Development and validation of a preoperative MRI habitat radiomics model to predict variant histology in bladder cancer

Motivation: Bladder cancer (BCa) patients with variant histology (VH) exhibit more aggressive tumor characteristics, resulting in poor prognosis and resistance to neoadjuvant therapy. Accurate assessment of VH is challenging due to biological complexity and limited biopsy samples. Goal(s): To identify VH in BCa and predict patient prognosis. Approach: An MRI-based fusion model that combines habitat radiomics, clinicopathologic variables, and conventional radiomics was developed, using data from 764 bladder cancer patients across four institutions. Results: The proposed model demonstrated high predictive accuracy (AUC 0.895-0.974) across multiple cohorts, with low-risk patients showing better disease-free survival and higher pathological complete response rates. Impact: The proposed VHRiS model represents a promising and robust tool forto identifying variant histologyVH, offering providing valuable insights into patient prognosis within the microtumoral environment and suggesting indicating potential benefit for precision clinical decision-making guidance in patients with bladder cancer.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2812
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Development and validation of a preoperative MRI habitat radiomics model to predict variant histology in bladder cancer

Qian Cai, Ling‐Min Kong, Keyi Zhang, Huanjun Wang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Development and validation of a preoperative MRI habitat radiomics model to predict variant histology in bladder cancer

Qian Cai, Ling‐Min Kong, Keyi Zhang, Huanjun Wang, Yan Guo, Yanjin Qin, Xueying Zhao
article en

Abstract

Motivation: Bladder cancer (BCa) patients with variant histology (VH) exhibit more aggressive tumor characteristics, resulting in poor prognosis and resistance to neoadjuvant therapy. Accurate assessment of VH is challenging due to biological complexity and limited biopsy samples. Goal(s): To identify VH in BCa and predict patient prognosis. Approach: An MRI-based fusion model that combines habitat radiomics, clinicopathologic variables, and conventional radiomics was developed, using data from 764 bladder cancer patients across four institutions. Results: The proposed model demonstrated high predictive accuracy (AUC 0.895-0.974) across multiple cohorts, with low-risk patients showing better disease-free survival and higher pathological complete response rates. Impact: The proposed VHRiS model represents a promising and robust tool forto identifying variant histologyVH, offering providing valuable insights into patient prognosis within the microtumoral environment and suggesting indicating potential benefit for precision clinical decision-making guidance in patients with bladder cancer.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
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