Wang Xiao-Yi, Zhao Yan-Feng, Yang Lin, Liu Ying, Yang Yi-Kun, Wu Ning
Department of Imaging Diagnosis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Pathology, National Cancer Center//National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Transl Cancer Res. 2020 Oct;9(10):6412-6422. doi: 10.21037/tcr-20-1934.
Tumor spread through air spaces (STAS) is an important pattern of invasion and impacts the frequency and location of recurrence. The objective was to assess the correlation between metabolic tumor burden of positron emission tomography/computed tomography (PET/CT) and 2015 World Health Organization (WHO) classification of lung adenocarcinoma, and to establish a risk prediction model of STAS.
We reviewed 127 consecutive patients. The SUV, SUV, SUV, MTV, TLG, diameter, and CTV were measured. All risk factors were analyzed by multivariate logistic regression analysis; regression coefficients and odds ratios were calculated for independent risk factors. A STAS risk prediction model was created using the regression coefficients to determine the predictive probability (PP).
The nodule types and SUV were significantly correlated with 2015 WHO pathological categories (P0.001). Most of (83.3%) the lepidic predominant adenocarcinoma (LPA) appeared as non-solid or part-solid nodules with the lowest SUV (P<0.05). There was a significant difference in STAS distribution among different nodule types (P=0.000). STAS was significantly correlated with SUV (P=0.000), SUV (P=0.000), SUV (P=0.000), TLG (P=0.001), and diameter (P=0.044). The risk prediction model of STAS was established. The PP of STAS and the incidence of STAS were analyzed using the ROC curve (AUC =0.759, P=0.000). The sensitivity, specificity, and accuracy of the predictive model for STAS were 47.1%, 88.6%, and 71.1%, respectively.
The LPA appeared as non-solid nodule with low SUV without STAS has a good prognosis. SUV and TLG are valuable predictive indices in the prediction of STAS. The predictive model developed in predicting the incidence of STAS has good specificity and accuracy.
肿瘤气腔播散（STAS）是一种重要的侵袭模式，影响复发频率和部位。目的是评估正电子发射断层扫描/计算机断层扫描（PET/CT）的代谢肿瘤负荷与2015年世界卫生组织（WHO）肺腺癌分类之间的相关性，并建立STAS风险预测模型。
我们回顾性分析了127例连续患者。测量了SUV、SUV、SUV、代谢体积（MTV）、总病变糖酵解（TLG）、直径和实性肿瘤最大径（CTV）。通过多因素逻辑回归分析所有危险因素；计算独立危险因素的回归系数和比值比。使用回归系数创建STAS风险预测模型以确定预测概率（PP）。
结节类型和SUV与2015年WHO病理分类显著相关（P<0.001）。大多数（83.3%）鳞屑状为主型腺癌（LPA）表现为非实性或部分实性结节，SUV最低（P<0.05）。不同结节类型之间STAS分布存在显著差异（P=0.000）。STAS与SUV（P=0.000）、SUV（P=0.000）、SUV（P=0.000）、TLG（P=0.001）和直径（P=0.044）显著相关。建立了STAS风险预测模型。使用ROC曲线分析STAS的PP和STAS发生率（AUC =0.759，P=0.000）。STAS预测模型的敏感性、特异性和准确性分别为47.1%、88.6%和71.1%。
无STAS的LPA表现为SUV低的非实性结节，预后良好。SUV和TLG是预测STAS的有价值指标。所建立的预测STAS发生率的模型具有良好的特异性和准确性。
