch's t‐test was used to assess between‐group differences. For categorical variables, Pearson's chi‐square test, Fisher's exact test, or the approximate chi‐square test was used to assess between‐group differences. P < 0.05 was considered statistically significant. Results The combined model incorporating the multi‐imaging fusion model, age, PSA density (PSAD), and the PI‐RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical‐radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical‐radiological model by 18.4%. Data Conclusion The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4–10 ng/mL and might aid in treatment decision‐making and reduce unnecessary biopsies. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890–1899.