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

Abstract

Background 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. 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. Methods Women 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. Results The 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. Conclusions With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

Non-mass Findings on MRI and Differential Diagnosis

“…Tan ve ark. 'da [38] premenopozal kadınlarda malign-benign KDK'yi ayırt etmede doku ana-lizinin değerini araştırmak amacıyla klinik ve rutin MRG özellikleri ile doku analizini birleştiren bir model oluşturmuşlar ve kombine modelin, tek başına klinik, rutin MRG özellikleri veya doku analizi ile karşılaştırıldığında malign-benign KDK'yı ayırt etmede tanısal performansının daha üstün olduğunu bulmuşlardır (AUC: 0,887-0,832-0,74). Klinik ve rutin MRG özellikleri ile karşılaştırıldığında, model yüksek özgüllük göstermiştir (%72,5'e karşı %80).…”

Ultrasound diagnosis of non-mass MRI-detected lesions

“…(1) Whether the use of US equipment, such as high-frequency probes (18–24 MHz), will enable morphological distinction between invasive and noninvasive cancer, and whether low blood flow display and contrast-enhanced US will increase the malignant diagnosis rate compared to MRI. (2) Distinguishing between background parenchymal enhancement and NME is difficult even for radiologists [ 68 ]. Breast MRI with CAD showed high diagnostic performance for CAD diagnosis of mass lesions, but poor performance for NME, according to a 2010 report [ 69 ].…”

“…Breast MRI with CAD showed high diagnostic performance for CAD diagnosis of mass lesions, but poor performance for NME, according to a 2010 report [ 69 ]. Recently, by adding radiomics signatures, discriminability with sensitivity of 0.887–0.820 and specificity of 0.80–0.864 has been confirmed even for NME [ 68 , 70 ]. It is a future challenge to see how the introduction of objective and uniform criteria based on advances in image analysis technology will affect second-look indications.…”

Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses

“…In 2020, breast cancer became the most common cancer of women worldwide ( 2 ), and the differentiation between benign and malignant breast lesions using MRI-based diagnostics was found to be critical for breast cancer treatments. However, distinguishing benign and malignant breast lesions on DCE-MRI is challenging, especially when NMEs are present ( 3 ).…”