MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study

Abstract

BackgroundThe aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes.MethodsTwenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors.ResultsTextural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008).ConclusionsTexture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

“…Relatively, analysis based on one single slice would inevitably lose a lot of important information because of heterogeneity in tumor volume. Hence, some previous studies conducted the 3D segmentation for radiomic analysis (11,(54)(55)(56). It must be pointed out that in order to achieve 3D segmentation, the image data should be acquired using the isotropic acquisition when the cases were initially scanned.…”

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

“…In each loop of the LOOCV, one sample was retained as the test case, and the other samples were used as the training set. At each LOOCV loop, feature selection procedure based on least absolute shrinkage and selection operator (LASSO) was applied on the training set 33,45‐49 . The procedure was repeated for all the LOOCV folds, and a classification score was generated for each test case.…”

Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study

“…14 Therefore, an accurate evaluation of Ki-67 status is important for the prognostic analysis of breast cancer; however, the accuracy of traditional invasive detection methods, namely biopsies, is influenced by sampling errors as biopsies may often not be sufficient in assessing intratumoural heterogeneity expression. 15,16 Breast magnetic resonance imaging (MRI) has a high sensitivity in the detection of breast cancer 17,18 and has played an important role in the detection, diagnosis, and staging of breast cancer. Previous studies have shown that variable MRI findings, such as apparent diffusion coefficient (ADC) values 19 and entropy-based features analysed by texture analysis, 16 correlate with Ki-67 status.…”