Clinical Utility of Multicancer Detection in Symptomatic Patients: A Decision-Making Perspective
Bibliographic record
Abstract
PURPOSE: There is growing interest in multicancer detection (MCD) blood tests for diagnosing patients with cancer-related symptoms. However, recent studies suggest that MCD testing may not be sensitive enough to rule out cancer in the symptomatic population without retraining the underlying classifiers. On the basis of clinical guidelines for suspected cancer referral, here we cast these data into a formal diagnostic decision-making perspective to assess clinical utility. METHODS: Data were extracted from the SYMPLIFY study (ISRCTN10226380), which evaluated the performance of the Galleri test (GRAIL, LLC). The decision threshold for suspected cancer referral was extracted from the National Institute for Health and Care Excellence Guideline 12. Clinical utility was estimated using Bayesian decision curve analysis. RESULTS: For the guideline-derived decision threshold of 3%, the Galleri MCD test avoided 18,005 unnecessary suspected cancer referrals per 100,000 symptomatic patients, with a 99.4% posterior probability of clinical utility. High probabilities of clinical utility were observed for gynecologic, lower GI, and upper GI referral pathways, avoiding between 25,414 and 62,501 unnecessary referrals per 100,000 symptomatic patients. The rapid diagnostic center and lung referral pathways showed negligible probabilities of clinical utility. The minimum diagnostic performance required for clinical utility varied significantly across referral pathways. The gynecologic pathway showed the lowest sensitivity requirement (under 30% for a highly specific test) and the lung pathway the highest (over 90% for any specificity level). CONCLUSION: Clinical utility of MCD testing for symptomatic patients in the United Kingdom varies substantially across referral pathways but is favorable for gynecologic and GI cancers. Future pathway-specific optimization of MCD tests must consider clinical utility explicitly and does not require retraining the underlying machine learning classifiers.
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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 | 0.064 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".