The increasing importance of histologic grading in tailoring adjuvant systemic therapy in 30,843 breast cancer patients

Identification of Glandular (Acinar)/Tubule Formation in Invasive Carcinoma of the Breast: A Study to Determine Concordance Using the World Health Organization Definition

CONTEXT.—

The Nottingham Grading System (NGS) developed by Elston and Ellis is used to grade invasive breast cancer (IBC). Glandular (acinar)/tubule formation is a component of NGS.

OBJECTIVE.—

To investigate the ability of pathologists to identify individual structures that should be classified as glandular (acinar)/tubule formation.

DESIGN.—

A total of 58 hematoxylin-eosin photographic images of IBC with 1 structure circled were classified as tubules (41 cases) or nontubules (17 cases) by Professor Ellis. Images were sent as a PowerPoint (Microsoft) file to breast pathologists, who were provided with the World Health Organization definition of a tubule and asked to determine if a circled structure represented a tubule.

RESULTS.—

Among 35 pathologists, the κ statistic for assessing agreement in evaluating the 58 images was 0.324 (95% CI, 0.314-0.335). The median concordance rate between a participating pathologist and Professor Ellis was 94.1% for evaluating 17 nontubule cases and 53.7% for 41 tubule cases. A total of 41% of the tubule cases were classified correctly by less than 50% of pathologists. Structures classified as tubules by Professor Ellis but often not recognized as tubules by pathologists included glands with complex architecture, mucinous carcinoma, and the "inverted tubule" pattern of micropapillary carcinoma. A total of 80% of participants reported that they did not have clarity on what represented a tubule.

CONCLUSIONS.—

We identified structures that should be included as tubules but that were not readily identified by pathologists. Greater concordance for identification of tubules might be obtained by providing more detailed images and descriptions of the types of structures included as tubules.

Pros and cons of artificial intelligence implementation in diagnostic pathology

The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.

Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective

Building on a growing number of pathology labs having a full digital infrastructure for pathology diagnostics, there is a growing interest in implementing artificial intelligence (AI) algorithms for diagnostic purposes. This article provides an overview of the current status of the digital pathology infrastructure at the University Medical Center Utrecht and our roadmap for implementing AI algorithms in the next few years.

Trends in surgery and adjuvant treatment for early-stage breast cancer: a population-based study in Queensland, Australia

PURPOSE

Our aim was to describe variations in the treatment of early-stage breast cancer and to examine factors associated with disease-specific survival (DSS).

METHODS

The study used linked data for 24,190 women with a T1 (≤ 20 mm) breast cancer who underwent surgery from 2005 to 2019. Multivariate logistic regression was used to model predictors of receiving breast-conserving surgery (BCS) compared to mastectomy and a multinomial model was used to examine factors associated with type(s) of treatment received.

RESULTS

Overall, 70.3% had BCS, with a reduced likelihood of BCS observed for younger women (p < 0.001), rural residence, (p < 0.001), socioeconomic disadvantage (p = 0.004), higher tumour grade (p < 0.001) and surgery in a public versus private hospital (p < 0.001). Compared to women who received BCS and radiation therapy (RT), those having mastectomy alone or mastectomy plus RT were more likely to be younger (p < 0.001), live in a rural area (p < 0.001), have higher-grade tumours (p < 0.001) and positive lymph nodes (p < 0.001). Overall 5-year survival was 95.3% and breast cancer-specific survival was 98.3%. Highest survival was observed for women having BCS and RT and lowest for those having mastectomy and RT (p < 0.001).

CONCLUSION

Our results indicate some variation in the management of early-stage breast cancer. Lower rates of BCS were observed for rural and disadvantaged women and for those treated in a public or low-volume hospital. Whilst survival was high for this cohort, differences in tumour biology likely explain the differences in survival according to treatment type.

Grading of invasive breast carcinoma: the way forward

Histologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histologic grading needs to be performed accurately, on properly fixed specimens, and by adequately trained dedicated pathologists that take the time to diligently follow the protocol methodology. In this paper, we review the history of histologic grading, describe the basics of grading, review prognostic value and reproducibility issues, compare performance of grading to gene expression profiles, and discuss how to move forward to improve reproducibility of grading by training, feedback and artificial intelligence algorithms, and special stains to better recognize mitoses. We conclude that histologic grading, when adequately carried out, remains to be of important prognostic value in breast cancer patients.