探讨18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDG PET/CT）放射组学在结直肠癌（CRC）患者中区分原发性肺癌（PLC）与孤立性肺转移（SLM）的价值和适用性。回顾性研究纳入了 103 名 CRC 和孤立性肺结节 (SPN) 患者。使用最小绝对收缩和选择算子（LASSO）筛选最佳放射组学特征并建立 PET/CT 放射组学模型。开发了 PET/CT 视觉和复杂模型（将放射组学与 PET/CT 视觉特征相结合）。受试者工作特征（ROC）曲线下面积（AUC）用于确定模型的预测价值和诊断效率。用于区分 PLC 和 SLM 的 PET/CT 放射组学模型的 AUC 为 0.872（95% CI：0.806） -0.939），与视觉结果（0.829 [95% CI：0.749-0.908；p = .352]）没有不同。然而，复杂模型的 AUC (0.936 [95% CI:0.892-0.981]) 显着高于 PET/CT 放射组学 (p = .005) 和视觉模型 (p = .001)。 PET/CT 放射组学区分 PLC 与 SLM 的敏感性 (SEN)、特异性 (SPE)、准确性 (ACC)、阳性预测值 (PPV) 和阴性预测值 (NPV) 分别为 0.720、0.887、0.806、0.857 和分别为0.770。PET/CT影像组学可以有效区分CRC和SPN患者的PLC和SLM，并指导个体化治疗的实施。

To investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC).This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models.The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806-0.939), which was not different from that of the visual (0.829 [95% CI: 0.749-0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892-0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively.PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment.