Artificial Intelligence, Lung Cancer
Conditions
Keywords
lung cancer, pathologic prediction, deep learning
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.
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.
Interventions
DIAGNOSTIC_TESTgross pathologic photo based deep learning model
Whether apply gross pathologic photo based deep learning model to predict pathologic subtype
Sponsors
Guangdong Provincial People's Hospital
Jiangxi Provincial Cancer Hospital
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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 |
Countries
China
Contacts
Primary ContactHaiyu Zhou
Backup ContactShaowei Wu
Outcome results
None listed