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用于精确鉴别和预测纵隔肿块的先进生物医学成像技术。

Advanced biomedical imaging for accurate discrimination and prognostication of mediastinal masses.

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany.

Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.

出版信息

Eur J Clin Invest. 2023 Dec;53(12):e14075. doi: 10.1111/eci.14075. Epub 2023 Aug 12.

Abstract

BACKGROUND

To investigate the potential of radiomic features and dual-source dual-energy CT (DECT) parameters in differentiating between benign and malignant mediastinal masses and predicting patient outcomes.

METHODS

In this retrospective study, we analysed data from 90 patients (38 females, mean age 51 ± 25 years) with confirmed mediastinal masses who underwent contrast-enhanced DECT. Attenuation, radiomic features and DECT-derived imaging parameters were evaluated by two experienced readers. We performed analysis of variance (ANOVA) and Chi-square statistic tests for data comparison. Receiver operating characteristic curve analysis and Cox regression tests were used to differentiate between mediastinal masses.

RESULTS

Of the 90 mediastinal masses, 49 (54%) were benign, including cases of thymic hyperplasia/thymic rebound (n = 10), mediastinitis (n = 16) and thymoma (n = 23). The remaining 41 (46%) lesions were classified as malignant, consisting of lymphoma (n = 28), mediastinal tumour (n = 4) and thymic carcinoma (n = 9). Significant differences were observed between benign and malignant mediastinal masses in all DECT-derived parameters (p ≤ .001) and 38 radiomic features (p ≤ .044) obtained from contrast-enhanced DECT. The combination of these methods achieved an area under the curve of .98 (95% CI, .893-1.000; p < .001) to differentiate between benign and malignant masses, with 100% sensitivity and 91% specificity. Throughout a follow-up of 1800 days, a multiparametric model incorporating radiomic features, DECT parameters and gender showed promising prognostic power in predicting all-cause mortality (c-index = .8 [95% CI, .702-.890], p < .001).

CONCLUSIONS

A multiparametric approach combining radiomic features and DECT-derived imaging biomarkers allows for accurate and noninvasive differentiation between benign and malignant masses in the anterior mediastinum.

摘要

背景

探讨放射组学特征和双源双能 CT(DECT)参数在鉴别纵隔良恶性肿块以及预测患者预后中的应用价值。

方法

本回顾性研究纳入 90 例经 DECT 增强检查证实的纵隔肿块患者(38 例女性,平均年龄 51±25 岁)。由 2 位经验丰富的阅片者评估患者的 CT 衰减值、放射组学特征和 DECT 衍生的影像学参数。采用方差分析(ANOVA)和卡方检验进行数据比较。采用受试者工作特征曲线(ROC)分析和 Cox 回归检验对纵隔肿块进行鉴别。

结果

90 个纵隔肿块中,49 个(54%)为良性,包括胸腺瘤(n=23)、胸腺增生/胸腺反弹(n=10)、纵隔炎(n=16)。其余 41 个(46%)为恶性病变,包括淋巴瘤(n=28)、纵隔肿瘤(n=4)和胸腺癌(n=9)。DECT 衍生参数(p≤.001)和增强 DECT 获得的 38 个放射组学特征(p≤.044)在良恶性纵隔肿块之间存在显著差异。这些方法的结合在区分良恶性肿块方面的曲线下面积为 0.98(95%CI,0.893-1.000;p<.001),灵敏度为 100%,特异性为 91%。在 1800 天的随访期间,纳入放射组学特征、DECT 参数和性别等多参数模型具有预测全因死亡率的潜力(c 指数=0.8[95%CI,0.702-0.890],p<.001)。

结论

放射组学特征与 DECT 衍生的成像生物标志物相结合的多参数方法可准确、无创地区分前纵隔的良恶性肿块。

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