NCT05221814

Pathological Classification of Pulmonary Nodules in Images Using Deep Learning

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (estimated)
• Sponsor: Jiangxi Provincial Cancer Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

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.

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

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	gross pathologic photo based deep learning model	Whether apply gross pathologic photo based deep learning model to predict pathologic subtype

Timeline

• Start date: 2020-06-01
• Primary completion: 2022-06-01
• Completion: 2023-01-01
• First posted: 2022-02-03
• Last updated: 2022-02-03

Locations

2 sites across 1 country: China

Regulatory

• FDA-regulated drug study