Pathological Classification of Pulmonary Nodules in Images Using Deep Learning

• Sponsor: Jiangxi Provincial Cancer Hospital (Other)
• Overall Status: Recruiting
• CT.gov ID: NCT05221814
• Collaborator: Guangdong Provincial People's Hospital (Other)
• Enrollment: 2,000
• Locations: 2
• Anticipated Duration (Months): 31
• Patients Per Site: 1000
• Patients Per Site Per Month: 32.2

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Lung Cancer; Artificial Intelligence	Diagnostic Test: gross pathologic photo based deep learning model

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

• Study Type: Observational
• Anticipated Enrollment: 2000 participants
• Observational Model: Other
• Time Perspective: Retrospective
• Official Title: Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning
• Actual Study Start Date: Jun 1, 2020
• Anticipated Primary Completion Date: Jun 1, 2022
• Anticipated Study Completion Date: Jan 1, 2023

Outcome Measures

Primary Outcome Measures

1. 1. Pathological subtype [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.

2. Area Under the Curve (AUC) [through study completion, an average of 2 year]

   The area under the ROC curve based the predicton efficency of model

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 80 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Inclusion Criteria:

1. Male or female，18 years and older.

2. Patients haven't undergone any therapy.

3. The pulmonary nodules were confirmed AIS, MIA or IAC.

4. The sizes of pulmonary nodules were less than 3cm.

5. The images were jpg format.

Exclusion Criteria:

1. Suffering from other tumor disease before or at the same time.

2. Images with poor quality or low resolution that precluded proper classification.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Guagndong Provincial People's Hospital	Guangzhou	Guangdong	China	510000
2	Jiangxi Cancer Hospital	Nanchang	Jiangxi	China	330000

Sponsors and Collaborators

• Jiangxi Provincial Cancer Hospital
• Guangdong Provincial People's Hospital

Investigators

• Principal Investigator: Haiyu Zhou, Guangdong Provincial People's Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Haiyu Zhou, vice-president, Jiangxi Provincial Cancer Hospital
• ClinicalTrials.gov Identifier: NCT05221814
• Other Study ID Numbers: 2021ky228
• First Posted: Feb 3, 2022
• Last Update Posted: Feb 3, 2022
• Last Verified: Jan 1, 2022

• Studies a U.S. FDA-regulated Drug Product: Yes
• Studies a U.S. FDA-regulated Device Product: No
• Product Manufactured in and Exported from the U.S.: No
• Keywords provided by Haiyu Zhou, vice-president, Jiangxi Provincial Cancer Hospital: lung cancer; pathologic prediction; deep learning
• Additional relevant MeSH terms: Multiple Pulmonary Nodules

Study Results

No Results Posted as of Feb 3, 2022