Interobserver variability in breast carcinoma grading results in prognostic stage differences

Rabe, Kimmie; Snir, Olivia L.; Bossuyt, Veerle; Harigopal, Malini; Celli, Romulo; Reisenbichler, Emily

doi:10.1016/j.humpath.2019.09.006

Cited by 31 publications

(23 citation statements)

References 23 publications

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“…Cancers are traditionally diagnosed by histopathology or cytopathology to confirm the presence of tumour cells within a patient sample, assess markers relevant to cancer and to characterise features such as tumour type, stage and grade. This microscopy-based assessment is crucial; however, the process is relatively labour-intensive and somewhat subjective [ 80 , 81 ]. A histology image viewed at high magnification (typically 20x or 40x) can reveal millions of subtle cellular features, and deep CNN models are exceptionally good at extracting features from high-resolution image data [ 82 ].…”

Section: Deep Learning In Oncologymentioning

confidence: 99%

Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

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Section: Deep Learning In Oncologymentioning

confidence: 99%

Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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“…Tumor grading has been a traditional component of pathologic evaluation and offers guidance to therapy and patient management in many organ systems. [1][2][3][4][5] However, there is no consensus on a grading system for invasive pulmonary adenocarcinoma. The 2015 WHO classification of pulmonary adenocarcinoma, 6 based on the predominant histologic pattern, has consistently been found to correlate with prognosis and separates adenocarcinoma into the three following prognostic groups: low grade (lepidic predominant); intermediate grade (acinar or papillary predominant); and high grade (solid or micropapillary predominant).…”

Section: Introductionmentioning

confidence: 99%

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

Moreira

Ocampo

Xia

et al. 2020

Journal of Thoracic Oncology

317

330

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Introduction: A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma. Conclusions: A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.

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“…Secondly, inter-and intra-observer variation has been extensively reported, with a wide range Kappa values (0.43 and 0.85) which correlates to a range from "fair" to "almost perfect" agreement [9,12,25,30,[57][58][59][60][61][62][63][64][65][66][67][68][69] (Table 3). A recent nationwide study in the Netherlands showed substantial variation in grading in daily clinical practice, both between pathology laboratories and between pathologists within individual laboratories [55].…”

Section: Histologic Grading: Reproducibility Issuesmentioning

confidence: 99%

Grading of invasive breast carcinoma: the way forward

2021

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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.

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Interobserver variability in breast carcinoma grading results in prognostic stage differences

Cited by 31 publications

References 23 publications

Deep learning in cancer diagnosis, prognosis and treatment selection

Deep learning in cancer diagnosis, prognosis and treatment selection

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

Grading of invasive breast carcinoma: the way forward

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