Impact of routine expert breast pathology consultation and factors predicting discordant diagnosis

Woeste, Matthew R; Jacob, Kevin; Duff, Maxwell B.; Donaldson, Marilyn; Sanders, Mary Ann; McMasters, Kelly M.; Ajkay, Nicolás

doi:10.1016/j.suronc.2022.101860

Cited by 3 publications

(8 citation statements)

References 27 publications

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“…We have observed this phenomenon in the few cases that were shared among a group of breast pathology experts where weighting of the contribution of the various morphological features present can be subjective and can influence their final classification 24 . Our study is in line with a previous report indicating 35% discordance rate 25 . In another study a significant discrepancy, which was defined as a disagreement that affected patient care, was found in 226 cases (11.5%) 26 .…”

Section: Discussionsupporting

confidence: 89%

“…24 Our study is in line with a previous report indicating 35% discordance rate. 25 In another study a significant discrepancy, which was defined as a disagreement that affected patient care, was found in 226 cases (11.5%). 26 Some authors 27 have reported major discordance in 8% of cases 10 ; however, some changes in the management were reported in 80% of the cases.…”

Section: Discussionmentioning

confidence: 95%

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Second opinion (external specialist referral) practice of breast pathology: the Nottingham experience

et al. 2023

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AimsBreast pathology is a challenging field, and discrepancies in diagnoses exist and can affect patient management. This study aims to review a breast referral practice and assess the pattern and frequency of breast lesions sent for an external expert review and evaluate potential impacts on patients’ care.Methods and resultsSeven hundred and forty cases that were referred to Nottingham City Hospital for a second opinion between 2019 and 2022 which have slides and reports were retrieved and reviewed. Reasons for referral, initial diagnosis, proffered specialist opinion and any discrepancy or potential impacts of management were assessed. The most frequent entities were papillary lesions (19%), fibroepithelial lesions (17%), invasive carcinomas that were sent for confirmation of the invasive diagnosis or subtyping of the invasive tumour (17%), intraductal epithelial proliferation with atypia (9%) and spindle cell lesions (8%). Other entities included biphasic tumours such as adenomyoepithelioma, as well as vascular and nipple lesions. Few cases were sent for prognostic classification or comments on the management, and in occasional cases no initial diagnosis was offered. After reviewing the cases by the expert pathologists, the initial diagnosis was confirmed or one of the suggested diagnoses was preferred in 79% of cases, including 129 cases (17%) in which the opinion resulted minor changes in the management. Significant changes in the classification of lesions were made in 132 cases (18%) which resulted in significant change in the patient management recommendation. In 14 cases (2%) a final classification was not possible, and further specialist opinion was obtained. Comments on the differential diagnosis and advice on further patient management were provided in most cases.ConclusionsThis study demonstrates the value of external referral of challenging, rare and difficult to classify breast lesions. It also highlights the most common breast lesions that are likely to be challenging, and specialist opinion can refine their classification to improve patient care.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 95%

Second opinion (external specialist referral) practice of breast pathology: the Nottingham experience

et al. 2023

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“…The identification of patients with high-grade tumours at the time of biopsy is essential for the decision to treat a patient with neoadjuvant chemotherapy [7,34], and especially within the larger ER-positive, HER2-negative subgroup [7]. However, conventional histological grading of biopsies by pathologists remains challenging and most biopsies are assigned the intermediate NHG2, or are not graded at all [14,18]. This lack of precision in biopsy grading leads to a discrepancy between pathologists, and in one cohort up to 45% of women had a change in diagnosis between the biopsy and the resected tumour [18].…”

Section: Discussionmentioning

confidence: 99%

“…However, conventional histological grading of biopsies by pathologists remains challenging and most biopsies are assigned the intermediate NHG2, or are not graded at all [14,18]. This lack of precision in biopsy grading leads to a discrepancy between pathologists, and in one cohort up to 45% of women had a change in diagnosis between the biopsy and the resected tumour [18]. We found that 41% of patients who were not assigned a grade on the biopsy were assigned to the high-risk group by DeepGrade, of which 54% were actually assigned as NHG3 by pathologists on their resected tumour specimen.…”

Section: Discussionmentioning

confidence: 99%

“…This causes significant discrepancies between the biopsy grading and the histological grade assigned on the surgically resected specimen [ 17 ]. These uncertainties are accompanied by the fact that a greater number of biopsy samples are not even assigned a grade, and that up to 70% of the biopsy samples are assigned the intermediate grade, NHG2 [ 15 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Boissin,

Wang,

Sharma

et al. 2024

Breast Cancer Res

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Background Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. Methods A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. Results Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). Conclusions DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

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Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Boissin,

Wang,

Sharma

et al. 2023

Preprint

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Introduction Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology. However, manual NHG assessment of biopsies is challenging and has a large inter-assessor variability with a large proportion being classified as NHG2 (intermediate grade). Here, we evaluate whether DeepGrade, a previously developed model for the risk stratification of resected tumour specimens, could be applied to risk-stratify biopsy specimens. Methods A total of 11,943,905 tiles from 1171 whole slide images (WSIs) of preoperative biopsies from 897 patients diagnosed with breast cancer in Stockholm, Sweden, were included in this retrospective observational study. DeepGrade, a deep convolutional neural network model, was applied for classification of low and high risk tumours and evaluated against clinically assigned grades 1 and 3 using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. Results The DeepGrade model classified resected tumour cases with grades NHG1 and NHG3 using only biopsy specimens with an AUC of 0.903 (95% CI: 0.88;0.93). The model could also classify the biopsy NHG (1 and 3) assessed on the biopsy of 186 patients with an AUC of 0.959 (95% CI: 0.93; 0.99). Furthermore, out of the 434 NHG2 tumours, 255 (59%) were classified as DeepGrade2-low, and 179 (41%) were classified as DeepGrade2-high. Using a multivariable Cox proportional hazards model the hazard ratio between low- and high-risk groups was estimated as 2.01 (p-value = 0.036). Conclusions DeepGrade could predict the resected tumour grades NHG1 and NHG3 using only the biopsy specimen and sub-classify grade 2 tumours into low and high risks. The results demonstrate that the DeepGrade model can provide decision support for biopsy grading, and potentially provide decision support in the clinical setting to identifying high-risk tumours based on preoperative breast biopsies, thus improving information available for clinical treatment decisions.

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Impact of routine expert breast pathology consultation and factors predicting discordant diagnosis

Cited by 3 publications

References 27 publications

Second opinion (external specialist referral) practice of breast pathology: the Nottingham experience

Second opinion (external specialist referral) practice of breast pathology: the Nottingham experience

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

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