How Using Dedicated Software Can Improve RECIST Readings
Bibliographic record
Abstract
Decision support tools exist for oncologic follow up. Their main interest is to help physicians improve their oncologic readings but this theoretical benefit has to be quantified by concrete evidence. The purpose of the study was to evaluate and quantify the impact of using dedicated software on RECIST readings. A comparison was made between RECIST readings without dedicated application vs. readings using dedicated software (Myrian® XL-Onco, Intrasense, France) with specific functionalities such as 3D elastic target matching and automated calculation of tumoral response. A retrospective database of 40 patients who underwent a CT scan follow up was used (thoracic/abdominal lesions). The reading panel was composed of two radiologists. Reading times, intra/inter-operator reproducibility of measurements and RECIST response misclassifications were evaluated. On average, reading time was reduced by 49.7% using dedicated software. A more important saving was observed for lung lesions evaluations (63.4% vs. 36.1% for hepatic targets). Inter and intra-operator reproducibility of measurements was excellent for both reading methods. Using dedicated software prevented misclassifications on 10 readings out of 120 (eight due to calculation errors). The use of dedicated oncology software optimises RECIST evaluation by decreasing reading times significantly and avoiding response misclassifications due to manual calculation errors or approximations.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".