Molecular testing and patterns of treatment in patients with NSCLC: An IASLC analysis of ASCO CancerLinQ Discovery Data.
Bibliographic record
Abstract
9128 Background: Precision medicine has resulted in improved outcomes for non-small cell lung cancer (NSCLC); while molecular testing is considered critical for guiding treatment decisions for advanced stage NSCLC, adoption of testing in routine practice is variable. We analyzed the factors contributing to molecular testing and treatment patterns in patients with lung cancer. Methods: The ASCO CancerLinQ Discovery dataset was queried to identify patients diagnosed with lung cancer between the years 2010-2018. Data on demographics, tumor stage, histology and treatments were extracted, and receipt of molecular testing was investigated as the primary outcome. Univariate association of each clinicopathological variable with molecular testing outcome was performed using chi-square test for categorical variables and ANOVA test for numerical variables. A multivariable logistic regression analysis with backward selection at an alpha of 0.05 was reported. All analyses were conducted using SAS 9.4. Results: A total of 37,925 NSCLC patients with stage IV disease were analyzed. Patient characteristics: median age 65 years, 51% male, 68% white, 33.5% adenocarcinoma. Approximately 22% of all NSCLC patients had molecular testing results. In adenocarcinoma patients, 49% had molecular testing results available. In the stage IV group, 47% were treated with chemotherapy, 16% with immunotherapy and 3% with targeted therapy. On multivariable analysis, females were more likely to have molecular testing compared to males [(OR: 1.29 (1.22-1.37); p < 0.001]. Compared to White patients, Black patients were less likely to have molecular testing [OR: 0.89 (0.81-0.97); p = 0.009] and Asians were more likely to undergo testing [OR: 2.22 (1.79-2.75); p < 0.001]. Hispanic patients were more likely to undergo molecular testing compared to non-Hispanics [OR:1.24 (1.02-1.52); p = 0.03]. Additionally, treatment with immunotherapy [OR: 1.86 (1.72-2.01); p < 0.001] and targeted therapy [OR: 2.29 (2.00-2.64); p < 0.001] were associated with significantly higher likelihood of having molecular testing. These results were also confirmed on a subgroup analysis of adenocarcinoma patients. Conclusions: In this analysis of a US-based real-world dataset of stage IV NSCLC patients, White race and female sex are associated with higher likelihood of having molecular test performed. The percentage of patients undergoing testing remains sub-optimal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".