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Completed

NCT Number: NCT06576232

Standalone Observational Study Assessing the Performance of an AI/ML Tech-based SaMD on Chest LDCT Images (REALITY)

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.

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Key information

Conditions

High Risk Cancer

Age range

50 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD), Madrid, Spain

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Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Median LCS

Device

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.

Other names: eyonis LCS

Primary outcomes

  1. AUROC (Area under ROC curve) at patient level

    Time frame: 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 outcomes

  1. Sensitivity > 70% when Specificity=70%

    Time frame: 12 months

  2. Specificity > 70% when Sensitivity=70%

    Time frame: 12 months

  3. AUC of LROC > 0.75

    Time frame: 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.

  4. Detection sensitivity>0.8 with average FP rate per scan<1

    Time frame: 12 months

  5. ICC>0.8 for average diameter

    Time frame: 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.

  6. ICC>0.8 for long axis diameter

    Time frame: 12 months

  7. ICC>0.8 for short axis diameter

    Time frame: 12 months

  8. ICC>0.75 for Volume

    Time frame: 12 months

  9. DICE Coefficient >0.7

    Time frame: 12 months

Sponsors and collaborators

Lead sponsor

Median Technologies

Industry

Registry information

Official study 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)

Acronym: REALITY

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Aug 28, 2024
Registry last updated
Aug 28, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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