An observational study in High Risk Cancer, sponsored by Median Technologies. Completed at 5 sites in 2 countries. Open to participants aged 50 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-08-28.
Sponsored by Median Technologies · Observational
This is a Multinational, Multicenter, retrospective study for the evaluation of the standalone efficacy and safety of an Artificial Intelligence/Machine Learning (AI/ML) technology-based end-to-end Computer assisted Detection/Computer Assisted Diagnosis (CADe/CADx) Software as a Medical Device (SaMD) developed to detect, localize and characterize malignant, and suspicious for lung cancer nodules on Low Dose Computed Tomography (LDCT) scans taken as part of a Lung Cancer Screening (LCS) program.
LDCT Digital Imaging and Communications in Medicine (DICOM) images of patients who underwent lung cancer screening were selected and included into the study. Selected scans will then be analyzed by the CADe/CADx SaMD and compared to radiologist generated reference standards including lesions localization and lesion cancer diagnosis.
Figures of merit at patient level and lesion level detection and diagnostic efficacy will be calculated as well as sub-class analysis to ensure algorithm performance generalizability.
Median Technologies is the lead sponsor of 3 studies on the registry; none are open to participants now.
Counted across the registry records on this site, refreshed daily.
High risk lung cancer population from Radiology or Pneumology hospital departments.
Patients enrolled in this study were retrospectively collected from centers across the EU and USA where they were enlisted into lung cancer screening due to high risk of lung cancer according to established lung cancer screening guidelines.
The cohort used for testing the efficacy and safety of the device will be an "enriched cohort" with a 1:2 distribution of cancer positive and benign patients
Exclusion Criteria:
End-to-end processing of chest LDCT DICOM images by an AI/ML tech-based SaMD to detect, localize, and characterize (assign a malignancy score) each detected pulmonary nodule. The output of the device is a DICOM File (Median LCS result report) summarizing results per patient.
Also known as: eyonis LCS
AUROC (Area under ROC curve) at patient level
AUROC that measures Median LCS performance at patient level is strictly superior to 0.8. Support for Primary Endpoint: Derived from the patient level AUROC at the product fixed operating point : Sensitivity, Specificity, PPV, NPV.
Time frame: 12 months
Sensitivity > 70% when Specificity=70%
Time frame: 12 months
Specificity > 70% when Sensitivity=70%
Time frame: 12 months
AUC of LROC > 0.75
In contrast to the receiver operating characteristic (ROC) assessment paradigm, localization ROC (LROC) analysis provides a means to jointly assess the accuracy of localization and detection in an observational study.
Time frame: 12 months
Detection sensitivity>0.8 with average FP rate per scan<1
Time frame: 12 months
ICC>0.8 for average diameter
Intraclass Correlation Coefficient (ICC), is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other.
Time frame: 12 months
ICC>0.8 for long axis diameter
Time frame: 12 months
ICC>0.8 for short axis diameter
Time frame: 12 months
ICC>0.75 for Volume
Time frame: 12 months
DICE Coefficient >0.7
Time frame: 12 months
This study is completed, as verified in Aug 2024. You cannot join it, but the record below documents what was studied.
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