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Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study
PURPOSE: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). METHODS: Two hundred and thirty-five female patients with BC were enr...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490879/ https://www.ncbi.nlm.nih.gov/pubmed/36158700 http://dx.doi.org/10.3389/fonc.2022.922185 |
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author | Fang, Caiyun Zhang, Juntao Li, Jizhen Shang, Hui Li, Kejian Jiao, Tianyu Yin, Di Li, Fuyan Cui, Yi Zeng, Qingshi |
author_facet | Fang, Caiyun Zhang, Juntao Li, Jizhen Shang, Hui Li, Kejian Jiao, Tianyu Yin, Di Li, Fuyan Cui, Yi Zeng, Qingshi |
author_sort | Fang, Caiyun |
collection | PubMed |
description | PURPOSE: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). METHODS: Two hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS: A total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice. CONCLUSIONS: The nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC. |
format | Online Article Text |
id | pubmed-9490879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94908792022-09-22 Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study Fang, Caiyun Zhang, Juntao Li, Jizhen Shang, Hui Li, Kejian Jiao, Tianyu Yin, Di Li, Fuyan Cui, Yi Zeng, Qingshi Front Oncol Oncology PURPOSE: To develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC). METHODS: Two hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS: A total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice. CONCLUSIONS: The nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC. Frontiers Media S.A. 2022-09-07 /pmc/articles/PMC9490879/ /pubmed/36158700 http://dx.doi.org/10.3389/fonc.2022.922185 Text en Copyright © 2022 Fang, Zhang, Li, Shang, Li, Jiao, Yin, Li, Cui and Zeng https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Fang, Caiyun Zhang, Juntao Li, Jizhen Shang, Hui Li, Kejian Jiao, Tianyu Yin, Di Li, Fuyan Cui, Yi Zeng, Qingshi Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title | Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title_full | Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title_fullStr | Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title_full_unstemmed | Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title_short | Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study |
title_sort | clinical-radiomics nomogram for identifying her2 status in patients with breast cancer: a multicenter study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490879/ https://www.ncbi.nlm.nih.gov/pubmed/36158700 http://dx.doi.org/10.3389/fonc.2022.922185 |
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