评估治疗后头颈部淋巴水肿 CT-LEFAT 标准的观察者可靠性和诊断准确性:前瞻性盲法比较分析肿瘤学家的互评表现

MD Anderson Head and Neck Cancer Symptom Working Group, Natalie A. West, Serageldin Kamel, Zaphanlene Kaffey, Cem Dede, Samuel L. Mulder, Dina M. El-Habashy, Roger Neuberger, Mohamed A Naser, Steven J. Frank, Shitong Mao, Holly McMillan, Brad Smith, David Rosenthal, Stephen Y. Lai, Katherine A. Hutcheson, Amy C Moreno, Clifton David Fuller
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引用次数: 0

摘要

背景放疗相关淋巴水肿和纤维化(LEF)是头颈癌(HNC)患者放疗(RT)后的一种严重毒性反应。最近,CT淋巴水肿和纤维化评估工具(CT-LEFAT)被开发出来,通过在CT上可视化脂肪滞留来规范淋巴水肿和纤维化的诊断。本研究旨在评估 CT-LEFAT 标准的观察者间可靠性和诊断准确性。材料与方法本研究回顾性评估了 26 例接受 RT 治疗的 HNC 患者,这些患者至少接受了两次对比增强 CT 扫描。由五名医生评分员进行定性审查,根据 CT-LEFAT 标准评估 CT 上观察到的脂肪滞留。弗莱斯卡帕(Fleiss' kappa)分析用于评估评分者之间和评分者内部的可靠性,接收者工作特征曲线(ROC)下面积(AUC)分析用于评估诊断准确性。结果 六个 CT-LEFAT 区域的评分者之间的可靠性显示,所有评分者之间的一致性一般(0.04 ≤ kappa ≤ 0.36)。观察者内部的一致性一般为一般到中等(总体 kappa=0.44)。ROC AUC 分析因所使用的聚合方法而异(0.60 ≤ 平均 AUC ≤ 0.70)。这表明,在实现 LEF 诊断的临床就绪诊断性能之前,可能还需要更多材料,如进一步培训、改进成像方法或其他流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Observer Reliability and Diagnostic Accuracy of CT-LEFAT Criteria for Post-Treatment Head and Neck Lymphedema: A Prospective Blinded Comparative Analysis of Oncologist Human Inter-Rater Performance
Background Radiation-associated lymphedema and fibrosis (LEF) is a significant toxicity following radiation therapy (RT) for head and neck cancer (HNC) patients. Recently, the CT Lymphedema and Fibrosis Assessment Tool (CT-LEFAT) was developed to standardize LEF diagnosis through fat stranding visualized on CT. This study aims to evaluate the inter-observer reliability and diagnostic accuracy of the CT-LEFAT criteria. Materials and Methods This study retrospectively evaluated 26 HNC patients treated with RT that received a minimum of two contrast-enhanced CT scans. Qualitative review was conducted by five physician raters to assess the fat stranding observed on CT according to the CT-LEFAT criteria. Fleiss' kappa analysis was used to assess the inter- and intra-rater reliability, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) analysis was used to evaluate diagnostic accuracy. Results The inter-rater reliability across the six CT-LEFAT regions generally indicated a slight to fair agreement across all raters (0.04 ≤ kappa ≤ 0.36). Intra-observer agreement was generally fair to moderate (overall kappa=0.44). The ROC AUC analysis varied based on aggregation method used (0.60 ≤ average AUC ≤ 0.70). Conclusion This specific use-case evaluating CT-LEFAT criteria displays limited performance. This suggests that additional materials, such as further training, refinement of imaging methods, or other processes may be required before achieving clinically-ready diagnostic performance of LEF diagnosis.
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