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KMID : 0939920220540010234
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대한암학회지
2022 Volume.54 No. 1 p.234 ~ p.244
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Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective
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Wee Chan-Woo
Jang Bum-Sup
Kim Jin-Ho
Jeong Chang-Wook
Kwak Cheol
Kim Hyun-Hoe
Ku Ja-Hyeon
Kim Seung-Hyup
Cho Jeong-Yeon
Kim Sang-Youn
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Abstract
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Purpose: This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b).
Materials and Methods: A total of 1,915 who underwent radical prostatectomy between 2006?2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation.
Results: According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p < 0.001), percentage of positive biopsy cores (PPC) (β=0.033, p < 0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p < 0.001), and cTMRI (β=0.259, p < 0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p < 0.001), PPC (β=0.024, p < 0.001), Gleason score (β=0.753, p < 0.001), and cTMRI (β=0.507, p < 0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall area under the receiver operating characteristic curve (AUC)/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74, p=0.060) and SVI (0.88 vs. 0.84, p < 0.001).
Conclusion: Two models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists.
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KEYWORD
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Prostate neoplasms, Radiotherapy, Magnetic resonance imaging, Bayesian network, Extracapsular extension, Seminal vesicle
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