Abstract 4784: PATHFINDER 2: A prospective study to evaluate safety and performance of a multi-cancer early detection test in a population setting
Bibliographic record
Abstract
Abstract Background: Multi-cancer early detection (MCED) tests are being established as a novel approach to screen for multiple cancer types with one test. A blood-based MCED test using cell-free DNA targeted methylation patterns to detect a shared cancer signal and predict a cancer signal origin (CSO) has demonstrated feasibility. An initial return of results study (PATHFINDER; NCT04241796) reported performance of a refined version of the MCED test, including a 43.1% positive predictive value (PPV), 98.5% negative predictive value (NPV), 99.5% specificity, and 88.0% CSO accuracy. To build on this work, the PATHFINDER 2 study (NCT05155605) will evaluate safety and performance of the MCED test in larger, more diverse populations. Methods: PATHFINDER 2 is a prospective, multicenter, interventional study. Participants ≥50 years of age will be enrolled, with specific targets to improve diversity (age, sex, race/ethnicity) and few exclusions due to comorbid conditions. Exclusion criteria include current clinical suspicion of cancer or recent cancer/treatment (within 3 years). Participants will undergo blood draw for MCED testing, followed by return of results (cancer signal detection and CSO) to the investigator and diagnostic evaluations if a cancer signal is detected. A confirmatory PET-CT scan will be performed when CSO-directed workups do not result in a cancer diagnosis. Participants will be followed for approximately 3 years. Primary endpoints include 1) MCED test safety in terms of diagnostic testing triggered by a positive result (number/type of procedures and adverse events) and 2) test performance (PPV, NPV, specificity, sensitivity, CSO accuracy, cancer detection rate and number needed to screen). Secondary endpoints include participant reported outcomes (eg, anxiety), utilization of guideline-recommended cancer screening, and cancer detection rate of confirmatory PET-CT, amongst others. The study plans to enroll approximately 35,000 participants across North America. Sites were selected based on geographic location, catchment area demographics, and practice setting (academic vs non-academic). Enrollment targets for age, sex and race/ethnicity (White [non-Hispanic] 72%; Hispanic or Latino 11%; African American or Black 11%; Asian, Native Hawaiian or Other Pacific Islander 6%; American Indian/Alaska Native 1%) were derived from US Census data. Strategies to promote diverse recruitment include translation of participant-facing documents across multiple languages, participant and healthcare provider educational material, and site-led community outreach campaigns. MCED testing is an emerging, potentially paradigm-changing strategy in cancer screening, and PATHFINDER 2 is designed to characterize the safety, performance, and clinical implementation of the MCED test as a screening tool in a broad and representative population. Citation Format: Karthik V. Giridhar, Michael J. Demeure, Raymond H. Kim, Justin A. Chen, Shirish Gadgeel, Dax Kurbegov, Margarita Lopatin, Roland Matthews, Marc Matrana, Charles McDonnell, Donald Richards, Benjamin Rybicki, Gretchen Stipec, Nima Nabavizadeh. PATHFINDER 2: A prospective study to evaluate safety and performance of a multi-cancer early detection test in a population setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4784.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".