Wilk Agata Małgorzata, Kozłowska Emilia, Borys Damian, D'Amico Andrea, Fujarewicz Krzysztof, Gorczewska Izabela, Debosz-Suwinska Iwona, Suwinski Rafał, Smieja Jarosław, Swierniak Andrzej
Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.
Department of Biostatistics and Bioinformatics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland.
Transl Lung Cancer Res. 2023 Jul 31;12(7):1372-1383. doi: 10.21037/tlcr-23-60. Epub 2023 Jul 7.
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and the median overall survival (OS) is approximately 2-3 years among patients with stage III disease. Furthermore, it is one of the deadliest types of cancer globally due to non-specific symptoms and the lack of a biomarker for early detection. The most important decision that clinicians need to make after a lung cancer diagnosis is the selection of a treatment schedule. This decision is based on, among others factors, the risk of developing metastasis.
A cohort of 115 NSCLC patients treated using chemotherapy and radiotherapy (RT) with curative intent was retrospectively collated and included patients for whom positron emission tomography/computed tomography (PET/CT) images, acquired before RT, were available. The PET/CT images were used to compute radiomic features extracted from a region of interest (ROI), the primary tumor. Radiomic and clinical features were then classified to stratify the patients into short and long time to metastasis, and regression analysis was used to predict the risk of metastasis.
Classification based on binarized metastasis-free survival (MFS) was applied with moderate success. Indeed, an accuracy of 0.73 was obtained for the selection of features based on the Wilcoxon test and logistic regression model. However, the Cox regression model for metastasis risk prediction performed very well, with a concordance index (C-index) score equal to 0.84.
It is possible to accurately predict the risk of metastasis in NSCLC patients based on radiomic features. The results demonstrate the potential use of features extracted from cancer imaging in predicting the risk of metastasis.
非小细胞肺癌（NSCLC）是最常见的肺癌类型，III期疾病患者的中位总生存期（OS）约为2至3年。此外，由于症状不特异且缺乏早期检测的生物标志物，它是全球最致命的癌症类型之一。肺癌诊断后临床医生需要做出的最重要决定是选择治疗方案。这一决定除其他因素外，还基于发生转移的风险。
回顾性整理了一组115例接受化疗和放疗（RT）且有治愈意图的NSCLC患者，纳入了放疗前有正电子发射断层扫描/计算机断层扫描（PET/CT）图像的患者。PET/CT图像用于计算从感兴趣区域（ROI）即原发性肿瘤中提取的放射组学特征。然后对放射组学和临床特征进行分类，将患者分为转移时间短和长两组，并使用回归分析预测转移风险。
基于二分类无转移生存期（MFS）的分类取得了一定成功。事实上，基于Wilcoxon检验和逻辑回归模型选择特征时，准确率达到了0.73。然而，用于转移风险预测的Cox回归模型表现非常出色，一致性指数（C-index）得分等于0.84。
基于放射组学特征能够准确预测NSCLC患者的转移风险。结果表明从癌症影像中提取的特征在预测转移风险方面具有潜在用途。
