Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No 1277, Jiefang Avenue, Wuhan, Hubei, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China.
Sci Rep. 2023 Oct 26;13(1):18323. doi: 10.1038/s41598-023-45198-w.
This study aimed to evaluate the diagnostic performances of dual-layer CT (DLCT) for the identification of positive lymph nodes (LNs) in patients with lymphoma and retrospectively included 1165 LNs obtained by biopsy from 78 patients with histologically proven lymphoma, who underwent both pretreatment DLCT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). According to 18F-FDG PET/CT findings as a reference standard, cases were categorized into the LN-negative and LN-positive groups. LNs were then randomly divided at a ratio of 7:3 into the training (n = 809) and validation (n = 356) cohorts. The patients' clinical characteristics and quantitative parameters including spectral curve slope (λ), iodine concentration (IC) on arterial phase (AP) and venous phase (VP) images were compared between the LN-negative and LN-positive groups using Chi-square test, t-test or Mann-Whitney U test for categorical variables or quantitative parameters. Multivariate logistic regression analysis with tenfold cross-validation was performed to establish the most efficient predictive model in the training cohort. The area under the curve (AUC) was used to evaluate the diagnostic value of the predictive model, and differences in AUC were determined by the DeLong test. Moreover, the predictive model was validated in the validation cohort. Repeatability analysis was performed for LNs using intraclass correlation coefficients (ICCs). In the training cohort, long diameter (LD) had the highest AUC as an independent factors compared to other parameter in differentiating LN positivity from LN negativity (p = 0.006 to p < 0.001), and the AUC of predictive model jointly involving LD and λ-AP was significantly elevated (AUC of 0.816, p < 0.001). While the AUC of predictive model in the validation cohort was 0.786. Good to excellent repeatability was observed for all parameters (ICC > 0.75). The combination of DLCT with morphological and functional parameters may represent a potential imaging biomarker for detecting LN positivity in lymphoma.
本研究旨在评估双层 CT（DLCT）在鉴别淋巴瘤患者阳性淋巴结（LNs）中的诊断性能，回顾性纳入 78 例经组织学证实的淋巴瘤患者，这些患者均行预处理 DLCT 和 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）检查，共获得 1165 个 LNs。根据 18F-FDG PET/CT 结果作为参考标准，将病例分为 LN 阴性和 LN 阳性组。然后，将 LNs 按 7:3 的比例随机分为训练（n=809）和验证（n=356）队列。使用卡方检验、t 检验或 Mann-Whitney U 检验比较 LN 阴性和 LN 阳性组之间的患者临床特征和定量参数，包括光谱曲线斜率（λ）、动脉期（AP）和静脉期（VP）图像的碘浓度（IC）。采用十折交叉验证的多变量逻辑回归分析建立训练队列中最有效的预测模型。采用曲线下面积（AUC）评估预测模型的诊断价值，并通过 DeLong 检验确定 AUC 差异。此外，还在验证队列中验证了预测模型。采用组内相关系数（ICC）对 LNs 进行重复性分析。在训练队列中，与其他参数相比，长径（LD）作为独立因素在区分 LN 阳性与 LN 阴性方面具有最高的 AUC（p=0.006 至 p<0.001），联合 LD 和 λ-AP 的预测模型 AUC 显著升高（AUC 为 0.816，p<0.001）。而在验证队列中，预测模型的 AUC 为 0.786。所有参数的重复性均较好（ICC>0.75）。DLCT 联合形态学和功能参数可能代表一种潜在的影像学生物标志物，用于检测淋巴瘤中的 LN 阳性。
