A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study

BMC Medical Imaging

Research

Xiaoyu Han¹  Jinwen Hu²  Weiqun Ao³  Xiandi Zhu³  Shuitang Deng³  Guoqun Mao³  Xiuzhen Yao⁴  Shuyuan Tian⁵  Wenjie Xu⁶ 

[1] Department of Pathology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China;Department of Radiology, Putuo People’s Hospital, School of Medicine, Tongji University, Shanghai, China;Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, 310012, Hangzhou, Zhejiang Province, China;Department of Ultrasound, Putuo People’s Hospital, School of Medicine, Tongji University, Shanghai, China;Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, China;Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China;

关键词: Rectal cancer;    Multi-parametric;    Radiomics;    Ki-67;    Magnetic resonance imaging;   

DOI  :  10.1186/s12880-023-01123-1

 received in 2022-11-12, accepted in 2023-10-09,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundTo explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer.MethodsData from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms.ResultsThree models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians.ConclusionThe multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.

【 授权许可】

CC BY   
© The Author(s) 2023

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