Evaluation of Contextflow DETECT Lung CT Nodule Detection Software in Chest CT Scans

NCT05481762 | Completed

• 1 other identifier: 0020220401
• Study Type: observational
• Target: 337
• Locations: 1 country
• Sites: 1

Brief Summary

contextflow DETECT Lung CT is a Artificial Intelligence (AI)-based computed-aided detection (CADe) system, intended to support radiologists in the detection of lung nodules in chest computed tomography (CT) scans. System is intended to be used as a second-reader, therefore results provided by the software are meant to complement the radiologist's findings and decisions. Proposed study will be multi-reader, multi case (MRMC) retrospective reader study. The goal of the study is to evaluate the influence of CADe on the effectiveness of lung nodule detection. During the study, 10 radiologists will analyze 350 chest CT scans of adult patients, with and without the assistance of CADe. The study will be conducted remotely. CT scans will be uploaded to a web-based image submission and annotation platform, in which every participant of the study will be provided with individual account and assigned task list. The primary objective of the study determine if the diagnostic accuracy of radiologists with CADe assistance is superior to the diagnostic accuracy of radiologists without CADe assistance in localizing the pulmonary nodules with enhanced area under the free-response operating characteristic curve (AUC of FROC). The study will target approximately 350 asymptomatic adult patients, whose CT scans were acquired during routine CT examination. The patient population will include patients with and without lung nodules.

Conditions

Lung Cancer

Keywords

pulmonary nodule; artificial intelligence; computer-aided detection

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy

  The primary objective of the reader study is to determine if the diagnostic accuracy of radiologists with CADe assistance is superior to the diagnostic accuracy of radiologists without CADe assistance in localizing the pulmonary nodules with enhanced area under the free-response operating characteristic curve (AUC of FROC). The true positive rate (or sensitivity) is calculated as the identified positive lesion among the true positive divided by the total number of true positive lesions among all images. The number of false positive findings is collected per image.

  20 hours

Secondary Outcomes (2)

• Disease diagnosis capabilities

  20 hours

• Disease identification capabilities

  20 hours

Study Arms (1)

Asymptomatic adult patients

Device: Aided read with contextflow DETECT Lung CT

Interventions

Aided read with contextflow DETECT Lung CT DEVICE

Radiologists read chest CT scabs with and without the aid of contextflow DETECT Lung CT as a second reader

Asymptomatic adult patients

Eligibility Criteria

• Age: 22 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Asymptomatic adult patients (aged 22 or older), with or without pulmonary nodules detected during the routine chest CT scan. CT images taken with CT scanners provided by 3 different vendors, with 50% of images acquired from the US medical centers.

You may qualify if:

• adult asymptomatic patients, who undergo a routine chest CT scan.

You may not qualify if:

• symptomatic patients.

Sponsors & Collaborators

• contextflow GmbH: lead

Study Sites (1)

contextflow GmbH

Vienna, 1050, Austria

Location

Related Publications (3)

• Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008 Mar;246(3):697-722. doi: 10.1148/radiol.2462070712. Epub 2008 Jan 14.

  PMID: 18195376 BACKGROUND

• Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.

  PMID: 33538338 BACKGROUND

• Qian F, Yang W, Chen Q, Zhang X, Han B. Screening for early stage lung cancer and its correlation with lung nodule detection. J Thorac Dis. 2018 Apr;10(Suppl 7):S846-S859. doi: 10.21037/jtd.2017.12.123.

  PMID: 29780631 BACKGROUND

Related Links

• American Cancer Society, Cancer Statistics Center

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 28, 2022
• First Posted: August 1, 2022
• Study Start: April 1, 2022
• Primary Completion: October 30, 2022
• Study Completion: October 30, 2022
• Last Updated: February 8, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)