A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer.
Background: It has been reported that patients with early breast cancer with 1–2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. Purpose: To develop the potential of machine learning based on multipa...
| Publicado en: | Acta Radiologica Vol. 65; no. 2; pp. 185 - 195 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Feb2024
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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=175790718&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175790718 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02841851 1XG jtl: Acta Radiologica issn: 02841851 maglogo: Y pubinfo: dt: Feb2024 vid: 65 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 175790718 174308309 10.1177/02841851231215464 175790718 ppf: 185 ppct: 10 formats: tig: atl: A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer. aug: au: Yu, Haitong Li, Qin Xie, Fucai Wu, Shasha Chen, Yongsheng Huang, Chuansheng Xu, Yonglin Niu, Qingliang affil: 372527Medical Imaging Department, Weifang Medical University, Weifang, Shandong, PR China sug: ab: Background: It has been reported that patients with early breast cancer with 1–2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. Purpose: To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. Material and Methods: This retrospective study included 144 patients with 1–2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning–based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. Results: Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. Conclusion: The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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