Lung CancerNCT03780582
Evaluation of Use of Diagnostic AI for Lung Cancer in Practice
A study testing AI-human interaction in people with Lung Cancer, run by Ensemble Group Holdings, LLC at 1 site in Hong Kong.
Trial summary
This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer
- What is being tested?
- AI-human interaction
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 15 participants
- Where
- 1 siteHong Kong
- Study dates
- 14 Dec 2018to 15 Dec 2019
- Phase
- Interventional
- 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
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- The participant performs radiology screenings professionally
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions.
Read the detailed description
The three conditions are as follows: "probabilistic classification", where the radiologist diagnoses scans using an AI cancer likelihood score; "classification plus detection", where the radiologist see detecting lung nodules in addition to the AI's probabilistic classification score before making her own examination of the CT-scan; and "classification with delayed detection", where the radiologist identifies regions of interest independently of the AI and then sees the AI's detected ROIs.
- Conditions
- Lung Cancer
- Being tested
- AI-human interaction
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-human interactionBehavioural
Exploring what kinds of AI-human interaction improve radiologists detection accuracy.
| Study group | Role | What they receive |
|---|---|---|
| Probabilistic Classification | Experimental | Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. Receives: Behavioral: AI-human interaction |
| Classification Plus Detection | Experimental | Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules. Receives: Behavioral: AI-human interaction |
| Classification With Delayed Detection | Experimental | Radiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions. Receives: Behavioral: AI-human interaction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification accuracy
This compares radiologists' classifications with the ground truth in the tested cases.
Measured: up to 4 months after initiation of evaluation of the test set
Secondary outcomes (1)
detection concordance
Evaluation of concordance between radiologists in the tested cases in detection of lung nodules > 4 mm
Measured: up to 4 months after initiation of evaluation of the test set
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Dec 2018
- Study start14 Dec 2018
- Primary completion15 Dec 2019 (estimated)
- Study completion15 Dec 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 15 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ensemble Group Holdings, LLC
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03780582
- Sponsor's protocol ID
- EN-122018
- Status last verified
- Jul 2019
- First submitted
- 16 Dec 2018
- Last updated
- 23 Jul 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Use of Diagnostic AI for Lung Cancer in Practice
- ClinicalTrials.gov ID
- NCT03780582
- Acronym / sponsor ID
- EN-122018
- Conditions
- Lung Cancer
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 15 participants (estimated)
- Lead sponsor
- Ensemble Group Holdings, LLCIndustry (pharma, biotech, devices)
- Study start
- 14 Dec 2018 (actual)
- Primary completion
- 15 Dec 2019 (estimated)
- Study completion
- 15 Dec 2019 (estimated)
- First posted
- 19 Dec 2018 (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.
This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions.
Detailed description
The three conditions are as follows: "probabilistic classification", where the radiologist diagnoses scans using an AI cancer likelihood score; "classification plus detection", where the radiologist see detecting lung nodules in addition to the AI's probabilistic classification score before making her own examination of the CT-scan; and "classification with delayed detection", where the radiologist identifies regions of interest independently of the AI and then sees the AI's detected ROIs.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Probabilistic Classification | Experimental | Behavioral: AI-human interactionRadiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. |
| Classification Plus Detection | Experimental | Behavioral: AI-human interactionRadiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. They also see ROIs identified by the AI that represent lung nodules. |
| Classification With Delayed Detection | Experimental | Behavioral: AI-human interactionRadiologists see a "score" from 1-100 that represents the AI's prediction of whether the CT-scan comes from a patient with cancer or not before beginning their analysis of the scan. After identifying their own ROIs, the radiologist then can see ROIs identified by the AI that represent lung nodules before making final decisions. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- The participant performs radiology screenings professionally
Key exclusion criteria (1 listed)
- -
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 |
|---|---|---|
| AI-human interactionExploring what kinds of AI-human interaction improve radiologists detection accuracy. | Behavioural | Classification Plus Detection, Classification With Delayed Detection, Probabilistic Classification |
Primary outcome
Classification accuracy
Time frame: up to 4 months after initiation of evaluation of the test set
This compares radiologists' classifications with the ground truth in the tested cases.
1 secondary outcome
detection concordance
Time frame: up to 4 months after initiation of evaluation of the test set
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2019.
- Individual participant dataSharing planned: Undecided
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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?
A study testing AI-human interaction in people with Lung Cancer, run by Ensemble Group Holdings, LLC at 1 site in Hong Kong.
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 1 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 23 Jul 2019. 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 Hong Kong. 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?
Classification accuracy (time frame: up to 4 months after initiation of evaluation of the test set). 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 23 Jul 2019.