Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localized scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Regions of tumor in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883, respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration. © 2016 International Society for Advancement of Cytometry.
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NPJ Precis Oncol
January 2025
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian carcinoma morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge).
View Article and Find Full Text PDFAnn Diagn Pathol
January 2025
Department of Pathology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand. Electronic address:
The 2021 World Health Organisation classification of lung adenocarcinoma is based on the predominance and percentage of high-grade histological patterns, e.g. solid and micropapillary patterns, determined by semiquantitative estimation.
View Article and Find Full Text PDFRinsho Shinkeigaku
January 2025
Department of Neurology, Chiba Rosai Hospital.
Figures are essential components of case reports, often conveying information more effectively than the text. Common figure types include images, pathology slides, photographs, schematic drawings, and clinical courses. Each figure type should follow four design principles: alignment, repetition, proximity, and contrast.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular WSI datasets. However, it still has two major limitations: (i) without considering the relations among patches, thereby possibly restricting the model performance; (ii) unable to handle retrieval tasks, which is very important in clinic diagnosis. To overcome these limitations, in this paper, we propose a novel end-to-end MIL-based deep hashing framework, which is composed of a multi-scale representation attention based deep network as the backbone, patch-based dynamic graphs and hashing encoding layers, to simultaneously handle classification and retrieval tasks.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin, China.
Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task.
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