Abstract A03: Estimating scenarios for survival time in patients with metastatic melanoma receiving immunotherapy or targeted therapy
Bibliographic record
Abstract
Abstract Background: It is important for advanced cancer patients to understand their prognosis. This allows patients to plan appropriately for end-of-life. Unfortunately, many patients do not understand their life expectancy, often overestimating their likely survival time. Estimating survival in metastatic melanoma is particularly difficult, as immunotherapy and targeted therapies extend survival time and revolutionize care. We have previously shown that three survival scenarios (worst-case, typical, best-case), calculated using simple multiples of median overall survival ([OS], 0.25x, 0.5-2x, 3x, respectively), is a useful framework to estimate and communicate survival time to advanced cancer patients. Methods: This study aimed to determine whether three survival scenarios accurately estimate prognosis for metastatic melanoma patients receiving immunotherapy or targeted therapy. We searched Medline, EMBASE, and Cochrane Central Register of Controlled Trials for phase II/III randomized controlled trials (treatment arms n ≥90) of patients with unresectable stage IIIC/IV cutaneous melanoma receiving immunotherapy or targeted therapy from January 2001 to February 2022. We extracted OS data from Kaplan Meier curves and compared it to our multiples of median OS. Results: 26 trials (12,345 patients) were included. Our estimates of worst-case scenarios ranged from 3.29 (interquartile range [IQR] 2.82-3.76) to 6.82 (IQR 4.48-18.93) months; most-likely lower-typical from 6.57 (IQR 5.64-7.52) to 13.64 (IQR 8.96-18.93) and upper-typical from 26.28 (IQR 22.58-30.07) to 54.55 (IQR 35.83-75.73) months; and best-case from 39.43 (IQR 33.87-45.11) to 81.83 (IQR 53.74-113.60) months, among patients receiving first-line targeted and immunotherapy, respectively. Our multiples of the median OS accurately estimated survival from anywhere between 16.7% to 100% of estimates. Our scenarios tended to be more accurate for those receiving targeted (most between 70% to 100% accuracy) than immunotherapy (some as low as 16.7%); and second- (all between 50% to 100%) than first-line (some as low as 16.7%) treatment. We were unable to estimate scenarios for patients receiving first-line combination immunotherapy, as none of the treatment arms in this group met median OS. When we were inaccurate, we tended to overestimate survival. Conclusions: This study was limited by small sample sizes and immature data. The accuracy of our scenarios was more variable than previous work done by our team. Future research should include mature data and novel interventions when determining frameworks to communicate survival in metastatic melanoma. Citation Format: Megan Smith-Uffen, John Park, Andrew Parsonson, Anuradha Vasista. Estimating scenarios for survival time in patients with metastatic melanoma receiving immunotherapy or targeted therapy [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A03.
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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 | 0.077 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".