글로벌 연구동향
방사선종양학
- 2022년 03월호
[Cancer Res Treat.] Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective서울대병원 / 위찬우, 장범섭, 김진호*
- 출처
- Cancer Res Treat.
- 등재일
- 2022 Jan
- 저널이슈번호
- 54(1):234-244. doi: 10.4143/crt.2020.1221. Epub 2021 May 17.
- 내용
Abstract
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.
Affiliations
Chan Woo Wee 1 2 , Bum-Sup Jang 3 , Jin Ho Kim 2 4 5 , Chang Wook Jeong 6 , Cheol Kwak 6 , Hyun Hoe Kim 6 , Ja Hyeon Ku 6 , Seung Hyup Kim 7 , Jeong Yeon Cho 7 , Sang Youn Kim 7
1 Department of Radiation Oncology, SMG-SNU Boramae Medical Center, Seoul, Korea.
2 Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Korea.
3 Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, Korea.
4 Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
5 Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.
6 Department of Urology, Seoul National University College of Medicine, Seoul, Korea.
7 Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- 키워드
- Bayesian network; Extracapsular extension; Magnetic resonance imaging; Prostate neoplasms; Radiotherapy; Seminal vesicle.
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