Pathological Classification of Pulmonary Nodules in Images Using Deep Learning

Jiangxi Provincial Cancer Hospital

Status: Unknown

Conditions: Artificial Intelligence; Lung Cancer

Treatments: Diagnostic Test: gross pathologic photo based deep learning model

Study type: Observational

Funder types: Other

Identifiers

NCT05221814

2021ky228

Details and patient eligibility

About

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.

Full 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.

Enrollment: 2,000 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

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.