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.
Highlights
Radical prostatectomy (RP) is an effective form of local therapy for prostate cancer (PCa) [1, 2]
Of the 205 prostatectomy confirmed patients, total 409 lesions were detected at histological findings, whereas total 263 lesions were detected at MR findings
Park et al [33, 34] demonstrated that tumor ADCs derived from diffusion weighted imaging (DWI) and new PI-RADS v2 score was better than preoperative prostate-specific antigen (PSA), biopsy Gleason score (GS) and surgical variables in the Figure 3: the comparison of receiver operating curve (ROC) curves among four risk predictive models constructed with different classification methods and input variables. a. with the same MR input variables, the model constructed by support vector machine (SVMMR) has significantly higher area under the ROC curve value (Az = 0.959) than the model of logistic regression (Az = 0.886, p = 0.007). b. using the same SVM analysis, the model combining MR and DA’mico variables has significantly higher Az (0.970) than the model using sole DA’mico variables (Az = 0.859; p < 0.001)
Summary
Radical prostatectomy (RP) is an effective form of local therapy for prostate cancer (PCa) [1, 2]. D’Amico risk stratification scheme [7], and the University of California, San Francisco, Cancer of the Prostate Risk Assessment (CAPRA) score [8], have been developed in the urologic community to predict the probability of BCR within 3-5 or 10 years of treatment. These nomograms have been internationally validated, only a few of them have predicted the probability of 5-year BCR with more than 70% accuracy [9,10,11]. Efforts to improve existing outcome prediction tools in PCa are always encouraged
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