NCT04164186

Unknown

Fully Automated Pipeline for the Detection and Segmentation ...

Maastricht University Source

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Other study identifiers

Sponsor study identifier

ALSP

Study Type

observational

Actual enrollment (reported)

1,043

Locations

1 country

Sites

1

Timeline

Study Start

March 10, 2019

Actual (reported)

Primary Completion

November 7, 2019

Actual (reported)

Study Completion

October 31, 2020

Estimated (date passed)

This estimate has passed. The registry has not confirmed an actual date for this milestone.

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.

Registry Overview

Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.

Recruitment status: unknown
Actual enrollment (reported): 1,043 participants
Study completion date: October 31, 2020
Estimated (date passed)
This estimate has passed. The registry has not confirmed an actual date for this milestone.
Listed locations: 1 countries · 1 sites

Trial Relationships

Click on a node to explore related trials.

Some registry-reported names shown below are not yet linked in this graph.

Study Timeline

Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.

Study Start
March 10, 2019
Actual (reported)
Primary Completion
November 7, 2019
Actual (reported)
First Submitted
November 12, 2019
Actual (reported)
First Posted
November 15, 2019
Actual (reported)
Study Completion
October 31, 2020
Estimated (date passed)
This estimate has passed. The registry has not confirmed an actual date for this milestone.

Last Updated: April 6, 2020
Status Verified: November 2019
Last Update Submitted: April 3, 2020

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 tumorsOther

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.

Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.

Eligibility Criteria

Sexall

Healthy VolunteersNo

Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

Sampling MethodNon-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

Sponsors & Collaborators

University of California, San Francisco
Othercollaborator
Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang street, Dalian 116001, China
Type not reportedcollaborator

Study Sites (1)

Maastricht University

Maastricht, Limburg, 6229ER, Netherlands

Enrollment

1,043 participants (actual enrollment reported by the registry).

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

Actual (reported)

Availability of definite diagnosis

You may not qualify if:

Lack of segmentations

Contact the study team to confirm eligibility.

Centre Hospitalier Universitaire de Liege

Othercollaborator

Maastricht University

Otherlead

University Hospital, Aachen

Othercollaborator

Primary Completion

November 7, 2019

Actual (reported)

Study Completion

October 31, 2020

Estimated (date passed)

This estimate has passed. The registry has not confirmed an actual date for this milestone.

Last Updated

April 6, 2020

Record last verified: 2019-11

Data Sharing

IPD Sharing: Will not share

Source record

Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.

Source: ClinicalTrials.gov.

JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.

Registry identifier: NCT04164186
Data provider: ClinicalTrials.gov
Source updated: 2020-04-06
Normalization version: ctgov-legacy-1

Open registry record

Source retrieved: 2026-10-25 22:34:42 UTC

Locations

NL(1)

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

Brief Summary

Registry Overview

Trial Relationships

Study Timeline

Study Start

Primary Completion

First Submitted

First Posted

Study Completion

Conditions

Outcome Measures

Primary Outcomes (2)

Interventions

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Data Sharing

Available IPD Datasets

Source record

Locations

Brief Summary

Registry Overview

Trial Relationships

Related Scientific Literature

Study Timeline

Study Start

Primary Completion

First Submitted

First Posted

Study Completion

Conditions

Outcome Measures

Primary Outcomes (2)

Interventions

Eligibility Criteria

You may qualify if:

Sponsors & Collaborators

Study Sites (1)

Enrollment

Study Design

Study Record Dates

You may not qualify if:

Data Sharing

Available IPD Datasets

Source record

Locations