Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Improving the concordance of human epidermal growth factor receptor 2 (HER2) examinations among laboratories remains a challenge. In this multi-laboratory study, we investigated the concordance of HER2 immunohistochemistry (IHC) examination through manual and artificial intelligence (AI)-assisted interpretation.
Methods: A tissue microarray (TMA) comprising 53 breast cancer samples was constructed and distributed to 35 participating laboratories. For each sample on every slide, IHC scores of 0, 1+, 2+, and 3+ were recorded. Subsequently, cases that failed to achieve complete agreement during manual interpretation were re-evaluated using an AI-assisted microscope.
Results: During manual interpretation, 14 out of 53 cases (14/53, 26.4%) demonstrated concordant results across all laboratories, including 13 IHC-0 cases and 1 IHC-3+ case. Notably, cases scored as 1+ in at least one laboratory exhibited a low overall percentage agreement (OPA) and Fleiss Kappa value. Among the 39 cases with non-concordant manual interpretation, 14 cases (14/39, 35.9%) achieved complete agreement through AI-assisted HER2 interpretation. In cases where manual interpretation discrepancies were restricted to scores of 0 and 1+, 69.6% (16/23) of the cases still showed differences between 0 and 1+ in AI-assisted HER2 interpretation. Disagreements between manual and AI-assisted interpretation occurred significantly more frequently in sections manually scored as 1+ compared to those scored as 0 (58.6% . 2.1%, P<0.001).
Conclusions: The weakly staining phenotype leads to poor agreement in the manual interpretation of HER2 IHC-1+ breast cancers. AI-assisted HER2 interpretation offers a viable approach for multi-laboratory studies, effectively avoiding the subjective errors inherent in manual interpretation.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261348 | PMC |
| http://dx.doi.org/10.21037/gs-2024-560 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Use of artificial intelligence for classification of fractures around the elbow in adults according to the 2018 AO/OTA classification system.
BMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: A Multicenter Study.
JMIR Med Inform
September 2025
Department of Radiology, Air Force Medical Center, Air Force Medical University, Fucheng Road 30, Haidian District, Beijing, CN.
Background: Lateral malleolar avulsion fracture (LMAF) and subfibular ossicle (SFO) are distinct entities that both present as small bone fragments near the lateral malleolus on imaging, yet require different treatment strategies. Clinical and radiological differentiation is challenging, which can impede timely and precise management. On imaging, magnetic resonance imaging (MRI) is the diagnostic gold standard for differentiating LMAF from SFO, whereas radiological differentiation on computed tomography (CT) alone is challenging in routine practice.
View Article and Find Full Text PDFThe Applied Sport Science and Medicine of Powerlifting and Para Powerlifting: A Systematic Scoping Review with Recommendations for Future Research.
Sports Med
September 2025
School of Behavioural and Health Sciences, Australian Catholic University, McAuley at Banyo, Brisbane, Australia.
Background: Powerlifting is a strength sport featuring some of the world's strongest athletes. Recent decades have seen an exponential increase in research into the applied sport science and medicine of powerlifting and its Paralympic counterpart, para powerlifting. A scoping review of the area would provide athletes, coaches, policymakers, and researchers with an overview of the existing evidence to support performance, reduce injury, and foster further growth of these sports.
View Article and Find Full Text PDFValidation of uPath HER2 dual-colour dual in-situ hybridisation image analysis tool for HER2/neu testing in breast cancer.
J Clin Pathol
September 2025
Department of Pathology, Tata Memorial Center, Homi Bhabha National Institute (HBNI), Mumbai, Maharashtra, India
Aims: gene is amplified in 15%-20% of invasive breast cancers (IBCs), serving as critical prognostic and predictive marker. -targeted therapies have improved outcomes for -positive patients, highlighting the importance of accurate assessment. Immunohistochemistry is commonly used for screening overexpression, with equivocal cases reflex tested using in situ hybridisation (ISH) methods like fluorescence (FISH) or dual-colour dual ISH (D-DISH).
View Article and Find Full Text PDFTransfer Learning Based Deep Learning Approach for Knee Osteoarthritis Grading Using Modified XceptionNet Architecture.
J Vis Exp
August 2025
Professor & Head, Department of Artificial Intelligence and Machine Learning, K S Institute of Technology.
Knee osteoarthritis (KOA) affects millions of individuals worldwide and has no known curative treatment, making it a serious global health concern. The management of its development depends on early discovery, and X-ray imaging is a fundamental diagnostic technique. However, due to variations in radiologists' levels of experience, manual X-ray interpretation increases variability and possible inaccuracies.
View Article and Find Full Text PDF