ArticleJournal of proteome research2021
Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.
Article in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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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.
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Who cites it
29 citing papers in PubMed, 54 citations in OpenAlex.
- Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.BMC anesthesiology · 2023Trial
- Artificial intelligence and multi-omics integration in liquid biopsy for genitourinary cancers: a systematic scoping review.International urology and nephrology · 2026Article
- Potential urine NMR Metabolomic signatures for early diagnosis of paediatric tuberculosis.Scientific reports · 2026Article
- The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review.Burns & trauma · 2026Review
- Investigating stage-specific metabolic alterations in colorectal cancer through urine metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- A Taguchi Design of Experiments Approach for Untargeted Metabolomics Sample Preparation Optimization.Journal of biomolecular techniques : JBT · 2025Article
- Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma.Journal of clinical medicine · 2025Review
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Efficient extraction via titanium organic frameworks facilitates in-depth profiling of urinary exosome metabolite fingerprints.Analytical and bioanalytical chemistry · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma.BMC cancer · 2025Article
- Volatile Organic Metabolites as Potential Biomarkers for Genitourinary Cancers: Review of the Applications and Detection Methods.Metabolites · 2025Review
- MSMCE: A novel representation module for classification of raw mass spectrometry data.PloS one · 2025Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.Journal of the American Society for Mass Spectrometry · 2024Article
- SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.Analytical chemistry · 2024Article
- Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring.Frontiers in oncology · 2024Review
- Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics.bioRxiv : the preprint server for biology · 2023Article
- Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.Analytical chemistry · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.
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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.