Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, China.
Eur Radiol. 2023 Mar;33(3):2105-2117. doi: 10.1007/s00330-022-09174-8. Epub 2022 Oct 29.
To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies.
The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall.
Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed.
• Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.
对基于 CT 的肿瘤周围放射组学特征预测非小细胞肺癌（NSCLC）患者预后的价值进行全面评估，并评价现有研究的质量。
检索 2017 年至 2022 年期间在 PubMed、Embase、Web of Science 和 Cochrane Library 数据库中使用 CT 基于肿瘤周围放射组学特征预测 NSCLC 患者预后的研究。为纳入的研究提取有关患者、CT 扫描仪和放射组学分析的信息。使用放射组学质量评分（RQS）和预测模型风险偏倚评估工具（PROBAST）评估研究质量。
纳入了 13 项研究，共 2942 例患者，来自 2017 年至 2022 年。只有一项研究是前瞻性的，其余均为回顾性设计。69%和 46%的纳入研究分别采用了手动分割和多中心研究。3D-Slicer 和 MATLAB 软件最常用于病变的分割和特征的提取。肿瘤周围区域最常定义为从肿瘤边界扩张 15mm、20mm 或 30mm。研究的中位数 RQS 为 13（范围 4-19），所有纳入的研究总体上均被评估为具有高风险偏倚（ROB）。
基于 CT 图像的肿瘤周围放射组学特征在预测 NSCLC 预后方面显示出一定的前景，但仍需要设计良好的研究和进一步的生物学验证。
• 基于 CT 图像的肿瘤周围放射组学特征对预测非小细胞肺癌的预后有一定的帮助。
• 肿瘤周围区域常从肿瘤边界扩张 15mm 或 20mm，因为这被认为是安全的边界。
• 纳入研究的中位数放射组学质量评分（RQS）为 13（范围 4-19），所有研究总体上均被认为具有高风险偏倚。
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