MRI‐Based Breast Cancer Classification and Localization by Multiparametric Feature Extraction and Combination Using Deep Learning

Background Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. Purpose To implement a DL method for breast cancer classification and detection using feature extraction...

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Vydáno v:Journal of magnetic resonance imaging Ročník 59; číslo 1; s. 148 - 161
Hlavní autoři: Cong, Chao, Li, Xiaoguang, Zhang, Chunlai, Zhang, Jing, Sun, Kaixiang, Liu, Lianluyi, Ambale‐Venkatesh, Bharath, Chen, Xiao, Wang, Yi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.01.2024
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Témata:
Artificial neural networks
Breast cancer
breast MRI
Classification
computer‐aided detection
Contrast agents
Contrast media
Deep learning
Feature extraction
Females
Field strength
Histopathology
Lesions
Localization
Machine learning
Magnetic resonance imaging
Medical imaging
multiparametric MRI
Neural networks
Population studies
Sensitivity
Statistical analysis
Statistical tests
multiparametric MRI
breast MRI
deep learning
breast cancer
computer-aided detection
ISSN:1053-1807, 1522-2586, 1522-2586
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Shrnutí:Background Deep learning (DL) have been reported feasible in breast MRI. However, the effectiveness of DL method in mpMRI combinations for breast cancer detection has not been well investigated. Purpose To implement a DL method for breast cancer classification and detection using feature extraction and combination from multiple sequences. Study Type Retrospective. Population A total of 569 local cases as internal cohort (50.2 ± 11.2 years; 100% female), divided among training (218), validation (73) and testing (278); 125 cases from a public dataset as the external cohort (53.6 ± 11.5 years; 100% female). Field Strength/Sequence T1‐weighted imaging and dynamic contrast‐enhanced MRI (DCE‐MRI) with gradient echo sequences, T2‐weighted imaging (T2WI) with spin‐echo sequences, diffusion‐weighted imaging with single‐shot echo‐planar sequence and at 1.5‐T. Assessment A convolutional neural network and long short‐term memory cascaded network was implemented for lesion classification with histopathology as the ground truth for malignant and benign categories and contralateral breasts as healthy category in internal/external cohorts. BI‐RADS categories were assessed by three independent radiologists as comparison, and class activation map was employed for lesion localization in internal cohort. The classification and localization performances were assessed with DCE‐MRI and non‐DCE sequences, respectively. Statistical Tests Sensitivity, specificity, area under the curve (AUC), DeLong test, and Cohen's kappa for lesion classification. Sensitivity and mean squared error for localization. A P‐value <0.05 was considered statistically significant. Results With the optimized mpMRI combinations, the lesion classification achieved an AUC = 0.98/0.91, sensitivity = 0.96/0.83 in the internal/external cohorts, respectively. Without DCE‐MRI, the DL‐based method was superior to radiologists' readings (AUC 0.96 vs. 0.90). The lesion localization achieved sensitivities of 0.97/0.93 with DCE‐MRI/T2WI alone, respectively. Data Conclusion The DL method achieved high accuracy for lesion detection in the internal/external cohorts. The classification performance with a contrast agent‐free combination is comparable to DCE‐MRI alone and the radiologists' reading in AUC and sensitivity. Evidence Level 3. Technical Efficacy Stage 2.
Bibliografie:Chao Cong and Xiaoguang Li contributed equally to this work.
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ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.28713