The use of machine learning models to predict PFS and OS outcomes from waterfall plots in randomized clinical trials (MAP-OUTCOMES).
Notice bibliographique
Résumé
107 Background: Depth of tumor response (DepOR) of individual patients, as visualized by waterfall plots, is an emerging short-term endpoint that may represent a surrogate for survival-based outcomes such as PFS and OS. We hypothesize that the configuration of waterfall plots in randomized trials may predict PFS/OS outcomes. Methods: A literature-based search was performed for all phase II/III randomized clinical trials published in MEDLINE from 2010 to 2022 testing molecularly targeted agents (MTA) or immunotherapy (IO). Articles reporting at least 1 waterfall plot for each treatment arm depicting maximum DepOR of target lesions with corresponding PFS/OS Kaplan-Meier plots were included. Studies are defined as positive or negative based on the achievement of a priori stated primary endpoint. Trial data collected included sample size per arm, cancer type, mechanisms of action of drug(s) tested, line of treatment, etc. Images of waterfall plots were manually extracted from publications and then processed through a semi-automatic extraction process using WebPlotDigitizer and Tesseract to produce tabular representations. Logistic regression with L2 regularization was used for modeling; hyperparameter tuning was accomplished with five-fold cross-validation on a training set compromising 80% of the data. Results: A total of 111 studies were identified: 65 (59%) phase III and 46 (41%) phase II, mean sample size per arm 317 (19-1581). Most frequent cancer type was gastrointestinal 24 (22%). MTA, IO and combinations were tested in 113 (51%), 35(16%) and 11 (5%) studies respectively. Chemotherapy and other treatment regimens were used in 63 (28%) trials. PFS was the primary endpoint in 62 (56%); 80 (75%) studies were positive. Of the 111 studies only 83 (75%) were retained for machine learning analysis, the remainder were excluded due to atypical formatting such as superimposed waterfall plots. Performance of the model was assessed on a test set which comprised 20% of the original dataset. Table below shows the classification metrics from modelling. Both PPV and NPV were ≥80%. Conclusions: MAP-OUTCOMES evaluated pan-cancer randomized studies with diverse therapeutic anticancer agents. It is a computational tool with the potential to predict survival-based outcomes from waterfall plots and may help with decisions regarding follow-on randomized studies. Further validation is ongoing. [Table: see text]
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».