Lung CancerNCT05221814
Pathological Classification of Pulmonary Nodules in Images Using Deep Learning
An observational study of Lung Cancer and Artificial Intelligence, run by Jiangxi Provincial Cancer Hospital at 2 sites in China.
Trial summary
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Lung Cancer, Artificial Intelligence
- What is being tested?
- gross pathologic photo based deep learning model
- How many people
- 2,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jun 2020to 1 Jan 2023
- 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 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients in Guangdong Provincial People's hospital from June 30, 2020 to September 15, 2021.
Who may be able to join
- Male or female，18 years and older.
- Patients haven't undergone any therapy.
- The pulmonary nodules were confirmed AIS, MIA or IAC.
- The sizes of pulmonary nodules were less than 3cm.
- The images were jpg format.
Who cannot join
- Suffering from other tumor disease before or at the same time.
- Images with poor quality or low resolution that precluded proper classification.
Trial locations and contacts
2 sites in 1 country.
China2 sites, 2 recruiting
- Recruiting
Guagndong Provincial People's Hospital
Guangzhou, Guangdong, 510000
Haiyu Zhou, PhD, *8613710342002, lungcancer@163.com
- Recruiting
Jiangxi Cancer Hospital
Nanchang, Jiangxi, 330000
About this study
Official title: Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.
Read the detailed description
All white-light photographs of pulmonary nodules from phones of pathologically confirmed adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were retrospectively collected from consecutive patients who underwent surgery between June 30, 2020 and September 15, 2021 at Guangdong Provincial People's Hospital.Finally, a total of 1037 white-light images from 973 individuals were included in the study. The entire dataset was divided into training and test datasets, which were mutually exclusive, using random sampling. Of these, 830 images were used as the training dataset and 104 images from were used as the test dataset. The CNN model was used in classifying images, namely, Resnet-50. For the CNN model, pretrained model with the ImageNet Dataset were adopted using transfer learning. After constructing the CNN models using the training dataset, the performance of the models was evaluated using the test dataset and the prospective validation dataset.
- Conditions
- Lung Cancer, Artificial Intelligence
- Being tested
- gross pathologic photo based deep learning model
- Keywords
- lung cancer, pathologic prediction, deep learning
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
gross pathologic photo based deep learning modelDiagnostic test
Whether apply gross pathologic photo based deep learning model to predict pathologic subtype
What is being measured
The primary outcome is the main question the study is designed to answer.
1. Pathological subtype
According to WHO classification of pulmonary tumors in 2020, this study classify pulmonary tumors into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). We would collect the reports of pathological type of pulmonary nodules after surgery.
Measured: through study completion, an average of 2 year
Area Under the Curve (AUC)
The area under the ROC curve based the predicton efficency of model
Measured: through study completion, an average of 2 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Feb 2022
- Study start1 Jun 2020
- Primary completion1 Jun 2022 (estimated)
- Study completion1 Jan 2023 (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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Jiangxi Provincial Cancer Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Responsible party
- Principal investigator, Haiyu Zhou, Jiangxi Provincial Cancer Hospital
- Investigators
- Haiyu Zhou, Principal investigator, Guangdong Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05221814
- Sponsor's protocol ID
- 2021ky228
- Status last verified
- Jan 2022
- First submitted
- 5 Jan 2022
- Last updated
- 3 Feb 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- Yes
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning
- ClinicalTrials.gov ID
- NCT05221814
- Acronym / sponsor ID
- 2021ky228
- Conditions
- Lung Cancer, Artificial Intelligence
- Keywords
- lung cancer, pathologic prediction, deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Jiangxi Provincial Cancer HospitalUniversity, hospital or other
- Collaborators
- Guangdong Provincial People's Hospital
- Study start
- 1 Jun 2020 (actual)
- Primary completion
- 1 Jun 2022 (estimated)
- Study completion
- 1 Jan 2023 (estimated)
- First posted
- 3 Feb 2022 (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 aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.
Detailed description
All white-light photographs of pulmonary nodules from phones of pathologically confirmed adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were retrospectively collected from consecutive patients who underwent surgery between June 30, 2020 and September 15, 2021 at Guangdong Provincial People's Hospital.Finally, a total of 1037 white-light images from 973 individuals were included in the study. The entire dataset was divided into training and test datasets, which were mutually exclusive, using random sampling. Of these, 830 images were used as the training dataset and 104 images from were used as the test dataset. The CNN model was used in classifying images, namely, Resnet-50. For the CNN model, pretrained model with the ImageNet Dataset were adopted using transfer learning. After constructing the CNN models using the training dataset, the performance of the models was evaluated using the test dataset and the prospective validation dataset.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients in Guangdong Provincial People's hospital from June 30, 2020 to September 15, 2021.
Key inclusion criteria (5 listed)
- Male or female，18 years and older.
- Patients haven't undergone any therapy.
- The pulmonary nodules were confirmed AIS, MIA or IAC.
- The sizes of pulmonary nodules were less than 3cm.
- The images were jpg format.
Key exclusion criteria (2 listed)
- Suffering from other tumor disease before or at the same time.
- Images with poor quality or low resolution that precluded proper classification.
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 |
|---|---|---|
| gross pathologic photo based deep learning modelWhether apply gross pathologic photo based deep learning model to predict pathologic subtype | Diagnostic test | – |
Primary outcome
1. Pathological subtype
Time frame: through study completion, an average of 2 year
According to WHO classification of pulmonary tumors in 2020, this study classify pulmonary tumors into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). We would collect the reports of pathological type of pulmonary nodules after surgery.
Area Under the Curve (AUC)
Time frame: through study completion, an average of 2 year
The area under the ROC curve based the predicton efficency of model
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Feb 2022.
Related trials for Lung Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
A Prospective ,Multicenter ,Non-interventional ,Real-world Study Observing Changes in Molecular Markers Osimertinib Targeted Therapy in EGFRm NSCLC
Non Small Cell Lung Cancer
Active, not recruiting
Streamlined Geriatric and Oncological Evaluation Based On IC Technology
Breast Cancer, Comorbidities and Coexisting Conditions
RecruitingPhase 3
A Study Evaluating Sotorasib Platinum Doublet Combination Versus Pembrolizumab Platinum Doublet Combination as a Front-Line Therapy in Participants With Stage IV or Advanced Stage IIIB/C Nonsquamous Non-Small Cell Lung Cancers (CodeBreaK 202)
Non-Small Cell Lung Cancer (NSCLC)
Active, not recruitingPhase 2
Datopotamab Deruxtecan (Dato-DXd, DS-1062a) in Advanced and/or Unresectable Non-Small Cell Lung Cancer
Metastatic Lung Cancer, Advanced Non Small Cell Lung Cancer
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.
Haiyu Zhou principal investigator
Guangdong Provincial People's Hospital
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Artificial Intelligence, run by Jiangxi Provincial Cancer Hospital at 2 sites in China.
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 5 inclusion and 2 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 3 Feb 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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?
Contact Haiyu Zhou (+8613710342002, lungcancer@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
1. Pathological subtype (time frame: through study completion, an average of 2 year). 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 3 Feb 2022.