Artificial intelligence-based determination strategy for lung cancer spread through air space (STAS)

Zheng Zhang; Yuan Lin; Zhongze Gu

doi:10.1117/12.3026949

8 April 2024 Artificial intelligence-based determination strategy for lung cancer spread through air space (STAS)

Zheng Zhang, Yuan Lin, Zhongze Gu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130904T (2024) https://doi.org/10.1117/12.3026949
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Diagnosing alveolar septal thickening remains challenging, and predicting alveolar diffusion is crucial for preoperative radiological assessment of pulmonary conditions in patients. Non-invasive screening of early-stage patients using imaging techniques holds significant clinical importance. In response to this challenge, we effectively predict alveolar diffusion in adenocarcinoma nodules using a radiomics and deep learning combined method, named the Spread Through Air Space (STAS) Prediction Model. Specifically, by fusing radiomic features from the lung cancer nodule lesion region with deep learning features, the mutual enhancement of feature representation leads to a remarkable area under the curve (AUC) of 0.830 in the binary classification task (STAS patients vs. non-STAS patients) for the radiomics model. Moreover, the deep learning model, utilizing ResNet-18 network to extract deep features from tumor blocks, achieves an AUC of 0.841. The combined model, incorporating both deep learning and traditional radiomic features, outperforms standalone deep learning and radiomics models by 3.50% and 4.60%, respectively. The introduction of radiomic features enhances the model’s interpretability, demonstrating promising clinical applicability.

Citation Download Citation

Zheng Zhang, Yuan Lin, and Zhongze Gu "Artificial intelligence-based determination strategy for lung cancer spread through air space (STAS)", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130904T (8 April 2024); https://doi.org/10.1117/12.3026949

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