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Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer
BACKGROUND: Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506204/ https://www.ncbi.nlm.nih.gov/pubmed/37723421 http://dx.doi.org/10.1186/s12880-023-01090-7 |
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author | Zheng, Biyu Chen, Qingshuang |
author_facet | Zheng, Biyu Chen, Qingshuang |
author_sort | Zheng, Biyu |
collection | PubMed |
description | BACKGROUND: Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan. OBJECTIVE: Constructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model. METHOD: Patients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM. RESULT: Two hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result. CONCLUSION: The predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients. |
format | Online Article Text |
id | pubmed-10506204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105062042023-09-19 Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer Zheng, Biyu Chen, Qingshuang BMC Med Imaging Research BACKGROUND: Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan. OBJECTIVE: Constructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model. METHOD: Patients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM. RESULT: Two hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result. CONCLUSION: The predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients. BioMed Central 2023-09-18 /pmc/articles/PMC10506204/ /pubmed/37723421 http://dx.doi.org/10.1186/s12880-023-01090-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zheng, Biyu Chen, Qingshuang Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title | Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title_full | Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title_fullStr | Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title_full_unstemmed | Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title_short | Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
title_sort | novel model based on ultrasound predict axillary lymph node metastasis in breast cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506204/ https://www.ncbi.nlm.nih.gov/pubmed/37723421 http://dx.doi.org/10.1186/s12880-023-01090-7 |
work_keys_str_mv | AT zhengbiyu novelmodelbasedonultrasoundpredictaxillarylymphnodemetastasisinbreastcancer AT chenqingshuang novelmodelbasedonultrasoundpredictaxillarylymphnodemetastasisinbreastcancer |