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Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

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机构: [1]Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, Guangdong Province, China [2]Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, Guangdong Province, China [3]Department of Radiology, The Second People’s Hospital of Shaanxi Province, Xi’an 710000, Shaanxi province, China [4]GE Healthcare, Guangzhou 510623, Guangdong Province, China
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关键词: Lymph node metastasis Magnetic resonance imaging Radiomic Rectal adenocarcinoma Amide proton transferweighted imaging

摘要:
Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis.The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively.The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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基金编号: Grant No. 82202259

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出版当年[2022]版:
大类 | 3 区 医学
小类 | 3 区 核医学
最新[2025]版:
大类 | 3 区 医学
小类 | 4 区 核医学
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出版当年[2021]版:
Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

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第一作者机构: [1]Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, Guangdong Province, China
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