Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning.
Objective: To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters. Methods: A total of 148 PDAC retrospectively examined patients were classified as...
| Publicado en: | Abdominal Radiology Vol. 49; no. 1; pp. 117 - 131 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2024
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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=174800209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174800209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2024 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174800209 10.1007/s00261-023-04047-0 174800209 ppf: 117 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning. aug: au: Yuan, Zhongyu Shu, Zhenyu Peng, Jiaxuan Wang, Wei Hou, Jie Han, Lu Zheng, Guangying Wei, Yuguo Zhong, Jianguo affil: https://ror.org/008w1vb37 Jinzhou Medical University, Jinzhou, Liaoning Province, China sug: ab: Objective: To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters. Methods: A total of 148 PDAC retrospectively examined patients were classified as metastatic or non-metastatic based on results at 3 months after surgery. The radiomics features of the primary tumor were extracted from T2WI images, followed by dimension reduction. Then, multiple machine learning methods were used to construct models. Independent predictors were also screened using multifactor logistic regression and a nomogram was constructed in combination with the radiomics model. Area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the accuracy and reliability of the nomogram. Results: The diagnostic efficacy of the radiomics model in the training and test set was 0.822 and 0.803, sensitivity was 0.742 and 0.692, and specificity was 0.792 and 0.875, respectively. The diagnostic efficacy of the nomogram in the training and test set was 0.866 and 0.832. Conclusion: A radiomics nomogram based on machine learning improved the accuracy of predicting PCLM and may be useful for early preoperative diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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