ArticleThe Lancet. Digital health2024
Deep learning for [
Article in The Lancet. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06829147 (Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images), which is not on this map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
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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.
Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
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- Time-Driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer.Annals of biomedical engineering · 2026Article
- Evaluating convolutional neural network models for automated abdominal aortic calcification scoring in chronic kidney disease patients across multiple centers.Quantitative imaging in medicine and surgery · 2026Article
- Interpretable deep learning radiomics fromBMC medical imaging · 2026Article
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- Multi-view deep learning for automated lymphoma staging fromEJNMMI research · 2026Article
- Article
- Global and regional accuracy of deep learning-based tumor segmentation from whole-body [EJNMMI research · 2025Article
- An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma viaBMC medical informatics and decision making · 2025Article
- Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.European journal of nuclear medicine and molecular imaging · 2025Article
- Advances and challenges in precision imaging.The Lancet. Oncology · 2025Review
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Review
- Vision transformer to differentiate between benign and malignant slices inScientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundThe rising global cancer burden has led to an increasing demand for imaging tests such as [
methodsIn this retrospective analysis we collected 16 583 [
findingsIn the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937).
interpretationDeep learning accurately distinguishes between [
fundingNational Institutes of Health-National Cancer Institute Cancer Center Support Grant.
Indexed as
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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.