ArticleCancer discovery2024
Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.
Article in Cancer discovery, 2024. 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.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Beyond origin: multimodal AI synthesis to resolve cancers of unknown primary.Cancer biology & medicine · 2026Article
- Management and survival of patients with cancer of unknown primary discussed by a French national multidisciplinary tumour board: a retrospective analysis.The Lancet regional health. Europe · 2026Article
- TumorOriginPredictor: A Clinically Validated and Cloud-Deployed AI Platform for Tissue-of-Origin Identification in Cancer of Unknown Primary Using Somatic Mutation Profiles.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Automated classification of lung cancer subtypes cells using microscopic images and ensembled deep learning architectures.Scientific reports · 2025Article
- Pathologist-initiated whole genome and transcriptome sequencing demonstrates diagnostic utility in resolving difficult-to-diagnose tumors.Genome medicine · 2025Article
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Large language models enable tumor-type classification and localization of cancers of unknown primary from genomic data.Cell reports. Medicine · 2025Article
- A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.Genes · 2025Review
- Explainable AI Model Reveals Informative Mutational Signatures for Cancer-Type Classification.Cancers · 2025Article
- Whole genome sequencing improves tissue-of-origin diagnosis and treatment options for cancer of unknown primary.Nature communications · 2025Article
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.Cancer cell · 2025Article
- AI in optimized cancer treatment: laying the groundwork for interdisciplinary progress.Oxford open immunology · 2025Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.
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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.