ArticlePloS one2020
Predicting cancer origins with a DNA methylation-based deep neural network model.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach.JMIR formative research · 2026Article
- Article
- A deep-learning model for detecting choroidal metastases and predicting primary tumors from ultra-widefield fundus imaging.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci.Journal of neuropathology and experimental neurology · 2025Article
- Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation.NPJ precision oncology · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.International journal of clinical oncology · 2024Article
- Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary.Neoplasia (New York, N.Y.) · 2024Article
- Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases.Scientific reports · 2024Article
- The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review.Virchows Archiv : an international journal of pathology · 2024Review
- Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns.Biology methods & protocols · 2024Article
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
- methylClass: an R package to construct DNA methylation-based classification models.Briefings in bioinformatics · 2023Article
- Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer.Scientific reports · 2023Article
- Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.Journal of molecular cell biology · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cancer origin determination combined with site-specific treatment of metastatic cancer patients is critical to improve patient outcomes. Existing pathology and gene expression-based techniques often have limited performance. In this study, we developed a deep neural network (DNN)-based classifier for cancer origin prediction using DNA methylation data of 7,339 patients of 18 different cancer origins from The Cancer Genome Atlas (TCGA). This DNN model was evaluated using four strategies: (1) when evaluated by 10-fold cross-validation, it achieved an overall specificity of 99.72% (95% CI 99.69%-99.75%) and sensitivity of 92.59% (95% CI 91.87%-93.30%); (2) when tested on hold-out testing data of 1,468 patients, the model had an overall specificity of 99.83% and sensitivity of 95.95%; (3) when tested on 143 metastasized cancer patients (12 cancer origins), the model achieved an overall specificity of 99.47% and sensitivity of 95.95%; and (4) when tested on an independent dataset of 581 samples (10 cancer origins), the model achieved overall specificity of 99.91% and sensitivity of 93.43%. Compared to existing pathology and gene expression-based techniques, the DNA methylation-based DNN classifier showed higher performance and had the unique advantage of easy implementation in clinical settings. In summary, our study shows that DNA methylation-based DNN models has potential in both diagnosis of cancer of unknown primary and identification of cancer cell types of circulating tumor cells.
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.