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PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images by Yuchong Zhang; Hui Qu; Yumeng Tian; Fangjian Na; Jinshan Yan; Ying Wu; Xiaoyu Cui; Zhi Li; Mingfang Zhao is a Medicine article available to read on EtoBox.

What is PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images about?

## 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 6:2:2. 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 con

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Author

Yuchong Zhang; Hui Qu; Yumeng Tian; Fangjian Na; Jinshan Yan; Ying Wu; Xiaoyu Cui; Zhi Li; Mingfang Zhao

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Medicine (Health Sciences)