title: An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification.
abstract: Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Zhang
authors.affiliation: Center for Medical Device Evaluation, CFDA, Beijing, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
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.
authors.forename: Mei-Ling
authors.initials: ML
authors.lastname: Bao
authors.affiliation: Department of Pathology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
authors.forename: Hai
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
authors.forename: Xiao-Ning
authors.initials: XN
authors.lastname: Wang
authors.affiliation: Department of Urology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Tao
authors.affiliation: Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
authors.forename: Hai-Bin
authors.initials: HB
authors.lastname: Shi
mesh_terms: D000368:Aged; D006801:Humans; D016015:Logistic Models; D008297:Male; D049451:Nomograms; D011237:Predictive Value of Tests; D011379:Prognosis; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000601:surgery; D060388:Support Vector Machine; D016896:Treatment Outcome
keywords: MRI; biochemical recurrence; prostate cancer; radical prostatectomy; support vector machine