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Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma

BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from m...

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Autores principales: Xu, Aqiao, Chu, Xiufeng, Zhang, Shengjian, Zheng, Jing, Shi, Dabao, Lv, Shasha, Li, Feng, Weng, Xiaobo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364617/
https://www.ncbi.nlm.nih.gov/pubmed/35945526
http://dx.doi.org/10.1186/s12885-022-09967-6
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author Xu, Aqiao
Chu, Xiufeng
Zhang, Shengjian
Zheng, Jing
Shi, Dabao
Lv, Shasha
Li, Feng
Weng, Xiaobo
author_facet Xu, Aqiao
Chu, Xiufeng
Zhang, Shengjian
Zheng, Jing
Shi, Dabao
Lv, Shasha
Li, Feng
Weng, Xiaobo
author_sort Xu, Aqiao
collection PubMed
description BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. METHODS: We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). RESULTS: 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. CONCLUSION: Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09967-6.
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spelling pubmed-93646172022-08-11 Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma Xu, Aqiao Chu, Xiufeng Zhang, Shengjian Zheng, Jing Shi, Dabao Lv, Shasha Li, Feng Weng, Xiaobo BMC Cancer Research BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. METHODS: We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). RESULTS: 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. CONCLUSION: Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09967-6. BioMed Central 2022-08-10 /pmc/articles/PMC9364617/ /pubmed/35945526 http://dx.doi.org/10.1186/s12885-022-09967-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Xu, Aqiao
Chu, Xiufeng
Zhang, Shengjian
Zheng, Jing
Shi, Dabao
Lv, Shasha
Li, Feng
Weng, Xiaobo
Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title_full Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title_fullStr Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title_full_unstemmed Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title_short Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
title_sort development and validation of a clinicoradiomic nomogram to assess the her2 status of patients with invasive ductal carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364617/
https://www.ncbi.nlm.nih.gov/pubmed/35945526
http://dx.doi.org/10.1186/s12885-022-09967-6
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