Prostate cancer risk in African American men evaluated via digital histopathology multi-modal deep learning models developed on NRG Oncology phase III clinical trials.
Notice bibliographique
Résumé
108 Background: Artificial intelligence (AI) tools can display racial bias as a result of existing systemic health inequities and biased datasets. We have previously developed multi-modal AI (MMAI) prognostic models based on digital pathology images from five phase III randomized radiotherapy prostate cancer trials that outperform NCCN risk groups for prediction of distant metastasis (DM), biochemical failure (BF), prostate cancer-specific mortality (PCSM) and all-cause mortality (OS). In this study, we assessed the algorithmic fairness of the locked MMAI models between African American (AA) and non-AA populations in the five randomized trials. Methods: Patients enrolled in NRG/RTOG 9202, 9408, 9413, 9910, and 0126 with digitized biopsy histopathology slides were included in this study. The locked MMAI models were applied, and subgroup analyses were conducted by comparing distributions of clinical variables and MMAI scores (medians for continuous variables and proportions for categorical variables reported), and evaluating MMAI models’ prognostic ability among AA and non-AA men. The performance of the models were compared using DM as the primary endpoint and secondary endpoints of BF, PCSM, OS (death without an event as a competing risk) with Fine-Gray or Cox Proportional Hazards models. Either Kaplan Meier or cumulative incidence estimates were computed and compared using log-rank or Gray’s test. Results: This study included 5,624 men: 932 (17%) AA, 4503 (80%) white, and 189 (3%) other races. AA had younger median age (69 vs 71 year [yr]), higher median baseline PSA (12 vs 10 ng/mL), more T1-T2a (62% vs 57%), more Gleason < 7 (42% vs 36%) and 8-10 (15% vs 12%), and more NCCN low and high risk (12% vs 10% and 41% vs 33%). AA and non-AA had estimated 5-yr BF rates 27% and 27%, 5-yr DM rates 5% and 5%, 10-yr PCSM 5% and 7%, and 10-yr OS 58% and 60%, respectively. The median (interquartile range) score of the model optimizing for 5-yr DM (5-yr DM MMAI) was 0.044 (0.037–0.059) in AA and 0.043 (0.036–0.057) in non-AA. Similarly, all other MMAI models had differences in the medians between AA and non-AA ranging from 0.001 to 0.02. For all endpoints, the 5-yr DM MMAI model showed strong prognostic signal (hazard ratio [HR] per one standard deviation increase: 1.6 for DM, 1.4 for BF, 1.6 for PCSM and 1.3 for OS, all p-values < 0.001) and had comparable trends within AA vs. non-AA in the entire cohort (e.g., HR for DM 1.4 vs 1.6). Similar results were observed for the MMAI model optimizing for 10-yr PCSM. Conclusions: To our knowledge, this represents the first comparative analyses of a digital pathology AI prognostic model in AA vs. non-AA prostate cancer patients. The prognostic performance of the AI models was found to be comparable between subgroups. Our data supports the use of these models across racial groups, though further validation in AA cohorts is ongoing.
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 | 0,055 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,011 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».