Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective
| Autor: | Jeong Yeon Cho, Chang Wook Jeong, Hyun Hoe Kim, Chan Woo Wee, Seung Hyup Kim, Jin Ho Kim, Sang Youn Kim, Ja Hyeon Ku, Cheol Kwak, Bum Sup Jang |
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| Rok vydání: | 2022 |
| Předmět: |
Adult
Male 0301 basic medicine Cancer Research medicine.medical_specialty medicine.medical_treatment Urology Adenocarcinoma Logistic regression Extracapsular extension Genitourinary Cancer 03 medical and health sciences Prostate cancer 0302 clinical medicine Statistical significance Biopsy Humans Medicine Prostate neoplasms Radiation oncologist Aged Retrospective Studies Aged 80 and over Radiotherapy medicine.diagnostic_test business.industry Prostatectomy Confounding Prostatic Neoplasms Bayes Theorem Middle Aged medicine.disease Magnetic Resonance Imaging Seminal vesicle Logistic Models Bayesian network 030104 developmental biology ROC Curve Oncology 030220 oncology & carcinogenesis Radiation Oncology Original Article Prostate neoplasm Neoplasm Grading business |
| Zdroj: | Cancer Research and Treatment : Official Journal of Korean Cancer Association |
| ISSN: | 2005-9256 1598-2998 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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