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Using radiogenomics to characterize MRI-guided prostate cancer biopsy heterogenity.

2015· article· en· W1844424555 on OpenAlexaff
Radka Stoyanova, Alan Pollack, Charles M. Lynne, Mercè Jordà, Nicholas Erho, Lucia L.C. Lam, Christine Buerki, Elai Davicioni, Adrian Ishkanian

Bibliographic record

VenueJournal of Clinical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsRadiogenomicsProstate cancerMedicineProstateBiopsyCancerRadiologyPathologyInternal medicineRadiomics

Abstract

fetched live from OpenAlex

25 Background: Current methods for prostate cancer risk stratification are often insufficient to accurately predict outcome after definitive therapy. As tumor multi-focality and genetic heterogeneity can lead to diagnostic prostate biopsy sampling bias, we hypothesize that quantitative imaging with multiparametric (MP)-MRI will more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features, and improve accuracy of current risk classification systems. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2w, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 51 radiographic features (including intensity, volume, perfusion, and diffusion paramters). Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving ~2K features. Results: High quality genomic data was derived from 17 (100%) biopsies and clustered by patient origin. Using only prostate cancer related genomic features for hierarchical clustering, samples clustered by Gleason score (GS), indicating these biopsies contain prognostic signal. Similarly, when principal component analysis was performed on 51 imaging features, the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis identified 152 genomic features that were highly associated with the imaging features (|r| > 0.7). Furthermore, genomic features were found to be significantly enriched for prostate cancer related pathways (p < 0.05), representing a potential biologically meaningful link between imaging and genomic data. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk classification by directing pathological and genomic analysis to highest risk index lesions. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.262
GPT teacher head0.514
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations4
Published2015
Admission routes1
Has abstractyes

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