98%
921
2 minutes
20
Background: The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation exists when pathologists manually count mitotic figures. Artificial intelligence (AI) coupled with whole slide imaging offers a potential solution to this problem. The aim of this study was to accordingly critique an AI tool developed to quantify mitotic figures in whole slide images of invasive breast ductal carcinoma.
Methods: A representative H&E slide from 320 breast invasive ductal carcinoma cases was scanned at 40x magnification. Ten expert pathologists from two academic medical centers labeled mitotic figures in whole slide images to train and validate an AI algorithm to detect and count mitoses. Thereafter, 24 readers of varying expertise were asked to count mitotic figures with and without AI support in 140 high-power fields derived from a separate dataset. Their accuracy and efficiency of performing these tasks were calculated and statistical comparisons performed.
Results: For each experience level the accuracy, precision and sensitivity of counting mitoses by users improved with AI support. There were 21 readers (87.5%) that identified more mitoses using AI support and 13 reviewers (54.2%) that decreased the quantity of falsely flagged mitoses with AI. More time was spent on this task for most participants when not provided with AI support. AI assistance resulted in an overall time savings of 27.8%.
Conclusions: This study demonstrates that pathology end-users were more accurate and efficient at quantifying mitotic figures in digital images of invasive breast carcinoma with the aid of AI. Higher inter-pathologist agreement with AI assistance suggests that such algorithms can also help standardize practice. Not surprisingly, there is much enthusiasm in pathology regarding the prospect of using AI in routine practice to perform mundane tasks such as counting mitoses.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335442 | PMC |
http://dx.doi.org/10.1186/s13000-020-00995-z | DOI Listing |
Zhonghua Bing Li Xue Za Zhi
September 2025
Department of Pathology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou 450003, China.
To investigate the clinicopathological features, genetic characteristics, and differential diagnosis of glomangiomatosis with uncertain malignant potential. Two cases of glomangiomatosis with uncertain malignant potential were collected at Henan Provincial People's Hospital from 2013 and 2023. Immunohistochemistry and next generation sequencing (DNA-seq) were used to detect the related protein and gene variation.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
September 2025
Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China.
To investigate the clinicopathological characteristics of well-differentiated papillary mesothelial tumor (WDPMT). Sixteen cases of resected WDPMTs diagnosed at the Affiliated Hospital of Qingdao University, Qingdao, China from 2017 to 2024 were collected and the clinicopathological features were retrospectively analyzed. There were 7 males amd 9 females, with a mean age of 53.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
September 2025
Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China.
To investigate the clinicopathological and genetic characteristics of monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL). The forty-two MEITL cases diagnosed in the Department of Pathology, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China from 2016 to 2022 was retrospectively analyzed. Clinical data were collected, and follow-up was performed.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
Faculty of Applied Computer Science, University of Augsburg.
Introduction: Mitotic figure (MF) density has been established as a key biomarker for certain tumors. Recently, the differentiation between atypical MFs (AMF) and normal MFs (NMFs) has gained increased interest in research, as AMFs density could be an independent biomarker. This results in the challenge of finding an automated, deterministic way to differentiate between AMFs and NMFs.
View Article and Find Full Text PDFVet Comp Oncol
September 2025
Department of Veterinary Medical Sciences (DIMEVET), University of Bologna, Ozzano dell'Emilia, Italy.
Mitotic count (MC) is a well-established prognostic factor in many canine malignancies. While standardisation efforts have improved inter-pathologist agreement regarding the morphology of mitotic figures and the size of the counting area, the selection of the tumour region for MC assessment remains to be standardised. This study aimed to evaluate the spatial distribution of the most proliferative areas in selected canine tumour types, using Ki67 immunohistochemistry, to identify optimal candidate regions for MC assessment.
View Article and Find Full Text PDF