Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women
Abstract Background Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on...
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| Format: | Article |
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BMC
2021-03-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-021-00571-x |
| _version_ | 1818337185384890368 |
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| author | Yu Tan Hui Mai Zhiqing Huang Li Zhang Chengwei Li Songxin Wu Huang Huang Wen Tang Yongxi Liu Kuiming Jiang |
| author_facet | Yu Tan Hui Mai Zhiqing Huang Li Zhang Chengwei Li Songxin Wu Huang Huang Wen Tang Yongxi Liu Kuiming Jiang |
| author_sort | Yu Tan |
| collection | DOAJ |
| description | Abstract Background Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. Results The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. Conclusions With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME. |
| first_indexed | 2024-12-13T14:51:12Z |
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| id | doaj.art-27de615ab56b401e9037f982abdc75ef |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-13T14:51:12Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-27de615ab56b401e9037f982abdc75ef2022-12-21T23:41:20ZengBMCBMC Medical Imaging1471-23422021-03-0121111010.1186/s12880-021-00571-xAdditive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal womenYu Tan0Hui Mai1Zhiqing Huang2Li Zhang3Chengwei Li4Songxin Wu5Huang Huang6Wen Tang7Yongxi Liu8Kuiming Jiang9Department of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, The Third Affiliated Hospital of Guangzhou Medical UniversityDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalDepartment of Radiology, Guangdong Women and Children HospitalAbstract Background Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. Results The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. Conclusions With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.https://doi.org/10.1186/s12880-021-00571-xBreastMRINon-mass enhancementTexture analysisAdditive value |
| spellingShingle | Yu Tan Hui Mai Zhiqing Huang Li Zhang Chengwei Li Songxin Wu Huang Huang Wen Tang Yongxi Liu Kuiming Jiang Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women BMC Medical Imaging Breast MRI Non-mass enhancement Texture analysis Additive value |
| title | Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women |
| title_full | Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women |
| title_fullStr | Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women |
| title_full_unstemmed | Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women |
| title_short | Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women |
| title_sort | additive value of texture analysis based on breast mri for distinguishing between benign and malignant non mass enhancement in premenopausal women |
| topic | Breast MRI Non-mass enhancement Texture analysis Additive value |
| url | https://doi.org/10.1186/s12880-021-00571-x |
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