ArticleScientific reports2024
AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- ShadoNet: a cell detection and classification framework for Ki-67 pathology images.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence-based quantification of epidermal proliferation and apoptosis in human skin.JID innovations : skin science from molecules to population health · 2026Article
- Validation, implementation, and impact of an AI model in routine practice for pathologic diagnosis of prostate cancer in an academic medical center.Journal of pathology informatics · 2026Article
- Grading medullary thyroid carcinoma on fine needle aspiration biopsy specimens: a comparative study between manual counting and a deep learning platform.Journal of the American Society of Cytopathology · 2026Article
- Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex.Croatian medical journal · 2026Article
- Ki-67 as a treatment decision modifier in breast cancer.The oncologist · 2026Article
- Reliability of foundation models for image retrieval in histopathology.Npj imaging · 2026Article
- Evaluating the Impact of a Ki-67 Decision Support Algorithm on Pathology Residents' Scoring Accuracy.Journal of imaging informatics in medicine · 2026Article
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Lightweight self supervised learning framework for domain generalization in histopathology.Scientific reports · 2025Article
- The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?Diagnostics (Basel, Switzerland) · 2025Review
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- Evaluating the Diagnostic Potential of Biomarker Panels in Breast Cancer and Prostate Adenocarcinoma.Health science reports · 2025Article
- Determination of Ki-67 indices in neuroendocrine tumours of the gastrointestinal tract: the past, the present, and the future.Virchows Archiv : an international journal of pathology · 2025Article
- Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine.European journal of breast health · 2025Article
- Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.Scientific reports · 2025Article
- Patient outcomes and clinician perspectives following one year ofTranslational breast cancer research : a journal focusing on translational research in breast cancer · 2025Article
- Preoperative prediction of Ki-67 expression in hepatocellular carcinoma by spectral imaging on dual-energy computed tomography (DECT).Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.
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