Muscle CT Radiomics is Feasible in the Identification of Gout.

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ye Zeng, Chunlin Xiang, Gang Wu
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引用次数: 0

Abstract

Objective: The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout.

Materials and methods: A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance.

Results: Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively.

Conclusion: From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.

.

肌肉 CT 放射线组学可用于痛风的鉴定。
研究目的本研究旨在探讨肌肉 CT 放射组学在鉴别痛风方面的可行性:采用CT放射组学方法对30例痛风患者和20例非痛风患者的踝关节CT检查结果进行分析。使用三维切片机软件提取比目鱼肌的 CT 放射组学特征,然后对痛风病例和非痛风病例进行比较。然后用机器学习方法处理两组之间存在显著差异的放射组学特征。结果显示,有五项CT放射组学特征在两组间存在显著差异:结果:痛风病例与非痛风病例之间有五个 CT 放射组学特征存在明显差异(P < 0.05)。在逻辑回归中,AUC、灵敏度、特异性和准确性分别为 0.738、77%(46/60)、70%(28/40)和 74%(74/100)。在随机森林、Xgboost 和支持向量机分析中,准确率分别为 0.901、0.833 和 0.875:通过这项研究,可以得出结论:肌肉 CT 放射组学在识别痛风方面是可行的。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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