PD27-10 TEMPORAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE TOOL (SEPERA) TO INFORM NERVE-SPARING STRATEGY DURING RADICAL PROSTATECTOMY AND COMPARISON AGAINST UROLOGISTS
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Résumé
You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence II (PD27)1 May 2024PD27-10 TEMPORAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE TOOL (SEPERA) TO INFORM NERVE-SPARING STRATEGY DURING RADICAL PROSTATECTOMY AND COMPARISON AGAINST UROLOGISTS Lauren Pickel, Kevin Zhang, Ryan Booth, Aiman Shahid, Maximiliano Ringa, Amna Ali, Amy Chan, Nathan Perlis, Robert J. Hamilton, Neil E. Fleshner, Antonio Finelli, Alistair E. W. Johnson, Girish S. Kulkarni, Andrew Feifer, Alexandre R. Zlotta, and Jethro C. C. Kwong Lauren PickelLauren Pickel , Kevin ZhangKevin Zhang , Ryan BoothRyan Booth , Aiman ShahidAiman Shahid , Maximiliano RingaMaximiliano Ringa , Amna AliAmna Ali , Amy ChanAmy Chan , Nathan PerlisNathan Perlis , Robert J. HamiltonRobert J. Hamilton , Neil E. FleshnerNeil E. Fleshner , Antonio FinelliAntonio Finelli , Alistair E. W. JohnsonAlistair E. W. Johnson , Girish S. KulkarniGirish S. Kulkarni , Andrew FeiferAndrew Feifer , Alexandre R. ZlottaAlexandre R. Zlotta , and Jethro C. C. KwongJethro C. C. Kwong View All Author Informationhttps://doi.org/10.1097/01.JU.0001008580.58088.27.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Accurate prediction of side-specific extra-prostatic extension (ssEPE) is crucial to inform nerve-sparing (NS) strategy. We recently developed an artificial intelligence (AI) model, SEPERA (Side-specific Extra-Prostatic Extension Risk Assessment), to predict risk of ssEPE and validated it on an international, multi-institutional cohort of patients from 2008-2020. As ongoing validation of AI models is key, we aimed to further assess SEPERA and compare its recommendations against urologists. METHODS: Temporal validation was performed on 695 patients undergoing radical prostatectomy at two academic and community hospitals in Canada from 2020-2022. Patients that received prior radiation or hormone therapy were excluded. Primary outcome was presence of ssEPE on prostatectomy. SEPERA was compared to a logistic regression (LR) model from the original study, which included identical variables as SEPERA. Models were assessed based on discrimination, calibration, and decision curve analysis at decision thresholds between 0-30%, as commonly used in other studies. NS strategy at the time of surgery was compared to SEPERA's recommendations (complete, partial, or minimal NS). Proportion of cases with ssEPE and positive surgical margins (ssPSM) was examined. RESULTS: ssEPE was found in 467 out of 1390 prostatic lobes (34%). SEPERA performed similar to the original study with AUROC 0.75 (95% CI 0.73-0.78) and AUPRC 0.63 (95% CI 0.58-0.67). SEPERA outperformed LR, which achieved AUROC 0.73 (95% CI 0.70-0.75, p=0.002) and AUPRC 0.61 (95% CI 0.56-0.65, p=0.02). SEPERA was well-calibrated for risks between 15-45% and had higher net benefit compared to LR for clinically relevant risk thresholds between 15-25%. SEPERA's recommendations differed from clinical decisions in 49% of cases (Figure. 1). Where SEPERA recommended "Minimal" NS but more was performed, 52% of cases had ssEPE and 35% had ssPSM. Where SEPERA recommended "Complete" NS but less was performed, only 13% of cases had ssEPE and 3% had ssPSM. CONCLUSIONS: We further show the generalizability of SEPERA in both academic and community settings. SEPERA has the potential to improve pathological outcomes, particularly if decisions are guided by SEPERA in cases where "Minimal" NS is recommended. Download PPT Source of Funding: This project was supported by the Temerty Centre for AI Research and Education in Medicine Summer Research Studentship © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e554 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Lauren Pickel More articles by this author Kevin Zhang More articles by this author Ryan Booth More articles by this author Aiman Shahid More articles by this author Maximiliano Ringa More articles by this author Amna Ali More articles by this author Amy Chan More articles by this author Nathan Perlis More articles by this author Robert J. Hamilton More articles by this author Neil E. Fleshner More articles by this author Antonio Finelli More articles by this author Alistair E. W. Johnson More articles by this author Girish S. Kulkarni More articles by this author Andrew Feifer More articles by this author Alexandre R. Zlotta More articles by this author Jethro C. C. Kwong More articles by this author Expand All Advertisement PDF downloadLoading ...
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,066 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».