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基于 F-FDG PET/CT 的多变量临床预测模型在鉴别恶性肿瘤相关噬血细胞性淋巴组织细胞增多症中的开发和验证。

Development and Validation of F-FDG PET/CT-Based Multivariable Clinical Prediction Models for the Identification of Malignancy-Associated Hemophagocytic Lymphohistiocytosis.

机构信息

Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Sinounion Medical Technology (Beijing) Co., Ltd., Beijing, China.

出版信息

Korean J Radiol. 2022 Apr;23(4):466-478. doi: 10.3348/kjr.2021.0733. Epub 2022 Mar 8.

DOI:10.3348/kjr.2021.0733
PMID:35289150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8961016/
Abstract

OBJECTIVE

F-fluorodeoxyglucose (FDG) PET/CT is often used for detecting malignancy in patients with newly diagnosed hemophagocytic lymphohistiocytosis (HLH), with acceptable sensitivity but relatively low specificity. The aim of this study was to improve the diagnostic ability of F-FDG PET/CT in identifying malignancy in patients with HLH by combining F-FDG PET/CT and clinical parameters.

MATERIALS AND METHODS

Ninety-seven patients (age ≥ 14 years) with secondary HLH were retrospectively reviewed and divided into the derivation (n = 71) and validation (n = 26) cohorts according to admission time. In the derivation cohort, 22 patients had malignancy-associated HLH (M-HLH) and 49 patients had non-malignancy-associated HLH (NM-HLH). Data on pretreatment F-FDG PET/CT and laboratory results were collected. The variables were analyzed using the Mann-Whitney U test or Pearson's chi-square test, and a nomogram for predicting M-HLH was constructed using multivariable binary logistic regression. The predictors were also ranked using decision-tree analysis. The nomogram and decision tree were validated in the validation cohort (10 patients with M-HLH and 16 patients with NM-HLH).

RESULTS

The ratio of the maximal standardized uptake value (SUVmax) of the lymph nodes to that of the mediastinum, the ratio of the SUVmax of bone lesions or bone marrow to that of the mediastinum, and age were selected for constructing the model. The nomogram showed good performance in predicting M-HLH in the validation cohort, with an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.686-0.971). At an appropriate cutoff value, the sensitivity and specificity for identifying M-HLH were 90% (9/10) and 68.8% (11/16), respectively. The decision tree integrating the same variables showed 70% (7/10) sensitivity and 93.8% (15/16) specificity for identifying M-HLH. In comparison, visual analysis of F-FDG PET/CT images demonstrated 100% (10/10) sensitivity and 12.5% (2/16) specificity.

CONCLUSION

F-FDG PET/CT may be a practical technique for identifying M-HLH. The model constructed using F-FDG PET/CT features and age was able to detect malignancy with better accuracy than visual analysis of F-FDG PET/CT images.

摘要

目的

氟-18 脱氧葡萄糖(FDG)正电子发射断层扫描/计算机断层扫描(PET/CT)常用于诊断新诊断的噬血细胞性淋巴组织细胞增多症(HLH)患者的恶性肿瘤,具有可接受的敏感性,但特异性相对较低。本研究旨在通过结合 F-FDG PET/CT 和临床参数来提高 F-FDG PET/CT 识别 HLH 患者恶性肿瘤的诊断能力。

材料与方法

回顾性分析了 97 例(年龄≥14 岁)继发性 HLH 患者的资料,根据入院时间将其分为推导队列(n=71)和验证队列(n=26)。在推导队列中,22 例患者为恶性肿瘤相关性噬血细胞性淋巴组织细胞增多症(M-HLH),49 例为非恶性肿瘤相关性噬血细胞性淋巴组织细胞增多症(NM-HLH)。收集了预处理 F-FDG PET/CT 和实验室结果的数据。使用 Mann-Whitney U 检验或 Pearson 卡方检验分析变量,使用多变量二项逻辑回归构建预测 M-HLH 的列线图。使用决策树分析对预测因子进行排序。在验证队列中(10 例 M-HLH 和 16 例 NM-HLH)验证了列线图和决策树。

结果

选择了淋巴结最大标准化摄取值(SUVmax)与纵隔 SUVmax 的比值、骨病变或骨髓 SUVmax 与纵隔 SUVmax 的比值和年龄来构建模型。验证队列中,该列线图在预测 M-HLH 方面表现良好,受试者工作特征曲线下面积为 0.875(95%置信区间,0.686-0.971)。在适当的截断值下,识别 M-HLH 的敏感性和特异性分别为 90%(9/10)和 68.8%(11/16)。整合相同变量的决策树对识别 M-HLH 的敏感性为 70%(7/10),特异性为 93.8%(15/16)。相比之下,FDG PET/CT 图像的视觉分析显示 100%(10/10)的敏感性和 12.5%(2/16)的特异性。

结论

FDG PET/CT 可能是识别 M-HLH 的一种实用技术。使用 F-FDG PET/CT 特征和年龄构建的模型在识别恶性肿瘤方面比 FDG PET/CT 图像的视觉分析具有更高的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/0c8b91340c23/kjr-23-466-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/ab0e22696ee5/kjr-23-466-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/e171425aea9b/kjr-23-466-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/ab379b45c8e2/kjr-23-466-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/09ab73189657/kjr-23-466-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/01ea485da05e/kjr-23-466-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/0c8b91340c23/kjr-23-466-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/ab0e22696ee5/kjr-23-466-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/e171425aea9b/kjr-23-466-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/ab379b45c8e2/kjr-23-466-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/09ab73189657/kjr-23-466-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/01ea485da05e/kjr-23-466-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/492a/8961016/0c8b91340c23/kjr-23-466-g006.jpg

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