DetectionNCT04164186
Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images
An observational study of Detection and Segmentation, run by Maastricht University at 1 site in Netherlands.
Trial 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). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Detection, Segmentation
- What is being tested?
- Automatic detection and segmentation of NSCLC tumors
- How many people
- 1,043 participants
- Where
- 1 siteNetherlands
- Study dates
- 10 Mar 2019to 31 Oct 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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
Who may be able to join
- Availability of CT scans
- Availability of definite diagnosis
Who cannot join
- Lack of segmentations
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images: Quantitative and Qualitative Evaluation
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.
Read the detailed description
In this study, we aim to develop and test 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. We will assess the level of agreement between a group of radiologists, performing manual versus semi-automatic tumour segmentation. To do so, we will provide radiologists with two sets of CT scans. The first set will be segmented manually; the second one will be segmented using the automated software program.
Subsequently, we will use the inter- and intra-observer variance from the clinical study in a simulation or modeling study. We also compare the time needed and the consistency in segmentations by the software to medical doctors performance.
Reliability and Agreement study:
Primary tumours of 25 lung cancer patients will be delineated by 6 segmentation experts.
- Assess agreement between automatic segmentation and radiologists' segmentation The primary tumours of 25 patients will be manually segmented by the radiologists and automatically by the the tool. The time needed to perform this task and the reproducibility of the segmentation will be recorded. The degree of overlap between the ROs and the automatic contour will be assessed pairwise using the Dice coefficient.
- Delination of tumours by the experts, assisted by the software tool For another 25 patients, the experts will be provided with an automatic delineation, performed by the tool. They have the possibility to adjust and validate it. The time needed will be recorded. The difference between the mean overlap fraction in the first situation (manual delineation of experts) and the second situation (delineation of experts+ software tool) will be assessed, using a multi-observer Dice coefficient.
- Assessment of intra-observer variance The experts will repeat the segmentation of the lung tumours after 2 weeks. They will repeat the manual segmentation (n=25) and the semi-automatic segmentation (n=25). This will make it possible to assess the intra-observer variance in both situations.
- Qualitative assessment of the experts' preferences using an in-house developed visualization toolbox.
- Conditions
- Detection, Segmentation
- Being tested
- Automatic detection and segmentation of NSCLC tumors
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection of NSCLC on CT scans
Automatic detection of NSCLC tumors
Measured: November, 2019
Segmentation of NSCLC scans
Automatic segmentation of NSCLC tumors
Measured: November, 2019
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Nov 2019
- Study start10 Mar 2019
- Primary completion7 Nov 2019
- Study completion31 Oct 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DetectionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,043 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- Centre Hospitalier Universitaire de Liege, University Hospital, Aachen, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang street, Dalian 116001, China, University of California, San Francisco
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04164186
- Sponsor's protocol ID
- ALSP
- Status last verified
- Nov 2019
- First submitted
- 12 Nov 2019
- Last updated
- 6 Apr 2020
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Currently, there is no plan to make it public
Study at a glance
From the registry record, last updated by the sponsor on 6 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Fully Automated Pipeline for the Detection and Segmentation of Non-Small Cell Lung Cancer (NSCLC) on CT Images: Quantitative and Qualitative Evaluation
- ClinicalTrials.gov ID
- NCT04164186
- Acronym / sponsor ID
- ALSP
- Conditions
- Detection, Segmentation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,043 participants (actual)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- Centre Hospitalier Universitaire de Liege, University Hospital, Aachen, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang street, Dalian 116001, China, University of California, San Francisco
- Study start
- 10 Mar 2019 (actual)
- Primary completion
- 7 Nov 2019 (actual)
- Study completion
- 31 Oct 2020 (estimated)
- First posted
- 15 Nov 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
In this study, we aim to develop and test 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. We will assess the level of agreement between a group of radiologists, performing manual versus semi-automatic tumour segmentation. To do so, we will provide radiologists with two sets of CT scans. The first set will be segmented manually; the second one will be segmented using the automated software program.
Subsequently, we will use the inter- and intra-observer variance from the clinical study in a simulation or modeling study. We also compare the time needed and the consistency in segmentations by the software to medical doctors performance.
Reliability and Agreement study:
Primary tumours of 25 lung cancer patients will be delineated by 6 segmentation experts.
- Assess agreement between automatic segmentation and radiologists' segmentation The primary tumours of 25 patients will be manually segmented by the radiologists and automatically by the the tool. The time needed to perform this task and the reproducibility of the segmentation will be recorded. The degree of overlap between the ROs and the automatic contour will be assessed pairwise using the Dice coefficient.
- Delination of tumours by the experts, assisted by the software tool For another 25 patients, the experts will be provided with an automatic delineation, performed by the tool. They have the possibility to adjust and validate it. The time needed will be recorded. The difference between the mean overlap fraction in the first situation (manual delineation of experts) and the second situation (delineation of experts+ software tool) will be assessed, using a multi-observer Dice coefficient.
- Assessment of intra-observer variance The experts will repeat the segmentation of the lung tumours after 2 weeks. They will repeat the manual segmentation (n=25) and the semi-automatic segmentation (n=25). This will make it possible to assess the intra-observer variance in both situations.
- Qualitative assessment of the experts' preferences using an in-house developed visualization toolbox.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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
Key inclusion criteria (2 listed)
- Availability of CT scans
- Availability of definite diagnosis
Key exclusion criteria (1 listed)
- Lack of segmentations
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Automatic detection and segmentation of NSCLC tumorsan 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. | Other | – |
Primary outcome
Detection of NSCLC on CT scans
Time frame: November, 2019
Automatic detection of NSCLC tumors
Segmentation of NSCLC scans
Time frame: November, 2019
Automatic segmentation of NSCLC tumors
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Apr 2020.
- Individual participant dataSharing planned: No. Currently, there is no plan to make it public
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Detection and Segmentation, run by Maastricht University at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 6 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection of NSCLC on CT scans (time frame: November, 2019). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Apr 2020.