Lessons from a National Liquid Biopsy Program to Provide Cancer Testing and Treatment for Patients with Advanced Solid Tumors
Bibliographic record
Abstract
Personalized cancer treatment depends on the accurate and timely detection of the patient tumor variants. LBx enables minimally invasive tumor mutation profiling. We report results of a pan-Canadian LBx program for patients with advanced solid tumors. Plasma samples were tested at Imagia Canexia Health accredited laboratory using the clinically validated Follow It 38-gene panel. A proprietary platform was used to identify clinically relevant variants in the circulating tumor DNA and report results following accepted international guidelines on clinical significance. A total of 4229 eligible patients submitted samples for LBx testing, and reports for 97% of them were delivered within ~8 days. More than 80% of Canadian oncologists from >150 institutions across 12 provinces (11% from rural centers) participated in the project. The patient cohort consisted mostly of advanced or metastatic lung, breast, and colon cancers. ctDNA mutations were detected in >50% of cases, and clinical trials were recommended for 76% of all participants. Health economics modeling analysis found that Follow It® in combination with tissue biopsy was cost-saving and resulted in an additional 0.1138 QALYs gained relative to tissue biopsy alone. The successful pan-Canadian implementation of a cost-effective, robust LBx testing program demonstrated its sustained demand and feasibility, and its potential economic and health benefits.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".