Preneoplasia of the Breast and Molecular Landscape

Atypical ductal or lobular hyperplasia, lobular carcinoma in-situ, flat epithelial atypia, and future risk of developing breast cancer: Systematic review and meta-analysis

BACKGROUND

Biopsy-proven breast lesions such as atypical ductal hyperplasia (ADH) or atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS) and flat epithelial atypia (FEA) increase subsequent risk of breast cancer (BC), but long-term risk has not been synthesized. A systematic review was conducted to quantify future risk of breast cancer accounting for time since diagnosis of these high-risk lesions.

METHODS

A systematic search of literature from 2000 was performed to identify studies reporting BC as an outcome following core-needle or excision biopsy histology diagnosis of ADH, ALH, LCIS, lobular neoplasia (LN) or FEA. Meta-analyses were conducted to estimate cumulative BC incidence at five-yearly intervals following initial diagnosis for each histology type.

RESULTS

Seventy studies reporting on 47,671 subjects met eligibility criteria. BC incidence at five years post-diagnosis with a high-risk lesion was estimated to be 9.3 % (95 % CI 6.9-12.5 %) for LCIS, 6.6 % (95 % CI 4.4-9.7 %) for ADH, 9.7 % (95 % CI 5.3-17.2 %) for ALH, 8.6 % (95 % CI 6.5-11.4 %) for LN, and 3.8 % (95 % CI 1.2-11.7 %) for FEA. At ten years post-diagnosis, BC incidence was estimated to be 11.8 % (95 % CI 9.0-15.3 %) for LCIS, 13.9 % (95 % CI 7.8-23.6 %) for ADH, 15.4 % (95 % CI 7.2-29.3 %) for ALH, 17.0 % (95 % CI 7.2-35.3 %) for LN and 7.2 % (95 % CI 2.2-21.2 %) for FEA.

CONCLUSION

Our findings demonstrate increased BC risk sustained over time since initial diagnosis of high-risk breast lesions, varying by lesion type, with relatively less evidence for FEA.

Special Histologic Type and Rare Breast Tumors - Diagnostic Review and Clinico-Pathological Implications

Breast cancer is the most common malignant tumor in females. While most carcinomas are categorized as invasive carcinoma, no special type (NST), a diverse group of tumors with distinct pathologic and clinical features is also recognized, ranging in incidence from relatively more common to rare. So-called "special histologic type" tumors display more than 90% of a specific, distinctive histologic pattern, while a spectrum of tumors more often encountered in the salivary gland may also arise in the breast. Metaplastic carcinomas can present diagnostic challenges. Some uncommon tumors harbor pathognomonic genetic alterations. This article provides an overview of the key diagnostic points and differential diagnoses for this group of disparate lesions, as well as the salient clinical characteristics of each entity.

Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues

PURPOSE

Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis.

METHODS

Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images.

RESULTS

The AI-based image analysis provided the following area under the curve values (AUC): normal lesion versus DCIS, 0.9902; DCIS versus comedo DCIS, 0.9942; normal lesion versus CCL, 0.9786; and UDH versus DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the gradient-weighted class activation mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS.

CONCLUSIONS

These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.