Stromal computational signatures predict upgrade to invasive carcinoma in mass-forming DCIS: a brief report of 44 cases

Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ

Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a benefit of screening mammography is early cancer detection, it also leads to increased biopsy diagnosis of noninvasive lesions. Previously, treatment guidelines for both entities included surgical excision because of the risk of upgrade to invasive cancer after surgery and risk of progression to invasive cancer for DCIS. However, this universal management approach is not optimal for all patients because most lesions are not upgraded after surgery. Furthermore, some DCIS lesions do not progress to clinically significant invasive cancer. Overtreatment of high-risk lesions and DCIS is considered a burden on patients and clinicians and is a strain on the health care system. Extensive research has identified many potential histologic, clinical, and imaging factors that may predict ADH and DCIS upgrade and thereby help clinicians select which patients should undergo surgery and which may be appropriate for active surveillance (AS) with imaging. Additionally, multiple clinical trials are currently underway to evaluate whether AS for DCIS is feasible for a select group of patients. Recent advances in MRI, artificial intelligence, and molecular markers may also have an important role to play in stratifying patients and delineating best management guidelines. This review article discusses the available evidence regarding the feasibility and limitations of AS for ADH and DCIS, as well as recent advances in patient risk stratification.

Mass-forming ductal carcinoma in situ: An ultrasonographic and histopathologic correlation study

Ultrasound (US) guided core needle biopsy (CNB) for mass lesions resulting in a diagnosis of ductal carcinoma in situ (DCIS) is often considered radiologically discordant and generates surgical planning difficulty. One hundred cases of US-guided CNB for mass lesions diagnosed as DCIS were collected from 2013 to 2021. Histological features were reviewed and correlated with radiology and surgical excision findings. Thirty (30%) were high-grade (HG), and seventy (70%) were low- to intermediate-grade. Seventy-one (71%) cases had a histological correlate of a mass-forming lesion, including 26 (26%) were associated with benign mass-forming lesions (category 1) such as papilloma, complex sclerosing lesion/radial scar, fibroadenoma, sclerosing adenosis, and ruptured cyst; 23 (23%) were HG with solid pattern, comedo necrosis, and stromal desmoplasia (category 2); and 22 (22%) had predominantly papillary architecture (category 3). Twenty-nine (29%) were discordant with no histologic correlate of a mass lesion (category 4). Follow-up excisions were available in 79 cases. Invasive carcinoma was identified in 14 cases (18%), of which 8 were from the radiologically discordant category (35%), 3 (17%) associated with HG DCIS with desmoplasia, 2 (10%) associated with benign mass lesion and 1(5%) was predominantly papillary architecture. US-guided CNB for mass-forming lesions with a DCIS diagnosis on CNB can be grouped into four categories. Radiology-pathology correlation is essential. This categorization emphasized rad-path correlation and had a clear difference in upgrade rate on follow-up excision. Rad-path discordant biopsy cases were more likely to be associated with a missed invasive carcinoma (p < 0.05).

From Immunohistochemistry to New Digital Ecosystems: A State-of-the-Art Biomarker Review for Precision Breast Cancer Medicine

Simple Summary

In this state-of-the-art breast biomarker review, we have tried to imagine and illustrate future, emerging digital breast cancer ecosystems which allow for greater incorporation of traditional immunohistochemical and molecular biomarkers, WSI, and radiomic features.

Abstract

Breast cancers represent complex ecosystem-like networks of malignant cells and their associated microenvironment. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are biomarkers ubiquitous to clinical practice in evaluating prognosis and predicting response to therapy. Recent feats in breast cancer have led to a new digital era, and advanced clinical trials have resulted in a growing number of personalized therapies with corresponding biomarkers. In this state-of-the-art review, we included the latest 10-year updated recommendations for ER, PR, and HER2, along with the most salient information on tumor-infiltrating lymphocytes (TILs), Ki-67, PD-L1, and several prognostic/predictive biomarkers at genomic, transcriptomic, and proteomic levels recently developed for selection and optimization of breast cancer treatment. Looking forward, the multi-omic landscape of the tumor ecosystem could be integrated with computational findings from whole slide images and radiomics in predictive machine learning (ML) models. These are new digital ecosystems on the road to precision breast cancer medicine.