Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Acad Radiol. 2024 Jul;31(7):2962-2972. doi: 10.1016/j.acra.2024.02.011. Epub 2024 Mar 19.
To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).
Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.
Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).
A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.
评估双能 CT（DECT）参数和定量语义特征在区分表现为磨玻璃结节（GGN）的肺腺癌侵袭性方面的诊断性能。
本研究纳入了 2022 年 6 月至 2023 年 9 月间 69 名在我院接受 DECT 检查且经手术切除的 74 个 GGN 患者。从 DECT 生成的 40-130keV 虚拟单能量图像上计算 CT 值。在未增强 CT 图像上评估定量形态学测量和语义特征，并在病理证实的原位腺癌（AIS）-微浸润性腺癌（MIA）和浸润性腺癌（IAC）之间进行比较。采用多变量逻辑回归分析来识别独立预测因子。通过接受者操作特征曲线（ROC）下面积（AUC）评估诊断性能，并采用 DeLong 检验进行比较。
IAC 的 40-130keV 单能量 CT 值明显高于 AIS-MIA（均 P<0.05）。多变量逻辑分析显示，130keV 的 CT 值（比值比[OR] = 1.02，P=0.013）、最大横截面积长径（OR =1.40，P=0.014）、深或中度叶状分叶征（OR =19.88，P=0.005）和异常结节内血管形态（OR =25.57，P=0.017）是 IAC 的独立预测因子。联合预测模型显示出良好的区分性能，AUC 为 0.966（95.2%的敏感性、94.3%的特异性、94.8%的准确性），显著高于每个危险因素（AUC = 0.791-0.822，均 P<0.05）。
DECT 单能量 CT 值与定量语义特征的多参数联合预测模型有望用于术前区分 GGN 为主的肺腺癌中的 IAC 和 AIS-MIA。
