Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer
ObjectiveOur aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.MethodWe retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions f...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1099650/full |
| _version_ | 1811164284212740096 |
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| author | Li Xie Zhen Liu Chong Pei Xiao Liu Ya-yun Cui Nian-an He Lei Hu |
| author_facet | Li Xie Zhen Liu Chong Pei Xiao Liu Ya-yun Cui Nian-an He Lei Hu |
| author_sort | Li Xie |
| collection | DOAJ |
| description | ObjectiveOur aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.MethodWe retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions from 1116 female patients (mean age ± standard deviation, 45.40 ± 9.65 years). The lesions were divided into three subgroups based on the maximum diameter (MD): ≤15 mm; >15 mm and ≤25 mm; >25 mm. We recorded lesion stiffness (SWV1) and 5-point average stiffness of the peritumoral tissue (SWV5). The CNN models were built based on the segmentation of different widths of peritumoral tissue (0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm) and internal SWE image of the lesions. All single-parameter CNN models, dual-modal CNN models, and quantitative SWE parameters in the training cohort (971 lesions) and the validation cohort (300 lesions) were assessed by receiver operating characteristic (ROC) curve.ResultsThe US + 1.0 mm SWE model achieved the highest area under the ROC curve (AUC) in the subgroup of lesions with MD ≤15 mm in both the training (0.94) and the validation cohorts (0.91). In the subgroups with MD between15 and 25 mm and above 25 mm, the US + 2.0 mm SWE model achieved the highest AUCs in both the training cohort (0.96 and 0.95, respectively) and the validation cohort (0.93 and 0.91, respectively).ConclusionThe dual-modal CNN models based on the combination of US and peritumoral region SWE images allow accurate prediction of breast cancer. |
| first_indexed | 2024-04-10T15:19:04Z |
| format | Article |
| id | doaj.art-226fa40fade44ab6b00f08281b63104d |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T15:19:04Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-226fa40fade44ab6b00f08281b63104d2023-02-14T16:15:39ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-02-011310.3389/fonc.2023.10996501099650Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancerLi Xie0Zhen Liu1Chong Pei2Xiao Liu3Ya-yun Cui4Nian-an He5Lei Hu6Department of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Computing, Hebin Intelligent Robots Co., LTD., Hefei, ChinaDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Hefei City, The Third Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaDepartment of Ultrasound, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, ChinaObjectiveOur aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.MethodWe retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions from 1116 female patients (mean age ± standard deviation, 45.40 ± 9.65 years). The lesions were divided into three subgroups based on the maximum diameter (MD): ≤15 mm; >15 mm and ≤25 mm; >25 mm. We recorded lesion stiffness (SWV1) and 5-point average stiffness of the peritumoral tissue (SWV5). The CNN models were built based on the segmentation of different widths of peritumoral tissue (0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm) and internal SWE image of the lesions. All single-parameter CNN models, dual-modal CNN models, and quantitative SWE parameters in the training cohort (971 lesions) and the validation cohort (300 lesions) were assessed by receiver operating characteristic (ROC) curve.ResultsThe US + 1.0 mm SWE model achieved the highest area under the ROC curve (AUC) in the subgroup of lesions with MD ≤15 mm in both the training (0.94) and the validation cohorts (0.91). In the subgroups with MD between15 and 25 mm and above 25 mm, the US + 2.0 mm SWE model achieved the highest AUCs in both the training cohort (0.96 and 0.95, respectively) and the validation cohort (0.93 and 0.91, respectively).ConclusionThe dual-modal CNN models based on the combination of US and peritumoral region SWE images allow accurate prediction of breast cancer.https://www.frontiersin.org/articles/10.3389/fonc.2023.1099650/fullconvolutional neural networks (CNN)shear-wave elastography (SWE)peritumoral stiffnesssegmentationbreast cancer |
| spellingShingle | Li Xie Zhen Liu Chong Pei Xiao Liu Ya-yun Cui Nian-an He Lei Hu Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer Frontiers in Oncology convolutional neural networks (CNN) shear-wave elastography (SWE) peritumoral stiffness segmentation breast cancer |
| title | Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer |
| title_full | Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer |
| title_fullStr | Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer |
| title_full_unstemmed | Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer |
| title_short | Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer |
| title_sort | convolutional neural network based on automatic segmentation of peritumoral shear wave elastography images for predicting breast cancer |
| topic | convolutional neural networks (CNN) shear-wave elastography (SWE) peritumoral stiffness segmentation breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1099650/full |
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