Tumour growth kinetics assessment: added value to RECIST in cancer patients treated with molecularly targeted agents
Bibliographic record
Abstract
BACKGROUND: Treatment effect is categorised into four classes by RECIST based on the evolution of the size of target lesions and the occurrence of new lesions, irrespective of tumour growth kinetics before treatment. This study aimed at evaluating the added value of tumour growth kinetics assessment to RECIST in patients treated with molecularly targeted agents (MTAs). METHODS: On-study imaging, along with pre-baseline imaging, of patients treated with MTA(s) in clinical trials at Institut Curie were centrally reviewed. The tumour growth ratio (TGr), defined as the ratio of the slope of tumour growth before treatment and the slope of tumour growth on treatment between the nadir and disease progression, was calculated for each patient. RESULTS: A total of 50 patients included in 18 trials were eligible for the study. Among the 44 patients who withdrew from the study because of disease progression according to the investigators' assessment, 18 patients (41%) had a TGr <0.9. Among these 18 patients, 5 had disease progression according to RECIST 1.1 based on our retrospective reassessment of on-study imaging and occurrence of no new lesion during study treatment. CONCLUSION: Our preliminary results suggest that a substantial proportion of patients treated with MTAs have discontinued treatment although being potentially benefitted from them.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".