Abstract 4915: Optimal frequency of follow up scans on systemictherapy for advanced malignancies
Bibliographic record
Abstract
Background: Frequency of follow-up scans is specified in trials but there is little evidence to guide optimal frequency in standard therapy. Progression-free survival (PFS) generally follows first order kinetics and PFS half-lives (calculated by nonlinear regression analysis of more than 300 published PFS curves) correlated strongly with PFS medians (Stewart, AACR 2018).Method: We used the Excel formula EXP(-tn*0.693/t1/2) to calculate proportion of residual patients remaining progression-free at different time intervals, where tn is the potential time of follow-up scans (eg, every 3 weeks, 6 weeks, etc), * indicates multiplication, 0.693 is the natural logarithm of 2, and t1/2 is the PFS half-life in weeks.Results: Proportion of remaining patients expected to still remain progression-free at each subsequent scan varied with time interval between scans and with PFS half-life for the individual regimen. For example, with a PFS half-life of 4 months (17.3 weeks) and scans repeated every 6 weeks, 21% of the patients would have progressed by the first scan, 21% of the remaining patients would have progressed by the second scan at 12 weeks, etc. With PFS half-lives of 2, 4, 6, 12 and 20 months (for example), the proportion of remaining patients progressing by the time of each subsequent follow up scan if the scans were repeated every 3 weeks would be 21%, 11%, 8%, 4% and 2%, respectively, while with scans repeated every 6 weeks the proportion progressing would be 38%, 21%, 15%, 8% and 5%, by 12 weeks it would be 62%, 38%, 27%, 15% and 9%, and by 15 weeks it would be 70%, 45%, 33%, 18% and 11%, respectively.Conclusions: For therapies with short PFS half-lives, scanning every 6 weeks may not be often enough, while with long PFS half-lives, less frequent scans may be warranted. We plan analyses to estimate economic implications of varying scan frequency based on PFS half-life, drug and toxicity costs, opportunity costs (where other effective therapies might be offered if progression is detected), etc. We will also assess how this approach might be adapted for PFS curves that follow exponential 2-phase decay due to presence of distinct good vs poor prognosis subgroups, and we are in the process of using published PFS curves to assess the PFS half-lives for multiple different cancer therapies.Citation Format: David J. Stewart, Blair Macdonald, Sasha Van Katwyk, Arif Awan, Kednapa Thavorn. Optimal frequency of follow up scans on systemictherapy for advanced malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4915.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".