Standalone Observational Study Assessing the Performance of an AI/ML Tech-based SaMD on Chest LDCT Images (REALITY)
- Conditions
- High Risk Cancer
- Registration Number
- NCT06576232
- Lead Sponsor
- Median Technologies
- Brief Summary
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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1147
- ≥50-80 Years of age;
- Current or ex-smoker (>=20 pack years);
- Patient screened and surveilled for lung cancer screening following lung cancer screening guidelines (equivalent to United States Preventive Services Task Force (USPSTF) 2021 Criteria);
- Received LDCT due to inclusion in high-risk category for lung cancer.
- Prior lung resection;
- Pacemaker or other indwelling metallic medical devices in the thorax that interfere with CT acquisition;
- Patients/images used during AI model development;
- Patients with only hilar and/or mediastinal cancer(s);
- Patients with only ground glass cancer(s);
- Patients with nodules, solid or part-solid >30mm (masses);
- Patients that are not accompanied with the required clinical information;
- Patients with imaging with any of the following: missing slices, slice thickness >3mm;
- Partial cover of the lung.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method AUROC (Area under ROC curve) at patient level 12 months 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.
- Secondary Outcome Measures
Name Time Method ICC>0.8 for long axis diameter 12 months ICC>0.8 for short axis diameter 12 months ICC>0.75 for Volume 12 months Sensitivity > 70% when Specificity=70% 12 months Detection sensitivity>0.8 with average FP rate per scan<1 12 months ICC>0.8 for average diameter 12 months 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.
DICE Coefficient >0.7 12 months Specificity > 70% when Sensitivity=70% 12 months AUC of LROC > 0.75 12 months 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.
Trial Locations
- Locations (5)
University of Pennsylvania - Penn Center for Innovation
🇺🇸Philadelphia, Pennsylvania, United States
Baptist Clinical Research Institute
🇺🇸Memphis, Tennessee, United States
Universidad de Navarra
🇪🇸Pamplona, Spain
Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD)
🇪🇸Madrid, Spain
The University of Texas M.D. Anderson Cancer Center
🇺🇸Houston, Texas, United States