从原发性肺腺癌提取的放射组学特征预测胸内淋巴结转移的价值：系统评价和荟萃分析。

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

背景：放射组学在肺腺癌胸内淋巴结转移（LNM）中的应用越来越多，但从原发肿瘤预测 LNM 的放射组学诊断性能尚未得到系统评价。因此，本研究旨在提供一个关于使用放射组学方法预测肺腺癌 LNM 可能性的方法学质量和诊断性能的综合概述。

方法：从文献数据库（如 PubMed、Embase、Web of Science 核心合集和 Cochrane 图书馆）中收集研究。使用放射组学质量评分（RQS）和诊断准确性研究的质量评估-2（QUADAS-2）来评估每个研究的质量。计算训练和验证队列中最佳放射组学模型的合并敏感性、特异性和曲线下面积（AUC）。还进行了亚组和 meta 回归分析。

结果：纳入了 2018 年至 2022 年间的 17 项研究，每项研究的患者数为 159 至 1202 例，其中 10 项研究具有定量评估的足够数据。RQS 的百分比在 11.1%至 44.4%之间，大多数研究在 QUADAS-2 中被认为具有低偏倚风险和较少的适用性问题。Pyradiomics 和逻辑回归分析分别是最常用的放射组学特征提取和选择软件和方法。此外，17 项研究中的最佳预测模型主要基于放射组学特征与非放射组学特征（语义特征和/或临床特征）的组合。训练队列的合并敏感性、特异性和 AUC 分别为 0.84（95%置信区间[0.73-0.91]）、0.88（95%置信区间[0.81-0.93]）和 0.93（95%置信区间[0.90-0.95]）。对于验证队列，合并敏感性、特异性和 AUC 分别为 0.89（95%置信区间[0.82-0.94]）、0.86（95%置信区间[0.74-0.93]）和 0.94（95%置信区间[0.91-0.96]）。

结论：基于原发肿瘤的放射组学特征有可能预测肺腺癌术前 LNM。然而，放射组学工作流程需要标准化，以更好地促进放射组学的适用性。

试验注册：CRD42022375712。