B3-lesions of the breast: Risk of malignancy after vacuum-assisted breast biopsy versus core needle biopsy diagnosis

Outcomes of atypical (B3) core biopsy lesions diagnosed across BreastScreen NSW, Australia

INTRODUCTION

Atypical or B3 lesions comprise a heterogeneous group of uncertain malignant potential. B3 lesions diagnosed on core biopsy are usually recommended for diagnostic open biopsy. Identifying factors which could allow conservative management of B3 lesions would be helpful in avoiding unnecessary surgery. The aim of this study was to identify the upgrade rate to malignancy for B3 core biopsy lesions and to compare characteristics of lesions which were malignant and benign at excision.

METHOD

This retrospective study used data from BreastScreen New South Wales (NSW), Australia, of women who were diagnosed with B3 lesions on needle biopsy from 2011 to 2019.

RESULTS

During the study period, 1927 B3 lesions were included. The upgrade rate to malignancy was 26.4%. Of the malignant lesions on excision, 29.6% were invasive and 69.2% were in situ. The rates of upgrade to invasive cancer and DCIS varied substantially with the core biopsy lesion type. Lesions with atypia on core biopsy had significantly higher upgrade rates to malignancy at 34.7% compared to 13.6% for lesions without atypia (p < 0.0001). Lesions with malignant pathology were significantly larger than those with benign pathology (difference = 5.1 mm (95% CI 2.7-7.5 mm), p < 0.001).

CONCLUSIONS

The overall upgrade rate of B3 lesions to malignancy was 26.4%. The majority of the lesions were upgraded to DCIS instead of invasive cancer. Upgrade rates varied by lesion type. Lesions with atypia had significantly higher upgrade rates to cancer compared to lesions without atypia. Malignant lesions were significantly larger than benign lesions.

The positive predictive value of vacuum assisted biopsy (VAB) in predicting final histological diagnosis for breast lesions of uncertain malignancy (B3 lesions): A systematic review & meta-analysis

INTRODUCTION

High-risk or B3 breast lesions are considered lesions of uncertain malignant potential and comprise between 5 and 12% of initial biopsy results. We sought to perform a systematic review and meta-analysis of studies published within the last twenty years to determine the pooled Positive Predictive Value (PPV) of VAB in selected B3 lesions.

METHODS

The study report is based on the guidelines of PRISMA and Meta-Analysis of Observational Studies in Epidemiology.

OUTCOMES

The primary outcome of this study was to determine the PPV of VAB in determining final histological diagnosis in B3 breast lesions using pooled estimates. The secondary outcomes were to determine if needle gauge or the re-classification of Lobular Carcinoma in Situ(LCIS) introduced in 2012 influenced pooled estimates.

RESULTS

78 studies incorporating 6,377 B3 lesions were included in this review, 1214 of which were upgraded to DCIS or invasive malignancy following surgical excision(19%). The pooled PPV of VAB in Atypical Ductal Hyperplasia(ADH) and Lobular Neoplasia(LN) were 0.79(CI 0.76-0.83) and 0.84(CI 0.8-0.88). VAB of Flat Epithelial Atypia(FEA), radial scar and papillary lesions with/without atypia all had a pooled PPV >90% (underestimation rates 7%, 1%, 5% and 3% respectively). Needle gauge size and the change in LCIS classification did not appear to influence underestimation rates on subgroup analysis.

CONCLUSION

Results from this meta-analysis suggests it is reasonable to perform VAB as definitive treatment for certain B3 lesions, specifically LN, FEA, radial scar, and papillary lesions when specific criteria are fulfilled. Surgical excision should continue as the mainstay of treatment for ADH.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

B3 Lesions at Vacuum-Assisted Breast Biopsy under Ultrasound or Mammography Guidance: A Single-Center Experience on 3634 Consecutive Biopsies

The rate of upgrade to cancer for breast lesions with uncertain malignant potential (B3 lesions) diagnosed at needle biopsy is highly influenced by several factors, but large series are seldom available. We retrospectively assessed the upgrade rates of a consecutive series of B3 lesions diagnosed at ultrasound- or mammography-guided vacuum-assisted biopsy (VAB) at an EUSOMA-certified Breast Unit over a 7-year timeframe. The upgrade rate was defined as the number of ductal carcinoma in situ (DCIS) or invasive cancer at pathology after excision or during follow-up divided by the total number of B3 lesions. All lesions were reviewed by one of four pathologists with a second opinion for discordant assessments of borderline cases. Excision or surveillance were defined by the multidisciplinary tumor board, with 6- and 12-month follow-up. Out of 3634 VABs (63% ultrasound-guided), 604 (17%) yielded a B3 lesion. After excision, 17/604 B3 lesions were finally upgraded to malignancy (2.8%, 95% confidence interval [CI] 1.8-4.5%), 10/17 (59%) being upgraded to DCIS and 7/17 (41%) to invasive carcinoma. No cases were upgraded during follow-up. B3a lesions showed a significantly lower upgrade rate (0.4%, 95% CI 0.1-2.1%) than B3b lesions (4.7%, 95% CI 2.9-7.5%, p = 0.001), that had a 22.0 adjusted odds ratio for upgrade (95% CI 2.1-232.3). No significant difference was found in upgrade rates according to imaging guidance or needle caliper. Surveillance-oriented management can be considered for B3a lesions, while surgical excision should be pursued for B3b lesions.