PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.
BMC Cancer
; 23(1): 936, 2023 Oct 03.
Article
em En
| MEDLINE
| ID: mdl-37789252
ABSTRACT
OBJECTIVE:
To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning.METHODS:
We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 622. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility.RESULTS:
Four hundred seventy-seven patients were included and the nodules were divided into six groups benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89.CONCLUSIONS:
In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Adenocarcinoma
/
Nódulos Pulmonares Múltiplos
/
Adenocarcinoma de Pulmão
/
Neoplasias Pulmonares
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Cancer
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China