Abstract P008: Evaluating associations between genomic classifier and digital pathology based mutli-modal AI biomarkers in oligometastatic castration-sensitive prostate cancer
Bibliographic record
Abstract
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
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".