Heterogeneity of Focal Breast Lesions and Surrounding Tissue Assessed by Mammographic Texture Analysis: Preliminary Evidence of an Association with Tumor Invasion and Estrogen Receptor Status

Abstract

Aim: This pilot study investigates whether heterogeneity in focal breast lesions and surrounding tissue assessed on mammography is potentially related to cancer invasion and hormone receptor status. Materials and Methods: Texture analysis (TA) assessed the heterogeneity of focal lesions and their surrounding tissues in digitized mammograms from 11 patients randomly selected from an imaging archive [ductal carcinoma in situ (DCIS) only, n = 4; invasive carcinoma (IC) with DCIS, n = 3; IC only, n = 4]. TA utilized band-pass image filtration to highlight image features at different spatial frequencies (filter values: 1.0–2.5) from fine to coarse texture. The distribution of features in the derived images was quantified using uniformity. Results: Significant differences in uniformity were observed between patient groups for all filter values. With medium scale filtration (filter value = 1.5) pure DCIS was more uniform (median = 0.281) than either DCIS with IC (median = 0.246, p = 0.0102) or IC (median = 0.249, p = 0.0021). Lesions with high levels of estrogen receptor expression were more uniform, most notably with coarse filtration (filter values 2.0 and 2.5, rs = 0.812, p = 0.002). Comparison of uniformity values in focal lesions and surrounding tissue showed significant differences between DCIS with or without IC versus IC (p = 0.0009). Conclusion: This pilot study shows the potential for computer-based assessments of heterogeneity within focal mammographic lesions and surrounding tissue to identify adverse pathological features in mammographic lesions. The technique warrants further investigation as a possible adjunct to existing computer aided diagnosis systems.

Magnetic resonance imaging texture analysis classification of primary breast cancer

“…TA could be useful in patients where sampling (multiple) breast lesions is anatomically difficult or could lead to histological underestimation of the amount of disease present [35,37]. The data provided demonstrates that texture analysis is an imageprocessing tool worthy of further evaluation in the MR classification of breast cancer subtype.…”

“…Thus, as well as providing a method for mapping tumoral heterogeneity, it could also provide a means of image-based classification of breast cancer, which may prove to correlate with prognostic indicators of systemic spread (such as nodal status and vascular invasion); this merits further investigation. Such characterisation has been reported for computed tomography (CT) imaging [30], and assessment of lesion heterogeneity is becoming more topical [31][32][33][34][35] in the drive to fully characterise entire lesions rather than small sub-samples as obtained using biopsy alone.…”

“…There are, however, promising results using all COM features, and, therefore, this warrants future work, with increased patient numbers. Most previous studies utilising TA in breast MRI have explored the identification of malignancy and differentiation of benign from malignant lesions [12][13][14]35]. However, in the era of personalised treatment based on tumour biology, more detailed characterisation of malignancies is required for 28,36].…”

“…More detailed tissue characterisation is more complex and challenging than differentiation between malignant and benign tissue as differences in pathological features need to be clarified [35] and the applicability of texture analysis in diagnosis, classification, and grading is an emerging field. Recent studies using the technique have differentiated between invasive cancer and DCIS on mammographic images [35], classification of glioblastoma types [28], and differentiation between glioma and metastases on MR images [37]. This study demonstrates an extension to classification of breast cancer subtypes on MR.…”

CT Texture Analysis of Renal Masses

“…The considerations used to determine the size of the study cohort were to include a balanced group size of each potential lesion type as to achieve equal weighting within the model for each group. The number of cases (20) was chosen based on the limited number of available cases of oncocytoma within the pathology database (24), some of which were randomly selected to remain as prospective validation cases not used to create the initial models.…”