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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

AI-BLADE toolbox: AI-powered BLADdEr multiparametric MRI analysis for clinical application.

Muhammad Awais¹, Ramesh Paudyal¹, Oguz Akin²

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.

BJR Artificial Intelligence

|May 1, 2026

Summary

This summary is machine-generated.

We developed AI-BLADE, a user-friendly toolbox for bladder multiparametric MRI analysis. This tool extracts quantitative imaging biomarkers to improve bladder cancer diagnosis and patient outcomes.

Keywords:

bladder cancer deep features analysis model-based analysis multiparametric MRI

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Growing need for user-friendly, bladder-specific AI tools for quantitative imaging biomarkers (QIBs) from multiparametric MRI (mpMRI).
Current tools lack reliability for clinical applications in bladder cancer (BCa).

Purpose of the Study:

To develop and validate AI-BLADE (AI-powered BLADdEr multiparametric MRI Analysis for Clinical Application), a novel toolbox for BCa mpMRI analysis.
To extract reliable AI-QIBs for enhanced clinical decision-making.

Main Methods:

AI-BLADE integrates Deep Feature Analysis (MRI-DFA) and Data-Driven Model-Based Analysis (MRI-MBA) toolkits.
DFA classified BCa histology subtypes (n=104) using T2-weighted images.
MBA derived mpMRI QIBs (ADC, Ktrans) from 34 BCa patients.

Main Results:

The VGG19 model with a decision tree classifier achieved an AUC of 0.79 for BCa histology classification.
Mean ADC values were 1.22 × 10⁻³ mm²/s and mean Ktrans values were 0.27 min⁻¹.
AI-BLADE demonstrated strong performance in classifying BCa subtypes and deriving key physiological metrics.

Conclusions:

AI-BLADE (v1.0) is a flexible, user-friendly software for mpMRI analysis in BCa oncology.
The toolbox shows potential to enhance diagnostic accuracy and improve patient outcomes.
This novel AI toolbox facilitates AI-QIB-based clinical decision-making for BCa patients.