Diagnosing breast cancer subtypes using MRI radiomics and machine learning: A systematic review DOI
Zhenyue Wang, Shuanzeng Wei

Journal of Radiation Research and Applied Sciences, Journal Year: 2024, Volume and Issue: 18(1), P. 101260 - 101260

Published: Dec. 26, 2024

Language: Английский

Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning DOI Creative Commons
Bin Hu,

Yanjun Xu,

Huiling Gong

et al.

Academic Radiology, Journal Year: 2024, Volume and Issue: 32(2), P. 651 - 663

Published: Sept. 10, 2024

Language: Английский

Citations

4
Prediction of clinical outcome for high-intensity focused ultrasound ablation of adenomyosis based on non-enhanced MRI radiomics DOI Creative Commons
Ziyi Liu,

Ziyan Liu,

Xiaofeng Wan

et al.

International Journal of Hyperthermia, Journal Year: 2025, Volume and Issue: 42(1)

Published: Feb. 23, 2025

Objectives The study aimed to develop a non-enhanced MRI-based radiomics model for the preoperative prediction of efficacy adenomyosis after high-intensity focused ultrasound (HIFU) treatment.

Language: Английский

Citations

0
Multimodal pathomics feature integration for enhanced predictive performance in gastric cancer pathology through radiomic and CNN-derived features DOI Creative Commons

Lan Yan,

Peng Zhao,

Kangpeng Yan

et al.

Arabian Journal of Chemistry, Journal Year: 2025, Volume and Issue: 0, P. 1 - 17

Published: March 12, 2025

Language: Английский

Citations

0
Prediction of molecular subtypes of endometrial cancer patients on the basis of intratumoral and peritumoral radiomic features from multiparametric MR images DOI
Jing Zhou, Xuan Yu,

Yànli Cūi

et al.

European Journal of Radiology, Journal Year: 2025, Volume and Issue: unknown, P. 112110 - 112110

Published: April 1, 2025

Language: Английский

Citations

0
Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models DOI Creative Commons

Minfang Wang,

Wanjun Chen,

Ruiping Ren

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: May 14, 2025

Background Peritumoral characteristics demonstrate significant predictive value for neoadjuvant chemotherapy (NAC) response in breast cancer (BC) through tumor-stromal interactions. Radiomics analysis of peritumoral regions has shown robust capability predicting treatment outcomes; however, the optimal thickness maximizing accuracy remains undefined. Objective To establish a clinically implementable framework early identification NAC non-responders standardized prediction modeling. This study aims to determine by training and systematically comparing artificial intelligence (AI)-driven radiomics models across multiple zones using Automated Breast Volume Scanning (ABVS). Methods A total 402 BC patients who received were retrospectively analyzed. Pre-treatment ABVS images processed extract radiomic features from five interest (ROIs): intratumoral region (R0) four consecutive (R2-R8) extending outward at 2-mm intervals. The cohort was divided into testing cohorts. ROI-specific TabNet developed data. Comparative performed comprehensive performance evaluation, including discrimination, calibration, clinical utility assessment, classification metrics, identify zone. best-performing model ranked importance, with subsequent ablation studies validating contribution high-ranking features. Results Among population, 138 (34.3%) classified as non-responders. Model evaluation demonstrated progressively improved R0 R6, area under ROC curves increasing 0.681 0.845. R6 0.810 precision 0.765. combined integrating enhanced capability, achieving 0.909, 0.841, recall 0.902. Feature importance identified textural heterogeneity volumetric most influential variables, top derived predominantly 6-mm region. Conclusion zone response, AI-driven intratumoral-peritumoral superior performance. ABVS-based approach enables potential non-responders, facilitating timely therapeutic modifications.

Language: Английский

Citations

0
Development of a Nomogram-Integrated Model Incorporating Intra-tumoral and Peri-tumoral Ultrasound Radiomics Alongside Clinical Parameters for the Prediction of Histological Grading in Invasive Breast Cancer DOI
Wen Wan, Kai Zhu, Z X Ran

et al.

Ultrasound in Medicine & Biology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

1
Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model DOI Creative Commons
Fuyu Guo, Shiwei Sun,

Xiaoqian Deng

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 13, 2024

To explore the value of combined radiomics and deep learning models using different machine algorithms based on mammography (MG) magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance developing scientifically individualized treatment plans, assessing prognosis, planning preoperative interventions.

Language: Английский

Citations

0
Diagnosing breast cancer subtypes using MRI radiomics and machine learning: A systematic review DOI
Zhenyue Wang, Shuanzeng Wei

Journal of Radiation Research and Applied Sciences, Journal Year: 2024, Volume and Issue: 18(1), P. 101260 - 101260

Published: Dec. 26, 2024

Language: Английский

Citations

0