73 Is magnetic resonance imaging in prostate cancer a possible avenue for reducing overdiagnosis?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,008 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».