Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma.
Objectives: 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 tr...
| Publicado en: | Abdominal Radiology Vol. 48; no. 2; pp. 458 - 471 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161717331&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161717331 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161717331 160614249 10.1007/s00261-022-03731-x 161717331 ppf: 458 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. aug: au: Wei, Qiurong Yuan, Wenjing Jia, Ziqi Chen, Jialiang Li, Ling Yan, Zhaoxian Liao, Yuting Mao, Liting Hu, Shaowei Liu, Xian Chen, Weicui affil: Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 510120, Guangzhou, Guangdong Province, China sug: ab: Objectives: 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. Methods: 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. Results: 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. Conclusion: The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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