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- Title
Using support vector machine analysis to assess PartinMR: A new prediction model for organ-confined prostate cancer.
- Authors
Wang, Jing; Wu, Chen‐Jiang; Bao, Mei‐Ling; Zhang, Jing; Shi, Hai‐Bin; Zhang, Yu‐Dong; Wu, Chen-Jiang; Bao, Mei-Ling; Shi, Hai-Bin; Zhang, Yu-Dong
- Abstract
<bold>Background: </bold>Partin tables represent the most widely used predictive tool for prostate cancer stage at prostatectomy but with potential limitations.<bold>Purpose: </bold>To develop a new PartinMR model for organ-confined prostate cancer (OCPCA) by incorporating Partin table and mp-MRI with a support vector machine (SVM) analysis.<bold>Study Type: </bold>Retrospective.<bold>Population: </bold>In all, 541 patients with biopsy-confirmed prostate cancer underwent mp-MRI.<bold>Field Strength: </bold>T2 -weighted, diffusion-weighted imaging with a 3.0T MR scanner.<bold>Assessment: </bold>Candidate predictors included age, prostate-specific antigen, clinical stage, biopsy Gleason score (GS), and mp-MRI findings, ie, tumor location, Prostate Imaging and Reporting and Data System (PI-RADS) score, diameter (D-max), and 6-point MR stage. The PartinMR model with combination of a Partin table and mp-MRI findings was developed using SVM and 5-fold crossvalidation analysis.<bold>Statistical Tests: </bold>The predicted ability of the PartinMR model was compared with a standard Partin and a modified Partin table (mPartin) which used for mp-MRI staging. Statistical tests were made by area under receiver operating characteristic curve (AUC), adjusted proportional hazard ratio (HR), and a cost-effective benefit analysis.<bold>Results: </bold>The rate of OCPCA at prostatectomy was 46.4% (251/541). Using MR staging, mPartin table (AUC, 0.814, 95% confidence interval [CI]: 0.779-0.846, P = 0.001) is appreciably better than the Partin table (AUC, 0.730, 95% CI: 0.690-0.767). Contrarily, adding all MR variables, the PartinMR model (AUC, 0.891, 95% CI: 0.884-0.899, P < 0.001) outperformed any other scheme, with 79.3% sensitivity, 75.7% specificity, 79% positive predictive value, and 76.0% negative predictive value for OCPCA. MR stage represented the most influential predictor of extracapsular extension (HR, 2.77, 95% CI: 1.54-3.33), followed by D-max (2.01, 95% CI: 1.31-2.68), biopsy GS (1.64, 95% CI: 1.35-2.12), and PI-RADS score (1.21, 95% CI: 1.01-1.98).<bold>Data Conclusion: </bold>The new PartinMR model is superior to the conventional Partin table for OCPCA. Clinical implications of mp-MRI for prostate cancer stage must be confirmed in further trials.<bold>Level Of Evidence: </bold>3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:499-506.
- Publication
Journal of Magnetic Resonance Imaging, 2018, Vol 48, Issue 2, p499
- ISSN
1053-1807
- Publication type
journal article
- DOI
10.1002/jmri.25961