ReviewDiagnostics (Basel, Switzerland)2021
Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Deciphering the Multiscale Morphology of Somatic Oncogenic Alterations in Hepatocellular Carcinoma.bioRxiv : the preprint server for biology · 2026Article
- Construction and validation of a predictive model for drug resistance in clear cell renal cell carcinoma based on the heterogeneous characteristics of pathological omics.American journal of translational research · 2026Article
- Introducing the contextual digital divide: Insights from microscopic anatomy on usage behavior and effectiveness of digital versus face-to-face learning.Anatomical sciences education · 2025Article
- Review
- Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information.Discover oncology · 2025Article
- Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.The journal of pathology. Clinical research · 2024Article
- Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.Nature communications · 2024Article
- Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- HistoMIL: A Python package for training multiple instance learning models on histopathology slides.iScience · 2023Article
- Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.iScience · 2023Article
- Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.Scientific reports · 2023Article
- Multimodal deep learning to predict prognosis in adult and pediatric brain tumors.Communications medicine · 2023Article
- Artificial intelligence for multimodal data integration in oncology.Cancer cell · 2022Review
- Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.Clinical and molecular hepatology · 2022Review
- Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.Sensors (Basel, Switzerland) · 2022Article
- Estimation with Uncertainty via Conditional Generative Adversarial Networks.Sensors (Basel, Switzerland) · 2021Article
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.
Funding
Abstract
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.
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.