ReviewNature reviews. Cancer2021
Designing deep learning studies in cancer diagnostics.
Review in Nature reviews. Cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 6 of them syntheses 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
165 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.Translational psychiatry · 2024Pooled it
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.Journal of medical Internet research · 2023Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- Application of Deep Learning in Cancer Prognosis Prediction Model.Technology in cancer research & treatmentPooled it
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.FEBS open bio · 2026Article
- A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Interpretable multitask model for clinical pathology image prediction and interpretation.NPJ systems biology and applications · 2026Article
- Review
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Reliable classification of polyps based on artificial intelligence: a development and validation study.EClinicalMedicine · 2026Article
- Deep learning prediction model based on multi-modal transvaginal ultrasound scan images for endometrial cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Clinical and genetic predictors of temozolomide-induced severe myelotoxicity in adult diffuse glioma: a case-control study.Journal of neuro-oncology · 2026Article
- Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types.NPJ precision oncology · 2026Article
- Thyroid cancer detection and classification using spectral imaging and artificial intelligence.Scientific reports · 2026Article
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
105 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. In this Perspective, we discuss reasons for the moderate progress and describe remedies designed to facilitate transition to the clinic. Recent, presumably influential, deep learning studies in cancer diagnostics, of which the vast majority used images as input to the system, are evaluated to reveal the status of the field. By manipulating real data, we then exemplify that much and varied training data facilitate the generalizability of neural networks and thus the ability to use them clinically. To reduce the risk of biased performance estimation of deep learning systems, we advocate evaluation in external cohorts and strongly advise that the planned analyses, including a predefined primary analysis, are described in a protocol preferentially stored in an online repository. Recommended protocol items should be established for the field, and we present our suggestions.
Indexed as
Identifiers
33514930What 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.