Mammary ductal carcinoma in situ: a fresh look at architectural patterns

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk

Background

Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK.

Methods

The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk.

Results

The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3–25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0–13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006).

Conclusions

Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1165-5) contains supplementary material, which is available to authorized users.

Intratumoral Heterogeneity in Ductal Carcinoma In Situ: Chaos and Consequence

Ductal carcinoma in situ (DCIS) is a non-invasive proliferative growth in the breast that serves as a non-obligate precursor to invasive ductal carcinoma. The widespread adoption of screening mammography has led to a steep increase in the detection of DCIS, which now comprises approximately 20% of new breast cancer diagnoses in the United States. Interestingly, the intratumoral heterogeneity (ITH) that has been observed in invasive breast cancers may have been established early in tumorigenesis, given the vast and varied ITH that has been detected in DCIS. This review will discuss the intratumoral heterogeneity of DCIS, focusing on the phenotypic and genomic heterogeneity of tumor cells, as well as the compositional heterogeneity of the tumor microenvironment. In addition, we will assess the spatial heterogeneity that is now being appreciated in these lesions, and summarize new approaches to evaluate heterogeneity of tumor and stromal cells in the context of their spatial organization. Importantly, we will discuss how a growing understanding of ITH has led to a more holistic appreciation of the complex biology of DCIS, specifically its evolution and natural history. Finally, we will consider ways in which our knowledge of DCIS ITH might be translated in the future to guide clinical care for DCIS patients.

Ductal carcinoma in situ of the breast: correlation between histopathological features and age of patients

Background

The histopathological subtype, nuclear grade and presence or absence of comedonecrosis are established as critical elements in the reporting of ductal carcinoma in situ (DCIS) of the breast. The aims of this study were to determine the frequencies of morphological subtypes of DCIS, nuclear grade and comedonecrosis; to compare the age of patients with the histopathological characteristics of DCIS, and to assess the agreement of grade between in situ and invasive components in DCIS cases that were associated with invasive carcinoma.

Methods

We evaluated a series of 403 cases of DCIS, pure or associated with invasive mammary carcinoma, consecutively identified from the histopathology files of the Breast Pathology Laboratory, Federal University of Minas Gerais, Brazil, from 2003 to 2008.

Results

DCIS displayed a single growth pattern in most cases (55.1%) and the solid subtype was the most common morphology (42.2% of the total). High-grade DCIS was identified in 293/403 cases (72.7%) and comedonecrosis was present in 222/403 cases (55%). Among DCIS with a single architectural pattern, high grade was more common in the solid subtype (151/168 cases, 89.9%; p < 0.001). Only 32% of tumours with a cribriform pattern had high nuclear grade. Comedonecrosis was more common in the solid morphology than in the cribriform, papillary and micropapillary subtypes (p < 0.001). Patients with high-grade DCIS were younger in relation to patients with low-grade DCIS (p = 0.027) and patients with tumours with comedonecrosis were also younger in comparison to patients with tumours without comedonecrosis (p = 0.003). Fair agreement was observed between in situ and invasive components with regard to grade (weighted kappa = 0.23).

Conclusions

The high nuclear grade and the presence of comedonecrosis were identified more frequently in younger patients and more often correlated with the solid pattern of DCIS.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_227