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  • [Cancer Res Treat.] Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective

    서울대병원 / 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

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    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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