ReviewNPJ breast cancer2023
Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.
Review in NPJ breast cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 57 citations in OpenAlex.
- Breast Cancer: Epidemiology, Molecular Classification, Diagnostics and Evolving Treatment Paradigms.Molecules (Basel, Switzerland) · 2026Review
- Multi-omics analysis identifies GINS1 as a prognostic biomarker in lung adenocarcinoma, linked to macrophage polarization and tumor cell survival.Discover oncology · 2026Article
- Benchmarking foundation models as feature extractors for weakly supervised computational pathology.Nature biomedical engineering · 2026Article
- Decoding the breast cancer microenvironment by spatial multi-omics: from architecture to clinical translation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images.NPJ breast cancer · 2026Article
- Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict HER2-negative breast cancer subtypes: a multicenter study.BMC medical imaging · 2026Article
- Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes.Nature communications · 2026Article
- Multi-Scale Transcriptomic Sequencing Data Analysis RevealsOncology research · 2026Article
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Adaptive compression framework for giga-pixel whole slide images.Nature communications · 2025Article
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- Computational pathology in precision oncology: Evolution from task-specific models to foundation models.Chinese medical journal · 2025Review
- 3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.Journal of neuropathology and experimental neurology · 2025Article
- Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.Journal of pathology informatics · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.Clinical and experimental medicine · 2025Article
- Identification of novel potential biomarkers using bulk RNA and single cells to build a neural network model for diagnosis of liver cancer.Discover oncology · 2025Article
- DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.BMC bioinformatics · 2025Article
- Computational Pathology Detection of Hypoxia-Induced Morphologic Changes in Breast Cancer.The American journal of pathology · 2025Article
- SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Breast cancer remains a highly prevalent disease with considerable inter- and intra-tumoral heterogeneity complicating prognostication and treatment decisions. The utilization and depth of genomic, transcriptomic and proteomic data for cancer has exploded over recent times and the addition of spatial context to this information, by understanding the correlating morphologic and spatial patterns of cells in tissue samples, has created an exciting frontier of research, histo-genomics. At the same time, deep learning (DL), a class of machine learning algorithms employing artificial neural networks, has rapidly progressed in the last decade with a confluence of technical developments - including the advent of modern graphic processing units (GPU), allowing efficient implementation of increasingly complex architectures at scale; advances in the theoretical and practical design of network architectures; and access to larger datasets for training - all leading to sweeping advances in image classification and object detection. In this review, we examine recent developments in the application of DL in breast cancer histology with particular emphasis of those producing biologic insights or novel biomarkers, spanning the extraction of genomic information to the use of stroma to predict cancer recurrence, with the aim of suggesting avenues for further advancing this exciting field.
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.