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Comparing NCCN-based risk groups to the PROTEUS definition of high-risk localized prostate cancer to inform peri-operative care.

2025· article· en· W4410808328 on OpenAlexaff
David‐Dan Nguyen, Christopher J.D. Wallis, Bobby Shayegan, Aly‐Khan A. Lalani, Geoffrey Gotto, Amanda Hird, Andrea Kokorovic, Melissa Huynh, Andrew Feifer, Ricardo Rendon, Rodney H. Breau, Antonio Finelli, Neil Fleshner, Alexandre R. Zlotta

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsSinai Health SystemUniversity of TorontoWestern UniversityUniversité de MontréalSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of CalgarySt. Joseph’s Healthcare HamiltonUniversity Health NetworkOttawa HospitalDalhousie UniversityMcMaster University
Fundersnot available
KeywordsMedicineCancerProstateProstate cancerPeriGynecologyGeneral surgeryOncologyInternal medicine

Abstract

fetched live from OpenAlex

e17144 Background: There are varying definitions of high-risk prostate cancer. The most commonly used is the NCCN definition which defines patients as having high- or very-high-risk disease if they have any of clinical T3 or greater disease, Gleason Grade Group 4 or 5, or PSA > 20ng/mL. Recent studies of androgen receptor signaling inhibitors have employed alternative definitions of baseline risk. Herein, we compared patient distributions and oncologic outcomes for patients undergoing radical prostatectomy according to two definitions of high-risk disease. Methods: Using an institutional database of patients undergoing radical prostatectomy, we characterized patients as having high-risk disease either by using NCCN risk groups (any of Gleason grade group [GGG] 4 or 5; or Clinical T3 or T4; or PSA >20 ng/mL) or using the definition employed in the PROTEUS trial (overall GGG ≥3 and at least one of the following: GGG5 in at least one core; or GGG4 in at least 2 cores, each with >80% involvement; or GGG3+ in ≥6 systematic cores; or GGG3+ in ≥3 systematic cores and PSA ≥20 ng/mL). Descriptive statistics were used to compare patient distributions. Area under the receiver operating curve (AUC) were compared between NCCN- and PROTEUS-based definitions of high-risk disease for oncologic outcomes including extra-prostatic extension, positive surgical margins, and PSA >0.1 or >0.2 at 12 months post-operatively. Results: Among 424 patients undergoing radical prostatectomy in the dataset, 358 had complete pathological biopsy data allowing for nuanced pre-operative risk stratification. Of these, 62 (17%) were classified as high-risk per NCCN criteria while 44 (12%) were classified as high-risk per PROTEUS criteria. Among 62 patients classified as high-risk per NCCN criteria, 38 met the PROTEUS criteria and 24 did not. Conversely, among 44 patients classified as high-risk per PROTEUS criteria, 38 met the NCCN high-risk criteria and 6 did not. Using AUC, the two definitions were comparably predictive of extra-prostatic extension (NCCN 0.56, 95% CI 0.52-0.60; PROTEUS 0.56, 95% CI 0.53-0.60; p=0.96), positive surgical margins (NCCN 0.54, 95% CI 0.49-0.58; PROTEUS 0.52, 95% CI 0.49-0.56; p=0.44), PSA >0.1 at 12 months post-operatively (NCCN 0.54, 95% CI 0.46-0.62; PROTEUS 0.59, 95% CI 0.51-0.67; p=0.22), and PSA >0.2 at 12 months post-operatively (NCCN 0.59, 95% CI 0.49-0.69; PROTEUS 0.64, 95% CI 0.54-0.75; P=0.32). Conclusions: This comparative analysis based on highly granular data shows that prostate cancer patients defined as high-risk based on NCCN risk groups, or high-risk criteria employed in the PROTEUS clinical trial, have comparable outcomes. Therefore, identifying patients suitable for treatment intensification with peri-operative systemic therapy is critical in this evolving clinical landscape.

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)0.009
metaresearch head score (Gemma)0.047
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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.273
GPT teacher head0.564
Teacher spread0.291 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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