Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women

Yu Tan,Songxin Wu,Yongxi Liu,Huang Huang,Wen Tang,Li Zhang,Hui Mai,Chengwei Li,Kuiming Jiang,Zhiqing Huang

doi:10.1186/s12880-021-00571-x

Abstract

BackgroundNon-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women.MethodsWomen in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann–Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.ResultsThe combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.ConclusionsWith the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

Highlights

Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists
It might appear in benign breast lesions, such as focal adenosis or fibrocystic and inflammatory changes and can manifest in malignant lesions, such as lobular carcinoma, diffuse invasive breast cancer, invasive ductal carcinoma, ductal carcinoma in situ (DCIS), and occasionally, some special types of breast cancers [3,4,5]
Two hundred and ninety-three of the patients met the following criteria: (1) NME confirmed by pathological analysis; (2) magnetic resonance imaging (MRI) performed within 1 week before the surgery and during 7–15 days of the patients’ menstrual cycle to decrease the false-positive results provided by background enhancement (BPE) [16]; and (3) define the shortest part of its measuring diameter is greater than

Summary

Introduction

Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. It remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Tan et al BMC Med Imaging (2021) 21:48 from the surrounding enhanced breast parenchyma. It has no space occupation effect and typically contains scattered adipose and glandular tissues [1, 2]. Distinguishing between benign and malignant NME is a challenge in breast MRI-based diagnosis. Breast tissue affected by hormone effects would add to the difficulty of diagnosis of NME, especially in premenopausal women [11]

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