探讨计算机断层扫描(computed tomography, CT)形态学和放射组学特征鉴别亚厘米实性肺结节(sub-centimeter solid pulmonary nodules, SSPNs)良恶性的临床意义。

选择2021年1月至2023年3月我院收治的SSPNs患者203例(病灶最大直径≤8.0 mm)为对象。根据7︰3随机分为训练集143例和测试集60例。基线胸部低剂量CT判断SSPNs临床和形态特征，提取全肺放射学特征。采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator, LASSO)分析放射学特征和计算放射学特征评分，构建机器学习模型。通过受试者工作特征(receiver operating characteristic, ROC)曲线下面积(area under curve, AUC)、Hosmer-Lemeshow检验和决策曲线分析模型性能。

203例SSPNs中恶性结节检出98例(48.28%)。训练集和测试集中良恶性结节直径、血管集束征、结节-肺界面、毛刺征、胸膜凹陷、空泡征、空气支气管征差异有统计学意义(P<0.05)。多因素Logistic回归分析显示，结节直径、结节-肺界面、毛刺征、胸膜凹陷征、空气支气管征与SSPNs恶性风险相关(P<0.05)，5个CT特征建立形态学特征模型共提取1 783个高通量放射组学特征。相关性分析、Mann-Whitney U检验及LASSO筛选4个放射组学特征(wavelet-HLL_gldm_DependenceNonUniformity、squareroot_ngtdm_Strength、gradient_glcm_Imc1、original_glrlm_LongRunLowGrayLevelEmphasis)建立模型。基于Delong检验，联合模型在训练集和测试集中AUC高于形态学特征模型(Z＝4.618、2.534，P<0.05)和放射组学模型(Z＝2.449、1.982，P<0.05)。H-L拟合优度检验训练集和测试集中联合模型(χ²＝3.899、2.815，P＝0.866、0.945)拟合良好。决策曲线分析显示，高风险阈值0.02~0.98，联合模型较单一模型净收益大。

CT形态学和放射组学特征联合模型在SSPNs良恶性鉴别中诊断性能良好，可为手术高危队列恶性SSPNs临床诊断辅助工具。

To explore the clinical application value of morphological and radiomics feature models based on computed tomography (CT) in the differentiation of benign and malignant sub-centimeter solid pulmonary nodules (SSPNs).

A retrospective analysis was conducted on the CT image data of 203 SSPNs patients from January 2021 to March 2023. They were randomly divided into the training set 143 cases and the test set 60 cases in a ratio of 7︰3. Baseline low-dose chest CT evaluated the clinical and morphological features of SSPNs, while extracting radiological features of the entire lung. The Least Absolute Shrinkage and Selection Operator (LASSO) is used for radiological feature selection and calculating radiological feature scores, and constructing machine learning models. In the test set, the performance of the model is evaluated through the area under the curve (AUC) of receiver operating characteristic(ROC), Hosmer-Lemeshow test, and decision curve.

The detection rate of malignant nodules was 48.28% (98/203). In both the training set and the test set, there were significant differences between the benign group and the malignant group in terms of nodule diameter, vascular convergence sign, nodule-lung interface, spiculation sign, pleural indentation, vacuole sign, and air bronchogram sign (P<0.05). Multivariate Logistic regression analysis showed that nodule diameter, nodule-lung interface, spiculation sign, pleural indentation, and air bronchogram sign were significantly associated with the malignant risk of SSPNs (P<0.05). Morphological feature models were established using these five CT features. A total of 1, 783 high-throughput radiomics features were extracted. After correlation analysis, Mann-Whitney U test, and LASSO contraction, four of the most significant radiomics features were finally screened to establish the model(wavelet-HLL_gldm_DependenceNonUniformity, squareroot_ngtdm_Strength, gradient_glcm_Imc1, original_glrlm_LongRunLowGrayLeve). Based on the Delong test, the AUC of the combined model in both the training set and the test set was significantly higher than that of the morphological feature model (Z=4.618, 2.534, P<0.05) and the radiomics model (Z=2.449, 1.982, P<0.05). After the H-L goodness-of-fit test, the combined model in the training set and the test set (χ²=3.899, 2.815, P<0.05) fitted well. Decision curve analysis shows that when the high-risk threshold is between 0.02 and 0.98, the joint model offers greater net benefits than the single model.

The combined model of CT morphology and radiomics features has good diagnostic performance in differentiating benign and malignant SSPNs and is expected to become an important auxiliary tool for the clinical diagnosis of malignant SSPNs in high-risk surgical cohorts.