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双能 CT 三维碘图定量预测浸润性肺腺癌程序性死亡配体 1 的表达。

Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma.

机构信息

Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 5650871, Japan.

Future Diagnostic Radiology, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 5650871, Japan.

出版信息

Sci Rep. 2024 Aug 7;14(1):18310. doi: 10.1038/s41598-024-69470-9.

Abstract

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR]  7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

摘要

我们研究了三维(3D)碘密度直方图纹理特征与使用免疫组化方法检测非小细胞肺癌中程序性死亡配体 1(PD-L1)表达之间的关系。连续扫描 37 例患者行双能 CT(DECT)。获得平扫及增强(3 分钟延迟)图像。对每个结节进行 3D 纹理分析,从碘密度图和细胞外容积(ECV)中获得 7 个特征(最大值、最小值、中位数、平均值、标准差、偏度和峰度)。病理学家使用 PD-L1 免疫组化评估肿瘤比例评分(TPS,%):PD-L1 高(TPS≥50%)和低或阴性表达(TPS<50%)。使用逻辑回归分析评估 PD-L1 表达与每个 8 个参数之间的关系。多变量逻辑回归分析显示,偏度和 ECV 是与高 PD-L1 表达相关的独立指标(偏度:优势比[OR]7.1 [95% CI 1.1, 45.6],p=0.039;ECV:OR 6.6 [95% CI 1.1, 38.4],p=0.037)。在受试者工作特征分析中,偏度和 ECV 组合的曲线下面积为 0.83(95% CI 0.67, 0.93),敏感性为 64%,特异性为 96%。来自 3D 碘密度直方图和双能 CT 的 ECV 的偏度是预测 PD-L1 表达的重要因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14d9/11306593/5789bf6d1aec/41598_2024_69470_Fig1_HTML.jpg

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