SynthesisBriefings in bioinformatics2024
New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.
Synthesis in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed, 22 citations in OpenAlex.
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.iScience · 2026Review
- Analysis of prognostic factors and construction of a nomogram for patients with brain metastases from cancer of unknown primary based on the SEER database.Discover oncology · 2026Article
- Principal Component-Based Comprehensive Physical Health Score Associates With Long-Term Death in Patients With Ischemic Stroke.Journal of the American Heart Association · 2026Article
- An AI Approach to Differentiating Lung Squamous Cell Carcinoma From Metastases of Other Origins.JAMA network open · 2026Article
- Bridging radiology and pathology: domain-generalized cross-modal learning for clinical.NPJ digital medicine · 2026Article
- Molecular-Guided Precision Oncology in Cancer of Unknown Primary: A State-of-the-Art Perspective.Journal of personalized medicine · 2026Review
- Association of Nontraditional Lipid Parameters With Long-Term Prognosis of Acute Ischemic Stroke: A Principal Components Analysis.Journal of the American Heart Association · 2026Article
- Tumor-on-chip's alliance with molecular pathology against metastatic disease.Journal of biomedical science · 2026Review
- GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling.Cancer research communications · 2025Article
- SurvBoard: standardized benchmarking for multi-omics cancer survival models.Briefings in bioinformatics · 2025Article
- Sequencing of high-frequency mutated genes in breast cancer (BRCA) and associated-functions analysis.International journal of clinical and experimental pathology · 2025Article
- Diagnostic Utility of a 90-Gene Expression Assay (Canhelp-Origin) for Patients with Metastatic Cancer with an Unclear or Unknown Diagnosis.Molecular diagnosis & therapy · 2025Article
- Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.International journal of molecular sciences · 2024Article
- Predicting tumour origin with cytology-based deep learning: hype or hope?Nature reviews. Clinical oncology · 2024Article
- Poorly differentiated squamous cell carcinoma of unknown primary location a case report of perineal presentation.International journal of surgery case reports · 2024Article
- Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer.Current issues in molecular biology · 2024Article
- Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies.ACS pharmacology & translational science · 2024Review
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 1 country.
Funding
Abstract
Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.
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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.