Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma
ObjectivesTriple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Met...
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| Format: | Article |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.916988/full |
| _version_ | 1811183694745960448 |
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| author | Qing-cong Kong Wen-jie Tang Si-yi Chen Wen-ke Hu Yue Hu Yun-shi Liang Qiong-qiong Zhang Zi-xuan Cheng Di Huang Jing Yang Yuan Guo |
| author_facet | Qing-cong Kong Wen-jie Tang Si-yi Chen Wen-ke Hu Yue Hu Yun-shi Liang Qiong-qiong Zhang Zi-xuan Cheng Di Huang Jing Yang Yuan Guo |
| author_sort | Qing-cong Kong |
| collection | DOAJ |
| description | ObjectivesTriple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC).MethodsWe evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method.ResultsOf the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035).ConclusionsIn conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC. |
| first_indexed | 2024-04-11T09:51:13Z |
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| id | doaj.art-dfe259ce414d4958a7f07668d4bee34a |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T09:51:13Z |
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| series | Frontiers in Oncology |
| spelling | doaj.art-dfe259ce414d4958a7f07668d4bee34a2022-12-22T04:30:48ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-09-011210.3389/fonc.2022.916988916988Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinomaQing-cong Kong0Wen-jie Tang1Si-yi Chen2Wen-ke Hu3Yue Hu4Yun-shi Liang5Qiong-qiong Zhang6Zi-xuan Cheng7Di Huang8Jing Yang9Yuan Guo10Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaBreast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Breast Surgery, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, ChinaObjectivesTriple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC).MethodsWe evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method.ResultsOf the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035).ConclusionsIn conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.https://www.frontiersin.org/articles/10.3389/fonc.2022.916988/fullnomogramstriple negative breast cancermagnetic resonance imagingmetaplastic breast carcinomahistological subtypes |
| spellingShingle | Qing-cong Kong Wen-jie Tang Si-yi Chen Wen-ke Hu Yue Hu Yun-shi Liang Qiong-qiong Zhang Zi-xuan Cheng Di Huang Jing Yang Yuan Guo Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma Frontiers in Oncology nomograms triple negative breast cancer magnetic resonance imaging metaplastic breast carcinoma histological subtypes |
| title | Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma |
| title_full | Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma |
| title_fullStr | Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma |
| title_full_unstemmed | Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma |
| title_short | Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma |
| title_sort | nomogram for the prediction of triple negative breast cancer histological heterogeneity based on multiparameter mri features a preliminary study including metaplastic carcinoma and non metaplastic carcinoma |
| topic | nomograms triple negative breast cancer magnetic resonance imaging metaplastic breast carcinoma histological subtypes |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.916988/full |
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