Abstract P008: Evaluating associations between genomic classifier and digital pathology based mutli-modal AI biomarkers in oligometastatic castration-sensitive prostate cancer
Notice bibliographique
Résumé
Abstract Purpose: Prostate cancer is a heterogeneous disease ranging from indolent localized to metastatic castration-resistance. Efforts to generate prognostic and predictive biomarkers to understand disease trajectory beyond clinical variables alone include the Decipher Prostate Genomic Classifier (GC) and Artera Multimodal AI (MMAI). Both are validated prognostic biomarkers within localized prostate cancer and are currently being evaluated in the metastatic setting. It is unknown if these biomarkers are reporting on similar biology through different means (gene expression vs digital pathology) or if they are complementary and provide orthogonal insights. Herein, we aim to correlate GC and MMAI scores in patients with metastatic prostate cancer. Methods: We conducted a retrospective review of patients with oligometastatic castration-sensitive prostate cancer (omCSPC) with available transcriptome and digital H&E images from prostate biopsy tissue. GC scores were calculated from RNA sequencing data using the same coefficients but scores were re-scaled to a reference cohort from GRID registry while missing features were imputed as 0. Following digitization of H&E slides, an AI-detected, 128 image feature vector (IFV) was generated per patient which was subsequently combined with Gleason score, PSA, and T stage for final MMAI scoring (Artera, Inc). The primary endpoint was to assess correlations between these biomarkers as continuous variables with linear regression. Given the MMAI score is composed of both AI-detected digital pathology features and clinical features, we evaluated any associations between the GC and AI-detected image features. Uniform Manifold Approximation and Projection (UMAP) was performed on the 128 IFV to generate digital pathology clusters which were then associated with GC both as a continuous and categorical variable using ANOVA and chi-square test, respectively. Results: 85 patients (Metachronous n=74; Synchronous n=11) were included in the analysis. The median GC and MMAI scores were 0.60 and 0.52, respectively. Linear regression identified a very weak positive association between scores (R2=0.08, 95%CI 0.00-0.20). UMAP identified 4 digital pathology clusters. No cluster was found to be enriched with higher GC scores with median scores of 0.64, 0.67, 0.58, and 0.5 for clusters 1-4 respectively (p=0.138). Additionally, no cluster was enriched with either low (GC <.45, p=0.87), intermediate (GC ≥ 0.45-<0.6, p=0.73), or high (GC≥0.6, p=0.12) GC risk groups. Conclusions: We demonstrate for the first time that Decipher GC and Artera MMAI scores do not strongly correlate in a population of patients with omCSPC. This suggests these biomarkers may be complementary, identifying independently prognostic disease biology. Further work validating these findings is warranted. Citation Format: Philip A. Sutera, Yang Song, Amol Shetty, Jarey Wang, Kim Van der Eecken, Alex Hakansson, Yang Liu, Adrianna Mendes, Xiaolei Shi, Elai Davicioni, Emmalyn Chen, Rikiya Yamashita, Timothy Showalter, Tamara Lotan, Theodore DeWeese, Ana Kiess, Daniel Song, Matthew Deek, Piet Ost, Phuoc Tran.Evaluating associations between genomic classifier and digital pathology based mutli-modal AI biomarkers in oligometastatic castration-sensitive prostate cancer.[abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr P008
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».