Mammography features for early markers of aggressive breast cancer subtypes and tumor characteristics: A population-based cohort study.

Current breast cancer risk models identify mostly less aggressive tumors, although only women developing fatal breast cancer will greatly benefit from early identification. Here, we evaluated the use of mammography features (microcalcification clusters, computer-generated Breast Imaging Reporting and Data System [cBIRADS] density and lack of breast density reduction) as early markers of aggressive subtypes and tumor characteristics. Mammograms were retrieved from a population-based cohort of women that were diagnosed with breast cancer from 2001 to 2008 in Stockholm-Gotland County, Sweden. Tumor and patient characteristics were obtained from Stockholm Breast Cancer Quality Register and the Swedish Cancer Registry. Multinomial logistic regression was used to individually model each mammographic feature as a function of molecular subtypes, tumor characteristics and detection mode. A total of 4546 women with invasive breast cancer were included in the study. Women with microcalcification clusters in the affected breast were more likely to have human epidermal growth factor receptor 2 subtype (odds ratio [OR] 1.78; 95% confidence interval [CI] 1.24-2.54) and potentially less likely to have basal subtype (OR 0.54; 0.30-0.96) compared to Luminal A subtype. High mammographic cBIRADS showed association with larger tumor size and interval vs screen-detected cancers. Lack of density reduction was associated with interval vs screen-detected cancers (OR 1.43; 1.11-1.83) and potentially of Luminal B subtype vs Luminal A subtype (OR 1.76; 1.04-2.99). In conclusion, microcalcification clusters, cBIRADS density and lack of breast density reduction could serve as early markers of particular subtypes and tumor characteristics of breast cancer. This information has the potential to be integrated into risk models to identify women at risk for developing aggressive breast cancer in need of supplemental screening.

Breast Tissue Organisation and its Association with Breast Cancer Risk.

BACKGROUND: Mammographic percentage density is an established and important risk factor for breast cancer. In this paper, we investigate the role of the spatial organisation of (dense vs. fatty) regions of the breast defined from mammographic images in terms of breast cancer risk. METHODS: We present a novel approach that provides a thorough description of the spatial organisation of different types of tissue in the breast. Each mammogram is first segmented into four regions (fatty, semi-fatty, semi-dense and dense tissue). The spatial relations between each pair of regions is described using so-called forces histograms (FHs) and summarised using functional principal component analysis. In our main analysis, association with case-control status is assessed using a Swedish population-based case-control study (1,170 cases and 1283 controls), for which digitised mammograms were available. We also carried out a small validation study based on digital images. RESULTS: For our main analysis, we obtained a global p value of 2×10-7 indicating a significant association between the spatial relations of the four segmented regions and breast cancer status after adjustment for percentage density and other important breast cancer risk factors. Our (spatial relations) score had a per standard deviation odds ratio 1.29, after accounting for overfitting (percentage density had a per standard deviation odds ratio of 1.34). The spatial relations between the fatty and semi-fatty tissue and the spatial relations between the fatty and dense tissue were the most significant. The spatial relations between the fatty and semi-fatty tissue were associated with parity and age at first birth (p=6×10-4). Using digital images, we were able to verify that the same characteristics of tissue organisation can be identified and we validated the association for the spatial relations between the fatty and semi-fatty tissue. CONCLUSIONS: Our findings are consistent with the notion that fibroglandular and adipose tissue plays a role in breast cancer risk and, more specifically, they suggest that fatty tissue in the lower quadrants and the absence of density in the retromammary space, as shown in mediolateral oblique images, are protective against breast cancer.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information.

BACKGROUND: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. METHODS: Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). RESULTS: We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). CONCLUSION: We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SIGNIFICANCE: Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.