ArticleBritish journal of cancer2015
Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays.
Article in British journal of cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- BRCA1 Expression by Immunohistochemistry and Prognosis in Ovarian Cancer: A Systematic Review and Meta-Analysis.Targeted oncology · 2020Pooled it
- Unraveling the significance of AGPAT4 for the pathogenesis of endometriosis via a multi-omics approach.Human genetics · 2024Article
- Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.Frontiers in physiology · 2024Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer.NPJ breast cancer · 2022Article
- HER2-ResNet: A HER2 classification method based on deep residual network.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.Histochemistry and cell biology · 2021Article
- CD200 and CD200R1 are differentially expressed and have differential prognostic roles in non-small cell lung cancer.Oncoimmunology · 2020Article
- Evaluation of toll-like receptors as prognostic biomarkers in gastric cancer: high tissue TLR5 predicts a better outcome.Scientific reports · 2019Article
- Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides.Journal of imaging · 2019Article
- Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.PLoS medicine · 2019Article
- Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning.Computational and mathematical methods in medicine · 2019Article
- Thymidylate synthase predicts poor response to pemetrexed chemotherapy in patients with advanced breast cancer.Oncology letters · 2018Article
- Pharmacological targeting of MYC-regulated IRE1/XBP1 pathway suppresses MYC-driven breast cancer.The Journal of clinical investigation · 2018Article
- Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology.Clinical kidney journal · 2017Article
- Reduced E-cadherin facilitates renal cell carcinoma progression by WNT/β-catenin signaling activation.Oncotarget · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundTissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review.
methodsIn current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software.
resultsAlthough pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; =0.91, Quickscore; =0.92).
conclusionsThe proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.