ArticleScientific reports2023
Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 29 citations in OpenAlex.
- Computationally Derived Spatial Immune Signature Identifies Trastuzumab Responders in HER2+ Breast Cancer: NSABP B-41 Clinical Trial Validation.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts.Journal of pathology informatics · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics.The journal of pathology. Clinical research · 2026Article
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- A histopathology image-based computer-aided classification study for oral squamous cell carcinoma.Frontiers in oncology · 2026Article
- Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions.Nature communications · 2025Article
- Histopathological classification of colorectal cancer based on domain-specific transfer learning and multi-model feature fusion.Scientific reports · 2025Article
- Transesophageal echocardiography: Revolutionizing perioperative cardiac care.Biomolecules & biomedicine · 2025Review
- Article
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- A novel machine learning prediction model for metastasis in breast cancer.Cancer reports (Hoboken, N.J.) · 2024Article
- Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling.Nature machine intelligence · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.
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.