Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

IF 3.3 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yi Dai, Chun Lian, Zhuo Zhang, Jing Gao, Fan Lin, Ziyin Li, Qi Wang, Tongpeng Chu, Dilinuer Aishanjiang, Meiying Chen, Ximing Wang, Guanxun Cheng, Rong Huang, Jianjun Dong, Haicheng Zhang, Ning Mao
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引用次数: 0

Abstract

Background: Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.

Purpose: This study aims to develop and validate a deep learning system using dynamic contrast-enhanced MRI (DCE-MRI) for automated tumor segmentation and classification of HER2-zero, HER2-low, and HER2-positive statuses.

Study type: Retrospective.

Population: One thousand two hundred ninety-four breast cancer patients from three centers who underwent DCE-MRI before surgery were included in the study (52 ± 11 years, 811/204/279 for training/internal testing/external testing).

Field strength/sequence: 3 T scanners, using T1-weighted 3D fast spoiled gradient-echo sequence, T1-weighted 3D enhanced fast gradient-echo sequence and T1-weighted turbo field echo sequence.

Assessment: An automated model segmented tumors utilizing DCE-MRI data, followed by a deep learning models (ResNetGN) trained to classify HER2 statuses. Three models were developed to distinguish HER2-zero, HER2-low, and HER2-positive from their respective non-HER2 categories.

Statistical tests: Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of the model. Evaluation of the model performances for HER2 statuses involved receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC), accuracy, sensitivity, and specificity. The P-values <0.05 were considered statistically significant.

Results: The automatic segmentation network achieved DSC values of 0.85 to 0.90 compared to the manual segmentation across different sets. The deep learning models using ResNetGN achieved AUCs of 0.782, 0.776, and 0.768 in differentiating HER2-zero from others in the training, internal test, and external test sets, respectively. Similarly, AUCs of 0.820, 0.813, and 0.787 were achieved for HER2-low vs. others, and 0.792, 0.745, and 0.781 for HER2-positive vs. others, respectively.

Data conclusion: The proposed DCE-MRI-based deep learning system may have the potential to preoperatively distinct HER2 expressions of breast cancers with therapeutic implications.

Evidence level: 4 TECHNICAL EFFICACY: Stage 3.

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来源期刊
CiteScore
9.70
自引率
6.80%
发文量
494
审稿时长
2 months
期刊介绍: The Journal of Magnetic Resonance Imaging (JMRI) is an international journal devoted to the timely publication of basic and clinical research, educational and review articles, and other information related to the diagnostic applications of magnetic resonance.
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