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Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images

NCT04164186 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1043

Last updated 2020-04-06

No results posted yet for this study

Summary

Accurate segmentation of lung tumor is essential for treatment planning, as well as for monitoring response to therapy. It is well-known that segmentation of the lung tumour by different radiologists gives different results (inter-observer variance). Moreover, if the same radiologist is asked to repeat the segmentation after several weeks, these two segmentations are not identical (intra-observer variance). In this study we aim to develop an automated pipeline that can produce swift, accurate and reproducible lung tumor segmentations.

Conditions

  • Detection
  • Segmentation

Interventions

OTHER

Automatic detection and segmentation of NSCLC tumors

an automated deep learning detection and segmentation software for non-small cell lung cancer (NSCLC) that can automatically detect and segment tumors on CT scans and thus reduce the human variation.

Sponsors & Collaborators

  • Centre Hospitalier Universitaire de Liege

    collaborator OTHER

  • University Hospital RWTH Aachen University, Aachen, Germany.

    collaborator UNKNOWN

  • Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang street, Dalian 116001, China

    collaborator UNKNOWN

  • University of California, San Francisco

    collaborator OTHER

  • Maastricht University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-03-10
Primary Completion
2019-11-07
Completion
2020-10-31

Countries

  • Netherlands

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04164186 on ClinicalTrials.gov