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Related Experiment Video

Updated: Sep 9, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior-Data

Bai-Chuan Liu¹, Xiao-Hui Ding², Hong-Hao Xu¹

¹Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.

Journal of Magnetic Resonance Imaging : JMRI

|September 5, 2025

Summary

This study developed an interpretable radiomics model using Tabular Prior-data Fitted Network (TabPFN) for prostate cancer extraprostatic extension (EPE) detection on MRI. The model showed high performance, potentially improving clinical assessments.

Keywords:

extraprostatic extension foundation model interpretability magnetic resonance imaging prostate cancer radiomics tabular prior‐data fitted network

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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

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Published on: January 8, 2018

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Accurate assessment of extraprostatic extension (EPE) in prostate cancer (PCa) using MRI is challenging due to limitations in accuracy and interobserver agreement.
Developing robust AI tools is crucial for improving diagnostic performance in PCa staging.

Purpose of the Study:

To develop an interpretable Tabular Prior-data Fitted Network (TabPFN)-based radiomics model for evaluating EPE on MRI.
To explore the integration of this radiomics model with radiologists' assessments to enhance diagnostic accuracy.

Main Methods:

A retrospective study involving 513 patients undergoing radical prostatectomy.
Radiomics features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps, with a TabRadiomics model developed using TabPFN.
Comparison with baseline machine learning models and simulation of AI-modified grading algorithms incorporating radiologist input.

Main Results:

The TabRadiomics model demonstrated comparable performance to other machine learning models, achieving AUCs of 0.806 (internal) and 0.842 (external).
AI-modified algorithms showed improved accuracy compared to a less experienced reader, with potential to reduce radiologist interpretation burden.
No significant difference was observed between AI-modified algorithms and readers on the external test set.

Conclusions:

The developed TabRadiomics model exhibits high performance for EPE assessment in prostate cancer.
This AI-driven approach holds promise for improving the clinical evaluation and management of PCa.