Radiogenomic characterization of multifocal prostate cancer.
Bibliographic record
Abstract
126 Background: Up to 20% of patients with negative multiparametric magnetic resonance imaging (MRI) harbor Gleason score ≥7 prostate cancer (PCa). We sought to elucidate the molecular basis of and determine the prognostic significance of PCa visibility on MRI. Methods: We identified a retrospective cohort of patients who underwent MRI prior to prostatectomy with both MRI visible (PIRADS 3 – 5) and invisible PCa. MRI for each patient was re-reviewed and co-registered with whole-mount histopathology. DNA and RNA were co-isolated from all tumor foci pre-identified on FFPE specimens. High depth, targeted DNA and RNA next generation sequencing was performed to characterize the molecular profile of each tumor focus using the Oncomine Comprehensive Panel (DNA) and a custom targeted RNAseq panel assessing PCa relevant alterations. A multigene RNAseq model was developed and validated in two independent cohorts to predict MRI visible PCa and to determine the prognostic significance of MRI visibility. Results: A total of 26 primary tumor foci from 10 patients were analyzed. Of the 14 (54%) invisible lesions on MRI, 5 (36%) were Gleason 3+4 = 7 and the remainder were Gleason 6. We detected high-confidence prioritized PCa relevant mutations in 54% (14/26) of tumor foci, 43% (6/14) of which were in MRI invisible lesions. Notable point mutations were in APC, AR, ARID1B, ATM, ATRX, BRCA2, FAT1, MAP3K1, NF1, SPEN, SPOP, and TP53. A 9-gene RNA signature, the majority of which were under-expressed cellular organization and structure genes, was developed to predict MRI visibility with an AUC of 0.89. Validation of this signature in an independent data set (n = 16) yielded an AUC of 0.88 with a specificity of 100% for predicting MRI visible tumors. Using this signature in a cohort of 375 patients with clinical follow up, we found that predicted MRI visibility status was not an independent predictor of biochemical recurrence, metastasis-free survival, or PCa specific mortality (all p > 0.05). Conclusions: We observed under-expression of cellular organization and structural genes in MRI visible tumors compared to MRI invisible cancer foci. Using our validated signature to predict MRI visibility status, we found that MRI visibility is not a significant predictor of oncological outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".