1911 LARGE-SCALE MITOCHONDRIAL GENOME DELETION AS AN AID FOR NEGATIVE PROSTATE BIOPSY UNCERTAINTY
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection and Screening1 Apr 20111911 LARGE-SCALE MITOCHONDRIAL GENOME DELETION AS AN AID FOR NEGATIVE PROSTATE BIOPSY UNCERTAINTY Kent Froberg, Laurence Klotz, Kerry Robinson, Jennifer Creed, Brian Reguly, Cortney Powell, Daniel Klein, Andrea Maggrah, Roy Wittock, and Ryan Parr Kent FrobergKent Froberg Virginia, MN More articles by this author , Laurence KlotzLaurence Klotz Toronto, Canada More articles by this author , Kerry RobinsonKerry Robinson Thunder Bay, Canada More articles by this author , Jennifer CreedJennifer Creed Thunder Bay, Canada More articles by this author , Brian RegulyBrian Reguly Thunder Bay, Canada More articles by this author , Cortney PowellCortney Powell Thunder Bay, Canada More articles by this author , Daniel KleinDaniel Klein Thunder Bay, Canada More articles by this author , Andrea MaggrahAndrea Maggrah Thunder Bay, Canada More articles by this author , Roy WittockRoy Wittock Thunder Bay, Canada More articles by this author , and Ryan ParrRyan Parr Thunder Bay, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.2049AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Numerous biological characteristics of the mitochondrial genome (mtgenome) highlight this molecule as a clinically useful “biosensor” which can discriminate between normal and malignant tissues. These characteristics include: 1) in comparison to two copies of each nuclear genome, there are 100's to 1000's of mtgenomes within a cell, increasing recoverable biomarker signal; 2) the mtgenome has an accelerated somatic mutation rate over that of the nucleus, allowing early detection of alterations indicative of malignant transformation; 3) mutations are associated with a “cancerization field effect”; 4) these mutations are often easy to screen large-scale deletions. These molecular attributes were used to further develop an assay for accurate prediction of the outcome of a relatively rapid follow-up biopsy, after an initial negative biopsy. The objective of this work was to determine the potential clinical utility of a large-scale mtgenome deletion (3.4kb) for predicting the presence/absence of tumor foci in men with an initial negative biopsy. A nested case control study was designed to mimic an actual clinical cohort, for the purposes of determining clinically significant performance metrics. METHODS The study design was a retrospective nested case controlled study on a total of 101 patients with a negative, original biopsy, which had a follow-up biopsy within 1 year of the negative procedure. Of these, 20 were malignant and the remaining 81 were negative, based on the second biopsy pathology reports. 20um sections of fixed and embedded needle cores, representing the 6 anatomical regions of the prostate, were obtained from the archived blocks of the first biopsy. A quantitative real-time PCR assay was used to determine the cycle threshold value (Ct) which provides optimum clinical information. Overall, 1000 cores from close to 400 men were used in the cumulative studies for this biomarker. RESULTS A real-time PCR Ct cutoff of 31 returned a sensitivity of 85% and a negative predictive value of 92% for predicting the results of the second biopsy. Moreover, of 22 patients with ASAP on the intial biopsy, 10 of these patients (46.5%) were malignant on the repeat biopsy. This result was predicted by the study. CONCLUSIONS A npv of 92% highlights those patients who may not require a follow-up biopsy, while a sen of 85% indicates those who may benefit from a secondary biopsy. The deletion marker is consistent with the frequency at which patients, with an initial call of ASAP, are found to have malignancy on a second biopsy. This attribute may assist in managing some patients with ASAP. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e764 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.Metrics Author Information Kent Froberg Virginia, MN More articles by this author Laurence Klotz Toronto, Canada More articles by this author Kerry Robinson Thunder Bay, Canada More articles by this author Jennifer Creed Thunder Bay, Canada More articles by this author Brian Reguly Thunder Bay, Canada More articles by this author Cortney Powell Thunder Bay, Canada More articles by this author Daniel Klein Thunder Bay, Canada More articles by this author Andrea Maggrah Thunder Bay, Canada More articles by this author Roy Wittock Thunder Bay, Canada More articles by this author Ryan Parr Thunder Bay, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.016 |
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".