Pathological Classification of Pulmonary Nodules in Images Using Deep Learning

Lung Cancer Clinical Trial

Official title:

Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05221814
• Other study ID #: 2021ky228
• Status: Recruiting
• Start date: June 1, 2020
• Est. completion date: January 1, 2023

Study information

• Verified date: January 2022
• Source: Jiangxi Provincial Cancer Hospital
• Contact: Haiyu Zhou
• Phone: +8613710342002
• Email: lungcancer[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2000
• Est. completion date: January 1, 2023
• Est. primary completion date: June 1, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Lung Cancer
• Multiple Pulmonary Nodules

Intervention

Diagnostic Test:
• gross pathologic photo based deep learning model
Whether apply gross pathologic photo based deep learning model to predict pathologic subtype

Locations

Country	Name	City	State
China	Guagndong Provincial People's Hospital	Guangzhou	Guangdong
China	Jiangxi Cancer Hospital	Nanchang	Jiangxi

Sponsors (2)

Lead Sponsor	Collaborator
Jiangxi Provincial Cancer Hospital	Guangdong Provincial People's Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	through study completion, an average of 2 year
Primary	Area Under the Curve (AUC)	The area under the ROC curve based the predicton efficency of model	through study completion, an average of 2 year