MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer
Abstract Objectives Distant metastasis remains the main cause of death in breast cancer. Breast cancer risk is strongly influenced by pathogenic mutation.This study was designed to develop a multiple-feature model using clinicopathological and imaging characteristics adding pathogenic mutations asso...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-023-10555-5 |
| _version_ | 1811175833573785600 |
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| author | Zhenzhen Shao Jinpu Yu Yanan Cheng Wenjuan Ma Peifang Liu Hong Lu |
| author_facet | Zhenzhen Shao Jinpu Yu Yanan Cheng Wenjuan Ma Peifang Liu Hong Lu |
| author_sort | Zhenzhen Shao |
| collection | DOAJ |
| description | Abstract Objectives Distant metastasis remains the main cause of death in breast cancer. Breast cancer risk is strongly influenced by pathogenic mutation.This study was designed to develop a multiple-feature model using clinicopathological and imaging characteristics adding pathogenic mutations associated signs to predict recurrence or metastasis in breast cancers in high familial risk women. Methods Genetic testing for breast-related gene mutations was performed in 54 patients with breast cancers. Breast MRI findings were retrospectively evaluated in 64 tumors of the 54 patients. The relationship between pathogenic mutation, clinicopathological and radiologic features was examined. The disease recurrence or metastasis were estimated. Multiple logistic regression analyses were performed to identify independent factors of pathogenic mutation and disease recurrence or metastasis. Based on significant factors from the regression models, a multivariate logistic regression was adopted to establish two models for predicting disease recurrence or metastasis in breast cancer using R software. Results Of the 64 tumors in 54 patients, 17 tumors had pathogenic mutations and 47 tumors had no pathogenic mutations. The clinicopathogenic and imaging features associated with pathogenic mutation included six signs: biologic features (p = 0.000), nuclear grade (p = 0.045), breast density (p = 0.005), MRI lesion type (p = 0.000), internal enhancement pattern (p = 0.004), and spiculated margin (p = 0.049). Necrosis within the tumors was the only feature associated with increased disease recurrence or metastasis (p = 0.006). The developed modelIincluding clinico-pathologic and imaging factors showed good discrimination in predicting disease recurrence or metastasis. Comprehensive model II, which included parts of modelIand pathogenic mutations significantly associated signs, showed significantly more sensitivity and specificity for predicting disease recurrence or metastasis compared to Model I. Conclusions The incorporation of pathogenic mutations associated imaging and clinicopathological parameters significantly improved the sensitivity and specificity in predicting disease recurrence or metastasis. The constructed multi-feature fusion model may guide the implementation of prophylactic treatment for breast cancers at high familial risk women. |
| first_indexed | 2024-04-10T19:42:21Z |
| format | Article |
| id | doaj.art-bcc58d67721d4305bf6e160e28f7669f |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-10T19:42:21Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-bcc58d67721d4305bf6e160e28f7669f2023-01-29T12:14:59ZengBMCBMC Cancer1471-24072023-01-0123111310.1186/s12885-023-10555-5MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancerZhenzhen Shao0Jinpu Yu1Yanan Cheng2Wenjuan Ma3Peifang Liu4Hong Lu5Department of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyCancer Molecular Diagnostics Core, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyCancer Molecular Diagnostics Core, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyDepartment of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyDepartment of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyDepartment of Breast Imaging, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and TherapyAbstract Objectives Distant metastasis remains the main cause of death in breast cancer. Breast cancer risk is strongly influenced by pathogenic mutation.This study was designed to develop a multiple-feature model using clinicopathological and imaging characteristics adding pathogenic mutations associated signs to predict recurrence or metastasis in breast cancers in high familial risk women. Methods Genetic testing for breast-related gene mutations was performed in 54 patients with breast cancers. Breast MRI findings were retrospectively evaluated in 64 tumors of the 54 patients. The relationship between pathogenic mutation, clinicopathological and radiologic features was examined. The disease recurrence or metastasis were estimated. Multiple logistic regression analyses were performed to identify independent factors of pathogenic mutation and disease recurrence or metastasis. Based on significant factors from the regression models, a multivariate logistic regression was adopted to establish two models for predicting disease recurrence or metastasis in breast cancer using R software. Results Of the 64 tumors in 54 patients, 17 tumors had pathogenic mutations and 47 tumors had no pathogenic mutations. The clinicopathogenic and imaging features associated with pathogenic mutation included six signs: biologic features (p = 0.000), nuclear grade (p = 0.045), breast density (p = 0.005), MRI lesion type (p = 0.000), internal enhancement pattern (p = 0.004), and spiculated margin (p = 0.049). Necrosis within the tumors was the only feature associated with increased disease recurrence or metastasis (p = 0.006). The developed modelIincluding clinico-pathologic and imaging factors showed good discrimination in predicting disease recurrence or metastasis. Comprehensive model II, which included parts of modelIand pathogenic mutations significantly associated signs, showed significantly more sensitivity and specificity for predicting disease recurrence or metastasis compared to Model I. Conclusions The incorporation of pathogenic mutations associated imaging and clinicopathological parameters significantly improved the sensitivity and specificity in predicting disease recurrence or metastasis. The constructed multi-feature fusion model may guide the implementation of prophylactic treatment for breast cancers at high familial risk women.https://doi.org/10.1186/s12885-023-10555-5Breast cancerMRI phenotypesBiologic featuresPathogenic mutationDisease recurrence or metastasis |
| spellingShingle | Zhenzhen Shao Jinpu Yu Yanan Cheng Wenjuan Ma Peifang Liu Hong Lu MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer BMC Cancer Breast cancer MRI phenotypes Biologic features Pathogenic mutation Disease recurrence or metastasis |
| title | MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| title_full | MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| title_fullStr | MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| title_full_unstemmed | MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| title_short | MR imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| title_sort | mr imaging phenotypes and features associated with pathogenic mutation to predict recurrence or metastasis in breast cancer |
| topic | Breast cancer MRI phenotypes Biologic features Pathogenic mutation Disease recurrence or metastasis |
| url | https://doi.org/10.1186/s12885-023-10555-5 |
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