Liu Cong, Wang Yu-Feng, Wang Peng, Guo Feng, Zhao Hong-Ying, Wang Qiang, Shi Zhi-Wei, Li Xiao-Feng
Department of Minimally Invasive Oncology, Xuzhou New Health Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China.
Department of Nuclear Medicine, Xuzhou Cancer Hospital (The Xuzhou Hospital Affiliated to Jiangsu University), Xuzhou, Jiangsu 221000, P.R. China.
Oncol Lett. 2024 Jan 25;27(3):122. doi: 10.3892/ol.2024.14255. eCollection 2024 Mar.
Spread Through Air Spaces (STAS) is involved in lung adenocarcinoma (LUAD) recurrence, where cancer cells spread into adjacent lung tissue, impacting surgical planning and prognosis assessment. Radiomics-based models show promise in predicting STAS preoperatively, enhancing surgical precision and prognostic evaluations. The present study performed network meta-analysis to assess the predictive efficacy of imaging models for STAS in LUAD. Data were systematically sourced from PubMed, Embase, Scopus, Wiley and Web of Science, according to the Cochrane Handbook for Systematic Reviews of Interventions) and A Measurement Tool to Assess systematic Reviews 2. Using Stata software v17.0 for meta-analysis, surface under the cumulative ranking area (SUCRA) was applied to identify the most effective diagnostic method. Quality assessments were performed using Cochrane Collaboration's risk-of-bias tool and publication bias was assessed using Deeks' funnel plot. The analysis encompassed 14 articles, involving 3,734 patients, and assessed 17 predictive models for STAS in LUAD. According to comprehensive analysis of SUCRA, the machine learning (ML)_Peri_tumour model had the highest accuracy (56.5), the Features_computed tomography (CT) model had the highest sensitivity (51.9) and the positron emission tomography (pet)_CT model had the highest specificity (53.9). ML_Peri_tumour model had the highest predictive performance. The accuracy was as follows: ML_Peri_tumour vs. Features_CT [relative risk (RR)=1.14; 95% confidence interval (CI), 0.99-1.32]; ML_Peri_tumour vs. ML_Tumour (RR=1.04; 95% CI, 0.83-1.30) and ML_Peri_tumour vs. pet_CT (RR=1.04; 95% CI, 0.84-1.29). Comparative analyses revealed heightened predictive accuracy of the ML_Peri_tumour compared with other models. Nonetheless, the field of radiological feature analysis for STAS prediction remains nascent, necessitating improvements in technical reproducibility and comprehensive model evaluation.
气腔播散（STAS）与肺腺癌（LUAD）复发有关，癌细胞会扩散到相邻肺组织，影响手术规划和预后评估。基于放射组学的模型在术前预测STAS方面显示出前景，可提高手术精度和预后评估。本研究进行网络荟萃分析，以评估LUAD中STAS影像模型的预测效能。数据根据《Cochrane系统评价干预措施手册》和《系统评价测量工具2》，从PubMed、Embase、Scopus、Wiley和Web of Science系统获取。使用Stata软件v17.0进行荟萃分析，应用累积排序曲线下面积（SUCRA）来确定最有效的诊断方法。使用Cochrane协作网的偏倚风险工具进行质量评估，并使用Deeks漏斗图评估发表偏倚。该分析纳入14篇文章，涉及3734例患者，评估了LUAD中17种STAS预测模型。根据SUCRA综合分析，机器学习（ML）_肿瘤周围模型准确性最高（56.5），特征_计算机断层扫描（CT）模型敏感性最高（51.9），正电子发射断层扫描（PET）_CT模型特异性最高（53.9）。ML_肿瘤周围模型预测性能最高。准确性如下：ML_肿瘤周围模型与特征_CT模型[相对危险度（RR）=1.14；95%置信区间（CI），0.99 - 1.32]；ML_肿瘤周围模型与ML_肿瘤模型（RR = 1.04；95%CI，0.83 - 1.30）以及ML_肿瘤周围模型与PET_CT模型（RR = 1.04；95%CI，0.84 - 1.29）。比较分析显示，与其他模型相比，ML_肿瘤周围模型预测准确性更高。尽管如此，用于STAS预测的放射学特征分析领域仍处于起步阶段，需要提高技术可重复性和全面的模型评估。
