High Risk Cancer, Lung Cancer
Conditions
Brief summary
This is a two arm, randomized, controlled, blinded, multi-case multi reader (MRMC), retrospective study for the evaluation of the efficacy and safety of an AI/ML technology-based CADe/x developed to detect, localize and characterize malignancy score of pulmonary nodules on LDCT chest scans taken as part of a lung cancer screening program. LDCT DICOM images of patients who underwent routine lung cancer screening will be selected and enrolled into the study. Enrolled scans analyzed by radiologists with the assistance of the Median LCS (formerly iBiopsy) device are compared to the analysis by radiologists without the assistance of the Median LCS device. Figures of merit for patient level and lesion level detection and diagnostic efficacy will be calculated and compared, sub-class analysis will be performed to ensure device generalizability.
Interventions
DEVICEMedian LCS
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.
Sponsors
Median Technologies
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* ≥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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| ∆ AUC of ROCs > 0. Delta Area between the Response operating curve (AUROC) value with Median LCS and AUROC without Median LCS at patient level data is superior to 0. | 12 months | Demonstrate that patient diagnosis with Median LCS is improved compared to without Median LCS. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Specificity at max Youden | 12 months | Demonstrate that Median LCS assisted specificity is not inferior (H3), superior (H9) to radiologist alone. (Sensitivity with Median LCS-Patient) non inferior using non-inferiority margin delta = 0.1 to (Sensitivity Control Arm-Patient). First, non-inferiority. If passed, superiority will be performed. |
| ∆ AUC of LROC > 0 | 12 months | Demonstrate that Median LCS improves clinician's performance in finding detection and diagnosis. |
| Sensitivity at max Youden | 12 months | Demonstrate that Median LCS aided sensitivity is non inferior (H2) , superior (H8) to radiologist alone. (Sensitivity with Median LCS-Patient) non inferior using non-inferiority margin delta = 0.1 to (Sensitivity Control Arm-Patient). First, non-inferiority. If passed, superiority will be performed. |
| Recall rates for cancer patients (Sensitivity) | 12 months | Demonstrate that Median LCS aid to diagnose cancer patients compared to radiologist alone. Cancer-Recall-Rate will be calculated and compared between the two modalities using margin of 10%. First, non-inferiority. If passed, superiority will be performed. |
| Time analysis | 12 months | Demonstrate that Median LCS decreases the time of analysis per patient. |
| Recall rates for non-cancer patients (Specificity) | 12 months | Demonstrate that Median LCS aids to rule out non-cancer patients compared to radiologist alone. Non-Cancer-Recall-Rate will be calculated and compared between the two modalities using margin of 10%. First, non-inferiority. If passed, superiority will be performed. |
Countries
Spain, United States
Outcome results
None listed