Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies
Bibliographic record
Abstract
// Radka Stoyanova 1 , Alan Pollack 1 , Mandeep Takhar 2 , Charles Lynne 3 , Nestor Parra 1 , Lucia L.C. Lam 2 , Mohammed Alshalalfa 2 , Christine Buerki 2 , Rosa Castillo 4 , Merce Jorda 3, 5 , Hussam Al-deen Ashab 2 , Oleksandr N. Kryvenko 3, 5 , Sanoj Punnen 3 , Dipen J. Parekh 3 , Matthew C. Abramowitz 1 , Robert J. Gillies 6 , Elai Davicioni 2 , Nicholas Erho 2 , Adrian Ishkanian 1 1 Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA 2 Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada 3 Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA 4 Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA 5 Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL, USA 6 Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA Correspondence to: Radka Stoyanova, email: rstoyanova@med.miami.edu Keywords: prostate cancer, multiparametric MRI, MRI-targeted biopsies, gene expression, radiogenomics Received: February 26, 2016 Accepted: June 30, 2016 Published: July 11, 2016 ABSTRACT Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues. Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas (‘habitats’) were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.
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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 | 0.002 |
| 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 | 0.001 | 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".