Abstract
ObjectiveThis study aimed to explore the potential of magnetic resonance imaging (MRI) radiomics-based machine learning to improve assessment and diagnosis of contralateral Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions in women with primary breast cancer.Materials and MethodsA total of 178 contralateral BI-RADS 4 lesions (97 malignant and 81 benign) collected from 178 breast cancer patients were involved in our retrospective dataset. T1 + C and T2 weighted images were used for radiomics analysis. These lesions were randomly assigned to the training (n = 124) dataset and an independent testing dataset (n = 54). A three-dimensional semi-automatic segmentation method was performed to segment lesions depicted on T2 and T1 + C images, 1,046 radiomic features were extracted from each segmented region, and a least absolute shrinkage and operator feature selection method reduced feature dimensionality. Three support vector machine (SVM) classifiers were trained to build classification models based on the T2, T1 + C, and fusion image features, respectively. The diagnostic performance of each model was evaluated and tested using the independent testing dataset. The area under the receiver operating characteristic curve (AUC) was used as a performance metric.ResultsThe T1+C image feature-based model and T2 image feature-based model yielded AUCs of 0.71 ± 0.07 and 0.69 ± 0.07 respectively, and the difference between them was not significant (P > 0.05). After fusing T1 + C and T2 imaging features, the proposed model’s AUC significantly improved to 0.77 ± 0.06 (P < 0.001). The fusion model yielded an accuracy of 74.1%, which was higher than that of the T1 + C (66.7%) and T2 (59.3%) image feature-based models.ConclusionThe MRI radiomics-based machine learning model is a feasible method to assess contralateral BI-RADS 4 lesions. T2 and T1 + C image features provide complementary information in discriminating benign and malignant contralateral BI-RADS 4 lesions.
Highlights
Breast magnetic resonance imagery (MRI) demonstrates a high sensitivity for contralateral occult malignancies on mammography or ultrasonography
A total of 24,588 consecutive pre-treatment breast dynamic MRI examinations performed between January 2016 and December 2018 were retrospectively reviewed by our imaging data system The inclusion criteria were as follows: (a) primary breast cancer was detected by self-examination, clinical palpation, or imaging examination; (b) pre-treatment breast MRI revealed a contralateral BI-RADS 4 lesion, for which the histopathological subtype was confirmed by surgery or biopsy; (c) no history of breast cancer
Patients underwent breast MRI examination for pretreatment evaluation (n = 92), problem solving for an equivocal mammogram or ultrasound finding (n = 73), high-risk screening (n = 5), clinical symptoms with negative conventional imaging (n = 5), and axillary metastasis looking for a primary breast cancer (n=3)
Summary
Breast magnetic resonance imagery (MRI) demonstrates a high sensitivity for contralateral occult malignancies on mammography or ultrasonography. It is widely used for pre-treatment evaluation, especially for patients preparing for breast-conserving surgery. This may be the reason for the higher incidence of contralateral detection in recent decades. Primary breast cancer patients have intermediate risk for contralateral malignancies [1, 2]. The risk is 2–6 times that of the risk for a woman first developing a breast cancer [3]. The likelihood of malignancy for a suspicious contralateral lesion may be different from that of an ipsilateral lesion. A precise and personalized diagnostic strategy should be established for this unusual situation
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