Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma
Abstract 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 extracte...
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-022-09967-6 |
| _version_ | 1828437858084454400 |
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| author | Aqiao Xu Xiufeng Chu Shengjian Zhang Jing Zheng Dabao Shi Shasha Lv Feng Li Xiaobo Weng |
| author_facet | Aqiao Xu Xiufeng Chu Shengjian Zhang Jing Zheng Dabao Shi Shasha Lv Feng Li Xiaobo Weng |
| author_sort | Aqiao Xu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-10T19:54:20Z |
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| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-10T19:54:20Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
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| series | BMC Cancer |
| spelling | doaj.art-ab027fed47274fbbb8511cee5d246f172022-12-22T01:35:43ZengBMCBMC Cancer1471-24072022-08-0122111310.1186/s12885-022-09967-6Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinomaAqiao Xu0Xiufeng Chu1Shengjian Zhang2Jing Zheng3Dabao Shi4Shasha Lv5Feng Li6Xiaobo Weng7Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Department of Surgical, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Department of Radiology, Fudan University Shanghai Cancer CenterDepartment of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., LtdDepartment of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital)Abstract 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.https://doi.org/10.1186/s12885-022-09967-6Breast carcinomaMultiparametric magnetic resonance imagingNomogramsHER2Radiomics |
| spellingShingle | Aqiao Xu Xiufeng Chu Shengjian Zhang Jing Zheng Dabao Shi Shasha Lv Feng Li Xiaobo Weng Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma BMC Cancer Breast carcinoma Multiparametric magnetic resonance imaging Nomograms HER2 Radiomics |
| 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 | Breast carcinoma Multiparametric magnetic resonance imaging Nomograms HER2 Radiomics |
| url | https://doi.org/10.1186/s12885-022-09967-6 |
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