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Record W4405499816 · doi:10.3390/siuj5060069

The Effect of Pre-Biopsy Prostate MRI on the Congruency and Upgrading of Gleason Grade Groups Between Prostate Biopsy and Radical Prostatectomy

2024· article· en· W4405499816 on OpenAlexvenueno aff
Peter Stapleton, Thomas Milton, Niranjan Sathianathen, Michael O’Callaghan

Bibliographic record

VenueSociété Internationale d’Urologie Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersFlinders FoundationMovember Foundation
KeywordsProstatectomyMedicineProstate cancerBiopsyProstateConcordanceGrading (engineering)Prostate biopsyStage (stratigraphy)RadiologyCancerUrologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Prostate biopsy results form the mainstay of patient care. However, there is often significant discordance between the biopsied histology and the ‘true’ histology shown on a radical prostatectomy (RP). Discordance in pathology can lead to the mismanagement of patients, potentially missing clinically significant cancer and delaying treatment. There have been many advancements to improve the concordance of pathology and more accurately counsel patients; most notably, the induction of pre-biopsy mpMRIs has become a gold standard to aid in triaging and identifying clinically significant cancers, and also to facilitate ‘targeted’ biopsies. Although there have been multiple reviews on MRI-targeted biopsies, upgrading remains an ongoing phenomenon. Aim: To assess the rates of prostate cancer upgrading and the clinical implication of upgrading on NCCN stratification. Methods: We conducted a retrospective audit of 2994 men with non-metastatic prostate cancer diagnosed between 2010 and 2019 who progressed to a radical prostatectomy within 1 year of diagnosis without alternative cancer treatment from the multi-institutional South Australia Prostate Cancer Clinical Outcomes Collaborative registry. The study compared the histological grading between the biopsies and radical prostatectomies of men with prostate cancer and the varying rates of upgrading and downgrading for patients with and without a pre-biopsy MRI. Data were also obtain on suspected confounding variables; age, PSA, time to RP, T-stage at diagnosis and RP, number of cores, number of positive cores, prostate size, tumour volume and procedure type. The results were assessed through cross tabulation and uni- and multi-variate logistic regression while adjusting for confounders. Results: Upgrading occurred in (926) 30.9% of patients and downgrading in (458) 15.3% of patients. In total, 71% (410/579) of grade group 1 and 24.9% (289/1159) of grade group 2 were upgraded following a radical prostatectomy. By contrast, 33.4% (373/1118) of patients without prebiopsy MRI were upgraded at RP compared to 29.5% (553/1876) of the patients who received a pre-biopsy MRI. When analysed on a uni-variate level, the inclusion of a pre-biopsy MRI demonstrated a statically significant decrease in upgrading of the patient’s pathology and NCCN risk stratification (p = 0.026, OR 0.83, CI 0.71–0.98) (p = 0.049, OR 0.82, CI 0.64–1.01). However, when adjusted for confounders, the use of an MRI did not maintain a statistically significance. Conclusions: When considering the multiple variables associated with tumour upgrading, a pre-biopsy MRI did not show a statistically significant impact. However, upgrading of Gleason Grade Group following a prostatectomy is an ongoing phenomenon which can carry significant treatment implications and should remain a consideration with patients and clinicians when making decisions around treatment pathways. More research is still required to understand and improve biopsy grading to prevent further upgrading from affecting treatment choices.

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)0.024
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 score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.317
Teacher spread0.299 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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