Objectives: Employing 18F-FDG PET/CT radiomic properties both within and surrounding tumors, in conjunction with clinical attributes, to precisely differentiate among several pathological subtypes of non-small-cell lung cancer (NSCLC).Approaches: The study comprised 222 patients who received 18F-FDG PET/CT scans from January 2015 to December 2020 and were later diagnosed with NSCLC, encompassing 169 cases of lung adenocarcinoma (LUAD) and 53 cases of lung squamous cell carcinoma (LUSC). They were arbitrarily allocated into a training group and a validation group in a 7:3 ratio. Radiomics feature extraction was conducted on 18F-FDG PET/CT images of primary tumors and adjacent tumor regions with LIFE-x (5.2.0). A multivariate logistic regression analysis was employed to develop a nomogram for differentiating lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC). The clinical efficacy of each model was assessed and contrasted utilizing accuracy (Acc), sensitivity (Sen), specificity (Spe), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).Outcomes: The nomogram model that integrates 18F-FDG PET/CT radiomics features with clinical characteristics showed superior efficacy in differentiating adenocarcinoma from squamous cell carcinoma in NSCLC patients, surpassing models based only on PET or CT radiomics. The validation set exhibited an Area under curve (AUC) of 0.880, an Acc of 0.929, a Sen of 0.808, and a Spe of 0.962. This model exhibits the most superior overall performance in DCA.Conclusions: A nomogram model integrating radiomic features derived from 18F-FDG PET/CT images of tumors and adjacent tissues with clinical characteristics can effectively differentiate between LUAD and LUSC.
目的：利用肿瘤内部及周围组织的18F-FDG PET/CT影像组学特征，结合临床特征，精确区分非小细胞肺癌（NSCLC）的不同病理亚型。方法：本研究纳入2015年1月至2020年12月期间接受18F-FDG PET/CT检查并确诊为NSCLC的222例患者，包括169例肺腺癌（LUAD）和53例肺鳞状细胞癌（LUSC）。按7:3比例随机分为训练组和验证组。使用LIFE-x（5.2.0）软件对原发肿瘤及瘤周区域的18F-FDG PET/CT图像进行影像组学特征提取。采用多因素逻辑回归分析构建区分肺腺癌与肺鳞状细胞癌的列线图模型。通过准确率（Acc）、灵敏度（Sen）、特异度（Spe）、受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估并对比各模型的临床效能。结果：融合18F-FDG PET/CT影像组学特征与临床特征的列线图模型在区分NSCLC腺癌与鳞癌方面表现最优，其效能优于单纯基于PET或CT影像组学的模型。验证集曲线下面积（AUC）为0.880，准确率0.929，灵敏度0.808，特异度0.962。该模型在DCA中展现出最佳综合性能。结论：整合肿瘤及瘤周组织18F-FDG PET/CT影像组学特征与临床特征的列线图模型能有效区分肺腺癌与肺鳞状细胞癌。
肿瘤(癌症)患者之家