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Using support vector machine analysis to assess PartinMR: A new prediction model for organ‐confined prostate cancer
Author(s) -
Wang Jing,
Wu ChenJiang,
Bao MeiLing,
Zhang Jing,
Shi HaiBin,
Zhang YuDong
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25961
Subject(s) - prostate cancer , prostatectomy , medicine , receiver operating characteristic , nomogram , confidence interval , stage (stratigraphy) , prostate specific antigen , nuclear medicine , oncology , cancer , paleontology , biology
Background Partin tables represent the most widely used predictive tool for prostate cancer stage at prostatectomy but with potential limitations. Purpose 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. Study Type Retrospective. Population In all, 541 patients with biopsy‐confirmed prostate cancer underwent mp‐MRI. Field Strength T 2 ‐weighted, diffusion‐weighted imaging with a 3.0T MR scanner. Assessment 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. Statistical Tests 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. Results 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). Data Conclusion 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. Level of Evidence : 3 Technical Efficacy : Stage 2 J. MAGN. RESON. IMAGING 2018;48:499–506.