Can Habitat-Based MRI Radiomics Distinguish Between T2 and T3 Stages in Rectal Cancer? DOI

Weiqun Ao,

Sikai Wu, Guoqun Mao

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer DOI

Yueyan Wang,

Bo Xie, Kai Wang

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
Development and Validation of MRI Radiomics Model for Predicting Perineural Invasion in Rectal Cancer DOI Creative Commons
Zhengyu Cao,

Tiejun Yang,

Wanfeng Gong

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Abstract Background This study aims to explore the application of multiparametric MRI (mp-MRI) based radiomics in evaluating perineural invasion (PNI) status rectal cancer. Methods A retrospective analysis was conducted on clinical and data from 423 cancer patients confirmed by surgical pathology across two centers. total 343 Center 1 were split into a training set an internal validation (in-vad) 8:2 ratio, while 80 2 served as independent external (ex-vad) set. Univariate multivariate analyses performed features construct model. Radiomic extracted using Pyradiomics software, selected reduced mRMR LASSO methods combined model integrating subsequently built, nomogram developed. Results Among all patients, 131 cases (31.0%) PNI-positive. Multivariate identified mrT (OR = 1.038, P < 0.001) mrN predictors PNI, forming After radiomic feature selection, 30 used build The area under curve (AUC) values for training, in-vad, ex-vad sets 0.719, 0.631, 0.760, respectively. AUC 0.841, 0.815, 0.916, those 0.899, 0.826, 0.914. Delong test demonstrated that both models outperformed datasets, with no statistically significant difference between models. Conclusions mp-MRI effectively predicts PNI cancer, providing non-invasive accurate method preoperative evaluation.

Language: Английский

Citations

0
Harnessing Multi-Omics: Integrating Radiomics and Pathomics for Predicting Microsatellite Instability in Rectal Cancer DOI
Abdul Sammad, Zhongxiang Ding

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Attention mechanism-based multi-parametric MRI ensemble model for predicting tumor budding grade in rectal cancer patients DOI

Jianye Jia,

Yue Jai Kang, Jiahao Wang

et al.

Abdominal Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Can Habitat-Based MRI Radiomics Distinguish Between T2 and T3 Stages in Rectal Cancer? DOI

Weiqun Ao,

Sikai Wu, Guoqun Mao

et al.

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0