Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.
Background: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status Purpose: To evaluate the efficacy of ultrasound radiofrequency (URF) time-ser...
| Publicado en: | Acta Radiologica Vol. 65; no. 10; pp. 1178 - 1186 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Oct2024
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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=180298285&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180298285 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02841851 1XG jtl: Acta Radiologica issn: 02841851 maglogo: Y pubinfo: dt: Oct2024 vid: 65 iid: 10 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 180298285 10.1177/02841851241268463 180298285 ppf: 1178 ppct: 8 formats: tig: atl: Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis. aug: au: Sun, Pengfei Guo, Ruifang Hu, Xiangdong Dekker, Andre Traverso, Alberto Qian, Linxue Wang, Zhixiang affil: Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China sug: ab: Background: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status Purpose: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. Material and Methods: We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. Results: The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. Conclusion: The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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