Apparent diffusion coefficient values obtained by unenhanced MRI predicts disease-specific survival in bladder cancer. Issue 10 (October 2018)

• Record Type: Journal Article
• Authors: Sevcenco, S.; Maj-Hes, A.B.; Hruby, S.; Ponhold, L.; Heinz-Peer, G.; Rauchenwald, M.; Marszalek, M.; Klingler, H.C.; Polanec, S.; Baltzer, P.A.T.
• Is Part Of: Clinical radiology. Volume 73:Issue 10(2018)
• Page Start: 881
• Page End: 885
• Publication Date: 2018-10
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2018.05.022
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : Aim: To assess the ability of apparent diffusion coefficient (ADC) measurements obtained by MRI to predict disease-specific survival (DSS) in patients with bladder cancer and compare it with established clinico-pathological prognostic factors. Material and methods: The ethical review board approved this cross-sectional study. Patients with suspected bladder cancer receiving diagnostic 3 T diffusion-weighted imaging (DWI) of the bladder before transurethral resection of the bladder (TUR-B) or radical cystectomy were evaluated prospectively. Two independent radiologists measured ADC values in bladder cancer lesions in regions of interest. Associations between ADC values and pathological features with DSS were tested statistically. A combined model was established using artificial neuronal network (ANN) methodology. Results: A total of 51 patients (median age 69 years, range 41–89 years) were included. Three patients were lost to follow-up, leaving 48 patients for survival analysis. Seven patients died during the 795 months studied. ADC showed significant potential to predict DSS ( p< 0.05). Except for grading, all pathological features as assessed by TUR-B could predict DSS ( p< 0.05, respectively). The combined ANN classifier showed the highest accuracy to predict DSS (0.889, 95% confidence interval: 0.732–1, p= 0.001) compared to all single parameters. ADC was the second important predictor of the ANN. Conclusions: ADC measurements obtained by unenhanced MRI predicts DSS in bladder cancer patients. A combined classifier including ADC and clinico-pathological information showed high accuracy to identify patients at high risk for disease-related death. Highlights: Apparent Diffusion Coefficient (ADC) measurements can predict disease-specific survival in bladder cancer patients before treatment. Muscle invasiveness and vascular invasion were the most accurate clinic-pathological predictors of DSS. An artificial neural network classifier combining clinic-pathological factors and ADC values achieved high predictive accuracy.