Bibliographic record
Abstract
Introduction:We aimed to develop a radiomics-based prognostic model using machine learning to predict lymph node invasion (lnI), biochemical recurrence (BCr), metastasis-free survival (mFs), definitive androgen deprivation therapy (dAdt)-free survival (Fs), castration-resistant prostate cancer (CrpC)-Fs, and prostate cancer-specific survival (pCss) in individuals diagnosed with high-grade prostate cancer (pCa).Methods: A total of 295 individuals with high-grade pCa (gleason score ≥8) underwent preoperative positron emission tomography (pet) with 18F-fluorodeoxyglucose (Fdg) combined with computed tomography (Ct) imaging at our tertiary care health center in Quebec City, Canada.Clinical data (Cd), including age, prostate-specific antigen (psA) level, gleason score, and clinical stage, were used to build prognostic models to which handcrafted radiomics (HCr) or deep learning-based radiomics (dlr) were added to enhance performance.We trained the models using a subset of the cohort (250 individuals) and optimized them using a stratified five-fold cross-validation.the selected model was then validated using a test set of 45 individuals.performance on the test set was evaluated using the area under the curve of the receiver operator characteristic (AUC-roC) and the concordance index (C-index).A comparison with commonly used nomograms (msKCC and CAprA-s) was also made.Results: median followup was 64.7 (range 29.3-89.6)months.median age was 66 (48-80) years.median psA was 7.4 (1.1-155.3).A total of 230 (88%) and 31 (12%) had clinical t1-t2 and t3a disease, respectively.the majority (63.7%) had gleason 8.At rp, 86 (29%) individuals had lnI.At followup, 160 had BCr.In the training set, using Cd with radiomics yielded better performance for prediction of lnI (AUC 72±5 vs. 70±7 [msKCC] and 62±4 [CAprA-s]) and BCr-Fs (CI=65±6 vs. 64±3 [msKCC] and 63±4 [CAprA-s]).nomograms outperformed our combined radiomics-Cd model for prediction of other outcomes, although performances were like our Cd-only model.Conclusions: Integrating imaging data into prognostic tools through artificial intelligence enhances clinical predictions for lnI and BCr, enabling more accurate prognostication.With minimal training, we achieved better results than commonly used nomograms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.102 |
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".