Improving Ki67 assessment concordance by the use of an artificial intelligence‐empowered microscope: a multi‐institutional ring study

Abstract

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study Aims: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance. Methods and results: We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope. Conclusion:The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.

The additional value of ONEST (Observers Needed to Evaluate Subjective Tests) in assessing reproducibility of oestrogen receptor, progesterone receptor, and Ki67 classification in breast cancer

“…The common method of doing this is by eyeballing, i.e., having a look at the slide and estimating the amount of tumour cell staining. This may be tuned by estimating the area occupied by 100-200 cells, made more precise by counting 500-2000 cells [15], facilitating the count with an application [16,17], or by using digital image analysis [18][19][20] or artificial intelligence [21]. Because of the costs and time required for the latter methods deemed more precise and reproducible, eyeballing is probably the most generally used method worldwide and is not obviously worse than some forms of digital image analysis [22].…”

A scoping review of deep learning in cancer nursing combined with augmented reality: The era of intelligent nursing is coming

“…J o u r n a l P r e -p r o o f ) and Italy (n = 1). Of 6 studies, four were diagnostic study [37][38][39][40] , one was report [41] and the study design of the rest one is unclear [42] . All of the included studies conducted the application development of DL+AR.…”

“…All of the included studies conducted the application development of DL+AR. Five studies [37][38][39][40][41] developed the microscopes based on the deep learning method and augmented reality module to improve the accuracy and efficiency of cancer diagnosis, and the rest one [42] established a two-steps automatic system to improve accuracy in locating the tumor…”

“…In the stage of technology development in these studies, first of all, when the DL model was constructed, a certain number of samples were selected and the selected combined with AR. In the five studies that developed the AI microscope [37][38][39][40][41] , the results identified by the AI system were then projected into the original optical path by an AR technique. In one study that implemented a two-steps automatic system [42] ,…”

H2AFZ: A Novel Prognostic Marker in Canine Melanoma and a Predictive Marker for Resistance to CDK4/6 Inhibitor Treatment

“…Although numerous works have aimed to find new effective prognostic markers for canine MM, there is still a lack of reliable ones, and Ki67, beside specific histological features of malignancy, remains the only currently available established prognostic marker (2,23,24). However, there are still some limitations to its use, such as the presence of some cases with low Ki67 index but poor prognosis (2), the lack of consistency in Ki67 assessment on microscope, and inter-observer variations (25).…”