Research progress in predicting the risk of lymphatic or hematologic metastasis based on chest CT in early non-small cell lung cancer

Abstract

Non-small cell lung cancer (NSCLC) accounts for 80%-85% of lung cancer and is one of the malignant tumors that seriously endanger human health. Treatment of early NSCLC is based on surgical excision or stereotactic body radiation therapy. Whether there is regional lymphatic metastasis when diagnosis is confirmed will affect the choice of local treatment, and whether there is still a risk of lymphatic and hematologic metastasis after the completion of local therapy will be the basis for accurate decision of adjuvant therapy. How to predict the risk of lymphatic or hematologic metastasis of NSCLC remains a challenge. With the development of tumor and the plasticity of treatment, heterogeneity of biological characteristics of tumors in time and space seriously affects the accuracy of clinical diagnosis, treatment and prognostic prediction. Due to the heterogeneity of tumor, it is difficult for invasive biopsy to show the full picture of tumor biological characteristics as a gold standard, which promotes clinical attention to non-invasive methods, such as medical images, to identify biological features. The method to identify tumor biological features based on medical images experiences from the qualitative analysis of artificial visuals to the modeling of advanced statistical methods for manual extraction of imaging features, and then to the application of radiomics and deep learning models, which provide new possibilities for accurate and efficient medical imaging analysis. Based on chest computed tomography (CT) imaging, this paper summarized the progress of research on the prediction of risk of lymphatic and hematologic metastasis, an important factor affecting early NSCLC treatment decision-making.

Keywords

• |non-small cell lung cancer|radiomics|deep learning|lymphatic metastasis|hematologic metastasis