Can 3T multiparametric magnetic resonance imaging accurately detect prostate cancer extracapsular extension?
Bibliographic record
Abstract
BACKGROUND: Accurate staging is essential to determine the correct management of patients diagnosed with prostate cancer. We assess the accuracy of 3T multiparametric magnetic resonance imaging (MRI) with endorectal coil (3TemMRI) in detecting prostate cancer local extension. METHODS: We retrospectively reviewed charts from January 2008 to July 2012 from all patients undergoing radical prostatectomy. Patients were only included if 3TemMRI and radical prostatectomy were performed at our institution. Based on the presence of extracapsular extension (ECE) at 3TemMRI, prostate cancer was dichotomized into locally advanced or organ-confined disease. The accuracy of 3TemMRI local staging was then evaluated using definitive pathology as a reference. RESULTS: Overall, 177 radical prostatectomies were performed within the timeframe. After applying exclusion criteria, 60 patients were included in the final analysis. The mean patient age was 67 ± 7 (standard deviation) years. Mean prostate-specific antigen value was 12.7 ± 12.7 ng/L. Based on preoperative characteristics, we considered 38 of the 60 patients (63%) patients high risk. 3TemMRI identified an organ-confined tumour in 46 patients and locally advanced disease in 14 patients. When correlated to final pathology, 3TemMRI specificity, sensitivity, negative and positive predictive values, and accuracy in detecting locally advanced prostate cancer were 90%, 35%, 57%, 79% and 62%, respectively. INTERPRETATION: This study shows that the use of preoperative 3TemMRI can be used to identify organ-confined prostate cancer when locally advanced disease is suspected.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".