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

Content Provider	Springer Nature : BioMed Central
Author	Yao, Xiuzhen Ao, Weiqun Zhu, Xiandi Tian, Shuyuan Han, Xiaoyu Hu, Jinwen Xu, Wenjie Mao, Guoqun Deng, Shuitang
Abstract	Background To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. Methods Data 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. Results Three 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. Conclusion The 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.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-023-01123-1.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-023-01123-1
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2023-10-27
Access Restriction	Open
Subject Keyword	Imaging Radiology Rectal cancer Multi-parametric Radiomics Ki-67 Magnetic resonance imaging
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

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