73 Is magnetic resonance imaging in prostate cancer a possible avenue for reducing overdiagnosis?
Bibliographic record
Abstract
Background Transrectal ultrasonography (TRUS)-guided biopsies is the conventional diagnosis pathway in prostate cancer (PCa). However, this practice results of a high proportion of men diagnosed with clinically insignificant tumor, and eventually overtreatment. Scientific data suggest that multiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa) over TRUS-guided biopsies. We aimed to determine the diagnostic performance of mpMRI for the detection of csPCa and to estimate the reduction of unnecessary prostate biopsy (PBx). Method Literature searches were conducted in several indexed databases and grey literature between January 2008 and January 2019 to retrieve studies on mpMRI in diagnostic of csPCa. Two reviewers independently performed selection, quality assessment and data extraction. Eligible studies: 1) PBx-naïve patient or patient with previous negative PBx, 2) mpMRI performed with T2 and at least two functional MRI techniques, 3) PI-RADS scale for image assessment, 4) PBx as reference test. Sensibility (Se), specificity (Sp), negative predictive value (NPV), positive predictive value (PPV) and negative likelihood ratio (LR-) were estimated based on a positivity threshold of PI-RADS ≥ 3. A meta-analyze were performed using bivariate hierarchical models to estimate mean value and 95% confidence interval (95%CI) of Se, Sp, NPV, PPV and LR-. Sub-group analyses included: csPCA prevalence quartiles, PBx status and number of core PBx. Proportion of patient with unnecessary PBx was estimated from the rate of negative mpMRI results (PI-RADS ≤ 2). Results Forty-two original studies (1 RCT, 26 prospective and 15 retrospective studies) were included. Median csPCa prevalence (range) was 31% (13–55%) in all studies, 40% (21–47%) for PBx-naïve groups and 29% (13–55%) for previous negative PBx groups. Median value (range) of Se and Sp were 94% (62–100%) and 45% (2–79%), respectively. Median rates (range) of NPV (range) and PPV (range) were respectively 92% (33–100%) and 45% (18–88%) in all studies. In PBx-naïve groups and previous negative PBx groups, NPV (range) were 89% (33–100%) and 93% (50–100%), respectively. Median value (range) of mpMRI false-negative and false-positive rates was 7% (0–38%) and 55% (3–98%) respectively. Median rate of mpMRI negative results (PI-RADS ≤ 2) in all studies was 31% (range: 1–83%). Bivariate analysis results (95%CI) showed that mean Se, Sp, NPV and LR- were 92% [90–94%], 44% [36–52%], 92% [90–94%] and 0.17 (0.14–0.22), respectively. Sub-group analysis suggest small variations in NPV value according to the PBx status and the number of PBx, but a significant inverse relationship with csPCA prevalence (p = 0.01). Conclusions The results indicates a very low probability to find csPCa when mpMRI result is negative (PI-RADS ≤ 2) in PBx-naïve groups and previous negative PBx groups. Assuming that patients with PI-RADS ≤ 2 do not undergo PBx, we estimate that nearly one-third of men under diagnosis testing for prostate cancer suspicion could avoid unnecessary TRUS-guided PBx and negative adverse conséquences.
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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 | 0.036 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".