基于胸部CT预测早期非小细胞肺癌淋巴道或血道转移风险的研究进展

doi:10.19401/j.cnki.1007-3639.2022.04.007

摘要/Abstract

摘要：

非小细胞肺癌（non-small cell lung cancer,NSCLC）占肺癌的80%~85%,是严重危害人类健康的恶性肿瘤之一。早期NSCLC治疗以手术切除或立体定向放疗等为主,确诊时是否存在淋巴道转移将会影响到局部治疗方法选择,局部治疗完成后是否还存在淋巴道和血道转移风险将成为辅助治疗精准决策的依据。如何预测NSCLC的淋巴道或血道转移风险,仍是一个难题。随着肿瘤发生、发展的演进及治疗的可塑性,肿瘤在时间、空间上生物学特性的异质性严重影响临床诊断、治疗及预后预测的精准性。正是受限于肿瘤的异质性,目前作为金标准的侵入性活检难以反映肿瘤生物学特性的全貌。基于医学图像的肿瘤生物学特性识别方法经历了从人工肉眼定性分析到手动提取影像学特征利用高级统计方法建模,再到影像组学和深度学习模型的发展,使精准高效的医学影像学分析成为可能。本文基于胸部CT影像,从影像组学和深度学习角度综述了影响早期NSCLC治疗决策的重要影响因素,聚焦于淋巴道和血道转移风险预测的研究进展。

关键词: 非小细胞肺癌, 影像组学, 深度学习, 淋巴道转移, 血道转移

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.

Key words: Non-small cell lung cancer, Radiomics, Deep learning, Lymphatic metastasis, Hematologic metastasis

中图分类号:

R734.2

傅圆圆, 侯润萍, 傅小龙. 基于胸部CT预测早期非小细胞肺癌淋巴道或血道转移风险的研究进展[J]. 中国癌症杂志, 2022, 32(4): 343-350.

FU Yuanyuan, HOU Runping, FU Xiaolong. Research progress in predicting the risk of lymphatic or hematologic metastasis based on chest CT in early non-small cell lung cancer[J]. China Oncology, 2022, 32(4): 343-350.

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