The Use of Molecular Tools for Identifying and Guiding Treatment of Cancers of Unknown Primary: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cancer of unknown primary (CUP) represents a significant clinical challenge due to its heterogeneity and the poor prognosis often associated with the disease. Molecular profiling has emerged as a promising approach to address the challenges associated with CUP. This systematic review evaluates the existing evidence on the value of different types of molecular tools for CUP diagnosis and treatment. METHODS: MEDLINE, EMBASE and Cochrane Database of Systematic Reviews published between 2013 and 2024 were searched for shortlisting eligible studies, relating to the use of molecular profiling tests in clinical management of CUP patients. Eight studies are included in this review. RESULTS: Among 1556 publications from the literature search, four randomized controlled trials (RCTs), one comparative study, two single-arm studies reporting comparative data, and one diagnostic study were included. The certainty of the aggregate evidence ranged from low to very low for the studies, as assessed by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Studies using contemporary methods to determine tumour of origin or tumour agnostic actionable mutations demonstrated the positive impact on survival of CUP patients with access to targeted therapy and immunotherapy. CONCLUSIONS: This systematic review highlights the complexities of the existing literature in patients with CUP. The published impact of molecular profiling tools on survival outcomes by guiding treatment has been limited due to study design; however, improved survival has been shown in patients who have received immunotherapy or targeted therapy. The results from future RCTs or high-quality comparative studies will clarify the role of molecular profiling tools in patients with CUP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".