Dataset Information

Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports.

ABSTRACT: BACKGROUND:Histologic grade of ductal carcinoma in situ of the breast (DCIS) may become the single biomarker that decides whether patients will be treated. Yet, evidence shows that grading variation in daily practice is substantial. To facilitate quality improvement, feedback reports, in which laboratory-specific case-mix adjusted proportions per grade were benchmarked against other laboratories, were sent to the individual laboratories by March 1, 2018. One year later, the effect of these feedback reports on inter-laboratory variation was studied. METHODS:Synoptic pathology reports of all pure DCIS resection specimens between March 1, 2017 and March 1, 2019 were retrieved from PALGA (the nationwide Dutch pathology registry). Laboratory-specific proportions per grade were compared to the overall proportion in the year before and after feedback. The absolute deviation for all three grades at once, represented by the overall deviation score (ODS), was calculated as the sum of deviations from the grade-specific overall proportions. Case-mix adjusted, laboratory-specific odds ratios (ORs) for high- (grade III) versus low-grade (grade I-II) DCIS were obtained by multivariable logistic regression. RESULTS:Overall, 2954 DCIS reports from 31 laboratories were included. After feedback, the range between laboratories decreased by 22 and 6.5% for grades II and III, while an increase of 6.2% was observed for grade I. Both the mean ODS (27.2 to 24.1%) and maximum ODS (87.7 to 59.6%) decreased considerably. However, the range of case-mix adjusted ORs remained fairly stable and substantial (0.39 (95% CI: 0.18-0.86) to 3.69 (95% CI: 1.30-10.51)). CONCLUSION:A promising decrease in grading variation was observed after laboratory-specific feedback for DCIS grades II-III, while this was not observed for DCIS grade I. Overall, grading variation remained substantial which needs to be addressed considering its clinical implications. Nationwide consensus on a classification, and training of (expert breast) pathologists, for example by e-learning, may help to further improve grading standardization.

SUBMITTER: van Dooijeweert C

PROVIDER: S-EPMC7216330 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports.

van Dooijeweert Carmen C van Diest Paul J PJ Baas Inge O IO van der Wall Elsken E Deckers Ivette A G IAG

Diagnostic pathology 20200511 1

<h4>Background</h4>Histologic grade of ductal carcinoma in situ of the breast (DCIS) may become the single biomarker that decides whether patients will be treated. Yet, evidence shows that grading variation in daily practice is substantial. To facilitate quality improvement, feedback reports, in which laboratory-specific case-mix adjusted proportions per grade were benchmarked against other laboratories, were sent to the individual laboratories by March 1, 2018. One year later, the effect of the ...[more]

PMID: 32393303

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Project description:Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

Dataset Information

Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports.

Publications

Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports.

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