Cancer Res Treat.  2022 Jan;54(1):234-244. 10.4143/crt.2020.1221.

Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective

• Wee CW
• Jang BS
• Kim JH
• Jeong CW
• Kwak C
• Kim HH
• Ku JH
• Kim SH
• Cho JY
• Kim SY

• KMID: 2524604
• DOI: http://doi.org/10.4143/crt.2020.1221

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 (cT_MRI, 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 cT_MRI (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 cT_MRI (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 cT_MRI were established and installed on a separate website for public access to guide radiation oncologists.

Keyword

Prostate neoplasms; Radiotherapy; Magnetic resonance imaging; Bayesian network; Extracapsular extension; Seminal vesicle

Figure

• 

  Fig. 1 A preoperative multiparametric 3.0T magnetic resonance imaging using T1-weighted (A), T2-weighted (B), diffusion-weighted (C), and dynamic contrast-enhanced (D) images in a 67-year-old male diagnosed as prostate cancer by biopsy. Lesion with suspected seminal vesicle invasion (cTMRI3b) shows low signal intensity on T2- (B) and diffusion-weighted (C) images with contrast enhancement (D).

• 

  Fig. 2 (A) BN structure to estimate the probability of ECE. Each node demonstrates the associated variable, the discretized state, baseline prevalence, mean, deviation values in the study population. (B) The graph showing the impact of each variable on the probability of ECE. x-axis represents the normalized mean-value of each variable, and y-axis shows the mean probability of ECE. BN, Bayesian network; ECE, extracapsular extension; iPSA, initial prostate-specific antigen; MRI, magnetic resonance imaging.

• 

  Fig. 3 (A) BN structure to estimate the probability of SVI. Each node demonstrates the associated variable, the discretized state, baseline prevalence, mean, deviation values in study population. (B) The graph showing the impact of each variable on the probability of SVI. x-axis represents the normalized mean-value of each variable, and y-axis shows the mean probability of SVI. BN, Bayesian network; iPSA, initial prostate-specific antigen; MRI, magnetic resonance imaging; SVI, seminal vesicle invasion.

• 

  Fig. 4 (A) BN structure to estimate the probability of RM+ve. Each node demonstrates the associated variable, the discretized state, baseline prevalence, mean, deviation values in study population. (B) The graph showing the impact of each variable on the probability of RM+ve. x-axis represents the normalized mean-value of each variable, and y-axis shows the mean probability of RM+ve. BN, Bayesian network; iPSA, initial prostate-specific antigen; MRI, magnetic resonance imaging; RM+ve, positive resection margin.

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  Fig. 5 Comparison between the predictive accuracy of BN model and Roach formula for pathological ECE (A) and SVI (B) according to the DeLong’s comparison method. AUC, area under the curve; BN, Bayesian network; ECE, extracapsular extension; SVI, seminal vesicle invasion.

Reference

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