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

NCT04164186 | Unknown DMC

• 1 other identifier: ALSP
• Study Type: observational
• Target: 1,043
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (2)

• Detection of NSCLC on CT scans

  Automatic detection of NSCLC tumors

  November, 2019

• Segmentation of NSCLC scans

  Automatic segmentation of NSCLC tumors

  November, 2019

Interventions

Automatic detection and segmentation of NSCLC tumors OTHER

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.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

CT scans of 1043 patients diagnosed with NSCLC at one of the 8 centers (Netherlands, USA, China, Belgium) were collected retrospectively. All patients had a biopsy to confirm the diagnosis

You may qualify if:

• Availability of CT scans
• Availability of definite diagnosis

You may not qualify if:

• Lack of segmentations

Sponsors & Collaborators

• Maastricht University: lead
• Centre Hospitalier Universitaire de Liege: collaborator
• University Hospital RWTH Aachen University, Aachen, Germany.: collaborator
• Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang street, Dalian 116001, China: collaborator
• University of California, San Francisco: collaborator

Study Sites (1)

Maastricht University

Maastricht, Limburg, 6229ER, Netherlands

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 12, 2019
• First Posted: November 15, 2019
• Study Start: March 10, 2019
• Primary Completion: November 7, 2019
• Study Completion: October 31, 2020
• Last Updated: April 6, 2020

Record last verified: 2019-11

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)