Informations générales (source: ClinicalTrials.gov)

NCT03687918 Statut inconnu
External Validation of a Quantitative Computer Aided Diagnostic (QCAD) System for Prostate mpMRI (DIJON-CAD)
Observational
  • Tumeurs de la prostate
Hospices Civils de Lyon (Voir sur ClinicalTrials)
juin 2018
octobre 2018
29 juin 2024
Multiparametric magnetic resonance imaging (mpMRI) of the prostate combines T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Correlation with radical prostatectomy specimens has demonstrated that mpMRI has excellent sensitivity in detecting prostate cancers (PCa) with a Gleason score ≥7 and cancers with a Gleason 6 and a volume ≥0.5 cc. Nevertheless, its specificity is poor and there is large overlapping between the appearances of benign and malignant prostate lesions. As a result, the use of a 5-point subjective score has been widely encouraged to describe the level of suspicion of prostate lesions. This so-called 'Likert score' is a highly significant predictor of the malignant nature of prostate focal lesions. However, because there are no descriptions of specific criteria to be used in the scoring process, the Likert score relies heavily on the reader's experience. In an attempt to standardize mpMRI interpretation, the European Society of Urogenital Radiology and the American College of Radiology recently endorsed the so-called Prostate Imaging-Reporting and Data System (PIRADS) score. The second version of this scoring system (PI-RADS v2 score) gave good results in characterizing prostate focal lesions. However, Inter-reader agreement remains moderate at best, even after training, and there is still a high-rate of false positives. These results have led some authors to suggest that there might be structural limits to the ability of any score based on MR imaging to allow detection of prostate cancer with high specificity. Using quantitative magnetic resonance (MR) image features to characterize prostate lesions seen on mpMRI could improve interpretation standardization, and recently, several computer-aided diagnosis (CAD) systems combining various image features have shown promising results in characterizing prostate tissues. However, most CAD systems have been trained and evaluated on images from the same MR scanner. Unfortunately, quantification in MR imaging is limited by substantial inter-manufacturer variability in the calculation of quantitative image parameters. The quantitative thresholds defined for one manufacturer may therefore not be valid for another manufacturer. Of the many reported CAD systems, only few have shown robust results at cross-validation in datasets from different manufacturers. We developed in Lyon a mpMRI CAD system for discriminating Gleason ≥7 cancers in the peripheral zone (PZ). That CAD system was trained using mpMRI from patients treated by radical prostatectomy. It combines the 10th percentile of the apparent diffusion coefficient (ADC_10th) and the time to the peak of enhancement (TTP) at dynamic contrast-enhanced (DCE) imaging. It provided good results when cross-validated in two datasets from two different manufacturers (General Electric and Philips). We then tested the CAD on a cohort of 130 patients who underwent mpMRI (General Electric or Philips MR unit) before prostate biopsy. Each MR lesion targeted at biopsy had prospectively received a Likert score of likelihood of malignancy at the time of the biopsy. Retrospective analysis of these MR lesions with the CAD showed that the stand-alone CAD outperformed the Likert score in predicting the presence of Gleason ≥7 cancer at biopsy (Area under the receiver operating characteristic curve (AUC): 0.94 (95% confidence interval (95CI): 0.90-0.98 versus 0.81 (95CI: 0.75-0.88), p<0.0002)). These good results encourage us to perform an external validation of the CAD testing its performance on mpMRI from another manufacturer (Siemens) and another institution. The principal objective of the DIJON-CAD study is to evaluate the performances of the QCAD developed in Lyon (QCAD/Lyon) in a cohort of consecutive patients treated by prostatectomy and who underwent preoperative mpMRI on a Siemens 3 Tesla MR imager at the Dijon University Hospital center or at the Dijon Cancer Center (both institutions share the same MR unit). This study is the first step of the external validation of the QCAD/Lyon system. It is only aimed at verifying that the diagnostic performance of the system is not very poor on external mpMRI (which is a substantial risk). If the results are good, a proper multicentric prospective validation study will be planned.
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Etablissements

Les établissements hors Île-de-France dont les données sont issues de ClinicalTrials.gov Origine et niveau de fiabilité des données
Department of biostatistics, Université Joseph Fourrier - Grenoble - France Complet Contact (sur clinicalTrials)
Department of pathology, CHU de Dijon - Dijon - France Complet Contact (sur clinicalTrials)
Department of radiology and nuclear medicine, Centre anti-cancéreux Georges-François Leclerc, Dijon - Dijon - France Complet Contact (sur clinicalTrials)
Department of vascular and urinary imaging, hôpital Edouard Herriot, Hospices Civils de Lyon - 69003 - Lyon - France Olivier ROUVIERE, Pr En recrutement Contact (sur clinicalTrials)
LabTau, INSERM unit 1032, Lyon - Lyon - France Complet Contact (sur clinicalTrials)
Les établissements sans correspondance certaine dans le répertoire FINESS dont les données sont issues de ClinicalTrials.gov Origine et niveau de fiabilité des données
Department of radiology, CHU de Dijon - Dijon - France Complet Contact (sur clinicalTrials)
Department of urology, CHU de Dijon - Dijon - France Complet Contact (sur clinicalTrials)

Critères

Homme
Inclusion Criteria:

- male over 18 year-old

- treated by prostatectomy for prostate cancer

- who undergone a preoperative mpMRI at 3 Tesla at the Dijon University Hospital or at
the Dijon Cancer center

- non opposition of the patient


- Patients who received prior treatment for prostate cancer (hormonotherapy, external
beam radiation therapy, brachytherapy)