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BMC Cancer
Published in: BMC Cancer 1/2023

Open Access 01-12-2023 | Pulmonary Nodule | Research

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

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

Published in: BMC Cancer | Issue 1/2023

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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 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 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.
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Metadata
Title
PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images
Authors
Yuchong Zhang
Hui Qu
Yumeng Tian
Fangjian Na
Jinshan Yan
Ying Wu
Xiaoyu Cui
Zhi Li
Mingfang Zhao
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2023
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-023-11364-6

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