Clinical trial NCT06576232: Standalone Observational Study Assessing the Performance of an AI/ML Tech-based SaMD on Chest LDCT Images (REALITY)
Official title: Multinational, Multicenter, Retrospective Study to Evaluate an AI/ML Technology-Based End-to-End CADe/CADx SaMD, Which Allows Detection, Localization and Characterization of Pulmonary Nodules (REALITY)
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: High Risk Cancer
Lead sponsor: Median Technologies
Sponsor class: INDUSTRY
First posted: 2024-08-28
Enrollment: 1147
Sex: ALL
Minimum age (years): 50
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: 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

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.


Eligibility criteria: 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.

Primary outcomes:
measure: AUROC (Area under ROC curve) at patient level
timeFrame: 12 months
Secondary outcomes:
measure: Sensitivity > 70% when Specificity=70%
timeFrame: 12 months
measure: Specificity > 70% when Sensitivity=70%
timeFrame: 12 months
measure: AUC of LROC > 0.75
timeFrame: 12 months
measure: Detection sensitivity>0.8 with average FP rate per scan<1
timeFrame: 12 months
measure: ICC>0.8 for average diameter
timeFrame: 12 months
measure: ICC>0.8 for long axis diameter
timeFrame: 12 months
measure: ICC>0.8 for short axis diameter
timeFrame: 12 months
measure: ICC>0.75 for Volume
timeFrame: 12 months
measure: DICE Coefficient >0.7
timeFrame: 12 months
Other outcomes:
Locations:
city: Philadelphia
country: United States
facility: University of Pennsylvania - Penn Center for Innovation
state: Pennsylvania
city: Memphis
country: United States
facility: Baptist Clinical Research Institute
state: Tennessee
city: Houston
country: United States
facility: The University of Texas M.D. Anderson Cancer Center
state: Texas
city: Madrid
country: Spain
facility: Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD)
city: Pamplona
country: Spain
facility: Universidad de Navarra
Study officials:
affiliation: University of Pennsylvania
name: Anil VACHANI, MD
role: PRINCIPAL_INVESTIGATOR
References: