Abstract 7410: Make it better and faster: a retrospective study examining the introduction of an agnostic, reflexive, and expanded pathological protocol for lung cancer patients in Saskatchewan, Canada
Bibliographic record
Abstract
Abstract Background: Lung cancer evaluation for Saskatchewan patients involves an initial histologic or cytologic diagnosis rendered to the thoracic surgeon, followed by an oncologist consult. The oncologist initiates tissue biomarker testing for advanced stage patients at the first patient visit, and treatment guidance is determined once this is available. This multi-step process ranges 25-39 days from biopsy to treatment-critical information. We sought to reduce this delay and increase actionable mutation rates by implementing stage-agnostic, pathologist-initiated, expanded biomarker reflex testing. We also sought to determine if these changes would lead to improved times to treatment amongst the various populations throughout our unique provincial population considering pathologic testing for lung cancer patients occurs in one of the major population centers, Saskatoon. Methods: 1064 patients were included in the study and examined 18 months of data pre-and post-algorithm change. The differences between actionable mutation rate were examined using a T-test. Multivariable logistic regression models were used to estimate confidence intervals (CI) for the associations between independent factors and time to treatment from biopsy. The initial algorithm of advanced stage, parallel immunohistochemistry (IHC) for ALK, ROS, 22c3 PDL1 and DNA next-generation sequencing with ThermoFisher Oncomine for KRAS/BRAF/EGFR was updated to stage agnostic DNA triage panel for KRAS/BRAF/EGFR and PDL-1 IHC with all driver-negative patients reflexed to a ThermoFisher Oncomine Fusion panel with 49 actionable mutational targets. Results: 394 patients (pre algorithmic change) and 670 (post change) were examined. An increased detection rate of actionable mutation of 6.7% was noted with post-algorithmic changes. This included an increase of X ALK and Y ROS patients and X MET14 and NTRK patients. Biopsy to biomarker reporting decreased from 40.4 days to 29.7 days (-26%). Furthermore, prior to the implemented change, biopsy to treatment time was 30 days for patients residing near or in Saskatoon, and 37 days for those residing near or in Regina, and 28 and 30 days post algorithmic change, respectively. Conclusions: Patient biomarker results at the first oncologist visit are paramount to timely treatment of the patient. Saskatoon was able to maintain the same turnaround time (TAT) despite an increased number of patients being tested. Regina, despite being a major center, still shows an increased TAT due to the time it takes to transfer the patient material across the province. While an updated protocol increases biomarker equity for patients across the province, interventions need to be targeted to ensuring patients do not suffer untimely delay by not being in a biomarker testing center. Citation Format: Nicholas Jette, John Decoteau, Hui Wang, Darryl Yu, Mary Kinloch. Make it better and faster: a retrospective study examining the introduction of an agnostic, reflexive, and expanded pathological protocol for lung cancer patients in Saskatchewan, Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7410.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".