Abstract 2619: Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data
Bibliographic record
Abstract
Abstract Obtaining information regarding cancer recurrence from a retrospective, EHR-based dataset poses several challenges primarily due to the lack of structured data. Patients are at risk for cancer recurrence beginning at a time point at which they are characterized as having no evidence of disease. The absence of cancer may be indicated on a radiology report or a medical oncologist assessment, requiring manual review and interpretation of potentially ambiguous free text. Further, the recurrence event itself can be defined based on several distinct data sources including pathology, imaging, clinician assessments, or tumor markers. The likelihood of ascertaining recurrence is dependent on the frequency and type of surveillance performed and varies based on tumor type and based on clinicians' thresholds for pursuing workup of borderline or suspicious findings; if follow up assessments are infrequent, there are fewer opportunities to detect recurrence. Given these challenges, there is currently no standardized approach to evaluating cancer recurrence in EHR data, impeding analyses of rare molecular tumor subtypes in multi-institutional linked clinico-genomic databases. For this analysis, we leveraged the AACR Project GENIE Biopharma Collaborative data based on the PRISSMM curation model to develop an algorithm for identifying recurrence among patients diagnosed with stage I-III non-small cell lung cancer or with stage I-III colorectal cancer. This algorithm involves using curated pathology report data to identify a definitive surgery as the time at which patients have completed curative intent treatment. Subsequent imaging reports, pathology reports, medical oncologist assessments and tumor marker data are then evaluated in order to characterize the timing of specific recurrence events. We will present the real-world recurrence algorithm, its underlying rationale and discuss applications of recurrence endpoints. Beyond enabling estimates of recurrence-free survival, identifying cancer recurrence will allow for estimation of progression-free survival among stage I-III patients in addition to estimation of PFS among de novo stage IV patients. Estimating PFS in a large cohort of patients with linked phenomic and genomic data has historically been a limitation of these types of datasets. Overcoming this limitation will allow for precision medicine advances in oncology by facilitating data pooling across institutions and enabling examination of rare molecular subtypes in relation to clinically meaningful endpoints. Citation Format: Jessica A. Lavery, Samantha Brown, Eva Lepisto, Michele L. Lenoue-Newton, Caroline McCarthy, Hira Rizvi, Celeste Yu, Kenneth L. Kehl, Shawn M. Sweeney, Julia E. Rudolph, Nikolaus Schultz, Brooke Mastrogiacomo, Ritika Kundra, Jeremy Warner, Philippe Bedard, Gregory J. Riely, Katherine S. Panageas, Deborah Schrag, AACR Project GENIE Consortium. Defining real-world recurrence in the AACR Project GENIE Biopharma Collaborative Data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2619.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".