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Head-to-head comparison between decipher and prolaris tests: Two commercially available post-prostatectomy genomic tests.

2020· article· en· W3007846303 on OpenAlexaff
Mohammed Shahait, Mohammed Alshalalfa, Edward M. Schaeffer, Huei–Chung Huang, Andrea A. Cronican, Paul L. Nguyen, Ayah El‐Fahmawi, Priti Lal, Elai Davicioni, David I. Lee

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsMedicineProstatectomyCohortInternal medicineOncologyBiochemical recurrenceCancerPathologyProstate cancer

Abstract

fetched live from OpenAlex

348 Background: Several post-prostatectomy genomic tests are available; which are used to improve prognostication and to guide additional treatment after radical prostatectomy (RP). There has been no head to head comparison between these tests. The objective of this study is to compare the performance of two genomic tests in predicting oncological outcomes. Methods: 16 patients who underwent RP at the University of Pennsylvania (UPenn) (2013-2018), had adverse pathology (margin, and/or pT3a/b) and had each been tested with both Decipher (D) and Prolaris (P). Pearson correlation was used to compare scores from D and P as well as CCP scores and microarray derived CCP (mCCP). The associations of D and P with biochemical recurrence (BCR) and metastasis (M) was evaluated in survival analysis in a large cohort of RP patients treated at Johns Hopkins University (1992-2010) (JHU). Results: The median follow-up of the UPenn cohort was 24 months. 6 patients developed BCR and two distant M. There was a significant correlation between the D and P score (r=0.67,p=0.004), and between the 10-year BCR risk reported by P and the 5-year M risk reported by D (r=0.69, p=0.003). Each test called 7 patients to be high risk; 5 were in common. Both tests correctly called the 2 M cases as high risk and 4/6 BCR patients to be high risk. A microarray-derived CCP (mCCP) was highly correlated to the CCP scores reported from P (r=0.88, p=6.7e-6) in the UPenn cohort. To compare the prognostic performance of mCCP to D for predicting BCR and M, we used Post-RP cohort from JHU (N=355). Both scores were correlated (r=0.36, p2e-12). D and mCCP were stratified into 5 groups of incremental 20%. When including mCCP groups, D groups, Gleason score, SVI, EPE, LNI, and PSA; D remained independent prognostic variable of BCR (HR 1.16, 95%CI [1.05-1.3], p=0.005) and M (HR 1.3, 95%CI [1.12-1.52], p=0.0005). However, mCCP was not prognostic of BCR (p=0.59) nor M (p=0.62). Conclusions: The findings from this study show that P and D scores post-RP were highly correlated and help in identifying patients who at high risk of progression in this small cohort with short follow up. However, D outperformed mCCP for predicting BCR and M in larger cohorts with longer follow up.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.268
GPT teacher head0.531
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations1
Published2020
Admission routes1
Has abstractyes

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