Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis
ObjectiveTo investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively.Methods460 patients with 466 pathology-confirmed BCs w...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1219071/full |
| _version_ | 1797522042055032832 |
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| author | Shengsheng Lai Fangrong Liang Fangrong Liang Wanli Zhang Wanli Zhang Yue Zhao Jiamin Li Jiamin Li Yandong Zhao Yandong Zhao Yongzhou Xu Wenshuang Ding Jie Zhan Xin Zhen Ruimeng Yang Ruimeng Yang |
| author_facet | Shengsheng Lai Fangrong Liang Fangrong Liang Wanli Zhang Wanli Zhang Yue Zhao Jiamin Li Jiamin Li Yandong Zhao Yandong Zhao Yongzhou Xu Wenshuang Ding Jie Zhan Xin Zhen Ruimeng Yang Ruimeng Yang |
| author_sort | Shengsheng Lai |
| collection | DOAJ |
| description | ObjectiveTo investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively.Methods460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist’s work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison.ResultsDuring the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-).ConclusionThe RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists’ diagnosis for preoperative identification of molecular receptors of BC. |
| first_indexed | 2024-03-10T08:23:56Z |
| format | Article |
| id | doaj.art-f01c40ec8df041b7a18cf329c6ae9d53 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-10T08:23:56Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-f01c40ec8df041b7a18cf329c6ae9d532023-11-22T09:45:50ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-11-011310.3389/fonc.2023.12190711219071Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosisShengsheng Lai0Fangrong Liang1Fangrong Liang2Wanli Zhang3Wanli Zhang4Yue Zhao5Jiamin Li6Jiamin Li7Yandong Zhao8Yandong Zhao9Yongzhou Xu10Wenshuang Ding11Jie Zhan12Xin Zhen13Ruimeng Yang14Ruimeng Yang15School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong, ChinaDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Clinical & Technique Support, Philips Healthcare, Guangzhou, Guangdong, ChinaDepartment of Pathology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaDepartment of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, ChinaSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, ChinaDepartment of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, ChinaDepartment of Radiology, Guangzhou First People’s Hospital, Guangzhou, Guangdong, ChinaObjectiveTo investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively.Methods460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist’s work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison.ResultsDuring the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-).ConclusionThe RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists’ diagnosis for preoperative identification of molecular receptors of BC.https://www.frontiersin.org/articles/10.3389/fonc.2023.1219071/fullbreast cancermagnetic resonance imagingmolecular receptorradiomicsclassification |
| spellingShingle | Shengsheng Lai Fangrong Liang Fangrong Liang Wanli Zhang Wanli Zhang Yue Zhao Jiamin Li Jiamin Li Yandong Zhao Yandong Zhao Yongzhou Xu Wenshuang Ding Jie Zhan Xin Zhen Ruimeng Yang Ruimeng Yang Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis Frontiers in Oncology breast cancer magnetic resonance imaging molecular receptor radiomics classification |
| title | Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis |
| title_full | Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis |
| title_fullStr | Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis |
| title_full_unstemmed | Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis |
| title_short | Evaluation of molecular receptors status in breast cancer using an mpMRI-based feature fusion radiomics model: mimicking radiologists’ diagnosis |
| title_sort | evaluation of molecular receptors status in breast cancer using an mpmri based feature fusion radiomics model mimicking radiologists diagnosis |
| topic | breast cancer magnetic resonance imaging molecular receptor radiomics classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1219071/full |
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