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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control arm | low-dose CT scan image readings performed by radiologists without the assistance of Median LCS | ||
| Test arm | low-dose CT scan image readings performed by radiologists with the assistance of Median LCS. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| ∆ 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. | Demonstrate that patient diagnosis with Median LCS is improved compared to without Median LCS. | 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity at max Youden | 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. | 12 months |
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Inclusion Criteria:
Exclusion Criteria:
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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
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| Name | Affiliation | Role |
|---|---|---|
| Anil VACHANI, MD | University of Pennsylvania | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Pennsylvania - Penn Center for Innovation | Philadelphia | Pennsylvania | 19104 | United States | ||
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| Specificity at max Youden |
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. |
| 12 months |
| ∆ AUC of LROC > 0 | Demonstrate that Median LCS improves clinician's performance in finding detection and diagnosis. | 12 months |
| Recall rates for non-cancer patients (Specificity) | 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. | 12 months |
| Recall rates for cancer patients (Sensitivity) | 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. | 12 months |
| Time analysis | Demonstrate that Median LCS decreases the time of analysis per patient. | 12 months |
| Baptist Clinical Research Institute |
| Memphis |
| Tennessee |
| 38120 |
| United States |
| The University of Texas M.D. Anderson Cancer Center | Houston | Texas | 77030 | United States |
| Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD) | Madrid | 28040 | Spain |
| Universidad de Navarra | Pamplona | 31009 | Spain |
| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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