What question did this study set out to answer?

The study aims to develop a user-friendly AI toolbox for analyzing bladder-specific multiparametric MRI data.

January 24, 2026Open Access

AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI Analysis for Clinical Application

Key Points

The study aims to develop a user-friendly AI toolbox for analyzing bladder-specific multiparametric MRI data.
Developed AI-BLADE toolbox with Deep Feature Analysis and Data-Driven Model-Based Analysis functionalities.
Tested on two patient cohorts: 104 patients' T2-weighted images and 34 patients for tumor physiology metrics.
Used AI models, including VGG19, to classify bladder cancer histology subtypes and interrogate tumor physiology.
VGG19 model achieved the highest ROC area under the curve of 0.79 in classifying bladder cancer histology subtypes.
Mean apparent diffusion coefficient (ADC) value of 1.22x10-3 (mm2/s) and volume transfer constant (Ktrans) of 0.27 (min-1) were derived, reflecting tumor physiology.

Abstract

Abstract Objectives There is a growing need to develop user-friendly, bladder-specific image analysis tools that can produce reliable artificial intelligence (AI)-quantitative imaging biomarkers (QIBs) derived from multiparametric (mp)MRI data for clinical applications. To address it, we developed an AI-powered BLADdEr multiparametric MRI Analysis for Clinical Application (AI-BLADE, current release v1.0) toolbox designed for extracting mpMRI-derived quantitative metrics. Methods AI-BLADE is an advanced tool for bladder-specific mpMRI data analysis with two core functionalities: (i) Deep Feature Analysis (MRI-DFA toolkit) and (ii) Data-Driven Model-Based Analysis (MRI-MBA toolkit). AI-BLADE offers customizable options and serves as a one-stop shop solution for bladder cancer (BCa) clinical applications. The models within DFA and MBA were tested separately on two patient cohorts. DFA was used to classify BCa histology subtypes (n = 104) with T2-weighted images, while MBA was used to interrogate tumor physiology by deriving mpMRI QIBs, including apparent diffusion coefficient (ADC), and volume transfer constant (Ktrans) obtained from 34 BCa patients. Results Out of the 17 AI models tested, the VGG19 model with a decision tree classifier and no feature selection for the fully connected layer 7 achieved the highest area under the curve of the ROC of 0.79 in classifying BCa histology subtypes, demonstrating the strongest performance. The mean ADC and Ktrans values were 1.22x10-3 (mm2/s) and 0.27 (min-1), respectively, reflecting underlying tumor physiology. Conclusion The AI-BLADE (v1.0), a flexible and user-friendly software toolbox for analyzing mpMRI data, shows strong potential for application in BCa oncology, offering capabilities that can enhance diagnostic accuracy and support improved patient outcomes. Advances in knowledge This is the first study to design, develop, and implement a novel bladder-specific AI toolbox for analyzing mpMRI data. AI-BLADE enables an advanced image analysis workflow, facilitating AI-QIB-based clinical decision-making for patients with BCa.

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