Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.

Journal: BMC gastroenterology

Published Date: Aug 5, 2024

Abstract

BACKGROUND: This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer.

Authors

Lifang Fan

School of Medical Imageology, Wannan Medical College, Wuhu, 241002, Anhui, China.
Huazhang Wu

Anhui Province Key Laboratory of Cancer Translational Medicine, Bengbu Medical University, 2600 Donghai Avenue, Bengbu, Anhui, 233030, China.
Yimin Wu

Department of Ultrasound, The Second People's Hospital, WuHu Hospital, East China Normal University, Wuhu, Anhui, 241001, China.
Shujian Wu

Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui, China.
Jinsong Zhao

School of Medical Imageology, Wannan Medical College, Wuhu, 241002, Anhui, China. zhaojinsong@wnmc.edu.cn.
Xiangming Zhu

Anhui Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China.

Keywords

Adult Aged Decision Trees Deep Learning Female Humans Logistic Models Magnetic Resonance Imaging Male Middle Aged Neoplasm Staging Preoperative Period Radiomics Rectal Neoplasms Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39103772)

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3.

Abstract

Authors

Keywords

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