CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology

Abstract

Objective: Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results: CACTUS has been evaluated using a surgical pathology application-the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.

Breast cancer tumor heterogeneity has only little impact on the estimation of the Oncotype DX® recurrence score using Magee Equations and Magee Decision Algorithm™

“…A limitation in our study is that we did not evaluate the influence of tumor heterogeneity on the evaluation of the Nottingham score and of the histopathological measure of the maximal tumor diameter on HE tissue sections, as we only focused on complementary IHC (and when required ISH) data. Further study would be necessary to confirm the consistency of the interpretation of these parameters but literature data already point high reproducibility in this field [33,34].…”

Artificial intelligence in breast cancer histopathology

“…To tackle these issues, crowd-source labelling platforms (e.g. CAC-TUS 88 ) or the employment of GAN to create data from scratch (e.g. NAS-SGAN 25 ) have been proposed.…”