title: Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.
abstract: OBJECTIVE
  To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).
  
  
  METHODS
  This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis.
  
  
  RESULTS
  For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]).
  
  
  CONCLUSIONS
  Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa.
  
  
  CONCLUSIONS
  • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.
authors.affiliation: Center for Medical Device Evaluation, CFDA, Beijing, China, 100044.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009.
authors.forename: Chen-Jiang
authors.initials: CJ
authors.lastname: Wu
authors.affiliation: Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 210009.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Bao
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009.
authors.forename: Xiao-Ning
authors.initials: XN
authors.lastname: Wang
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009. njmu_zyd@163.com.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D012372:ROC Curve; D011873:Radiology Information Systems / Q000592:standards*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Machine learning; Multi-parametric MRI; Prostate Imaging Reporting and Data System v2; Prostate cancer; Support vector machine