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本文引用的文献

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Predicting the Prognosis of Prostate Cancer Bone Metastasis Using the Bone Scan Index and Hot Spots Calculated Using VSBONEⓇ Bone Scan Index from Tc-99m-Hydroxymethylene Diphosphonate Bone Scintigraphy.基于 Tc-99m-羟基亚甲基二膦酸盐骨扫描的 VSBONE ⁇ 骨扫描指数预测前列腺癌骨转移的预后。
Urol Int. 2022;106(9):963-969. doi: 10.1159/000522046. Epub 2022 Mar 4.
2
SAPHO syndrome: pathogenesis, clinical presentation, imaging, comorbidities and treatment: a review.SAPHO综合征:发病机制、临床表现、影像学、合并症及治疗:综述
Postepy Dermatol Alergol. 2021 Dec;38(6):937-942. doi: 10.5114/ada.2020.97394. Epub 2020 Jul 23.
3
SAPHO syndrome and pustulotic arthro-osteitis.SAPHO 综合征与脓疱性肢端角化病性骨关节炎。
Mod Rheumatol. 2022 Jul 1;32(4):665-674. doi: 10.1093/mr/roab103.
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Clinical utility of quantitative analysis of bone scintigraphy in detecting clinically active joint and high disease activity in patients with rheumatoid arthritis.定量分析骨闪烁显像在检测类风湿关节炎患者临床活动性关节和高疾病活动中的临床实用性。
BMC Med Imaging. 2021 Nov 23;21(1):177. doi: 10.1186/s12880-021-00712-2.
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Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.利用卷积神经网络自动识别骨闪烁扫描中的可疑骨转移病灶。
BMC Med Imaging. 2021 Sep 4;21(1):131. doi: 10.1186/s12880-021-00662-9.
6
Clinical Use of Quantitative Analysis of Bone Scintigraphy to Assess the Involvement of Arthritis Diseases in Patients with Joint Symptoms.骨闪烁显像定量分析在评估关节症状患者关节炎疾病累及情况中的临床应用
Diagnostics (Basel). 2020 Nov 24;10(12):1000. doi: 10.3390/diagnostics10121000.
7
Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.基于深度神经网络的人工智能辅助诊断癌症骨转移骨闪烁扫描。
Sci Rep. 2020 Oct 12;10(1):17046. doi: 10.1038/s41598-020-74135-4.
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The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer.深度学习算法在前列腺癌患者骨闪烁成像中的应用。
Ann Nucl Med. 2020 Dec;34(12):926-931. doi: 10.1007/s12149-020-01524-0. Epub 2020 Sep 21.
9
Anterior chest wall in SAPHO syndrome: magnetic resonance imaging findings.SAPHO综合征的前胸壁:磁共振成像表现
Arthritis Res Ther. 2020 Sep 14;22(1):216. doi: 10.1186/s13075-020-02309-6.
10
Imaging features in patients with SAPHO/CRMO: a pictorial review.SAPHO/CRMO患者的影像学特征:图文综述
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自动骨扫描指数在关节炎中的应用:一种评估滑膜炎、痤疮、脓疱病、骨肥厚和骨炎(SAPHO)综合征的定量方法

Usefulness of the Automated Bone Scan Index in Arthritis: A Quantitative Approach for Evaluating Synovitis, Acne, Pustulosis, Hyperostosis, and Osteitis (SAPHO) Syndrome.

作者信息

Nomura Kenta, Nakayama Michihiro, Okizaki Atsutaka

机构信息

Department of Radiology, Asahikawa Medical University, Midorigaoka-Higashi 2-1-1-1, Asahikawa, 078-8510 Hokkaido Japan.

出版信息

Nucl Med Mol Imaging. 2025 Apr;59(2):147-153. doi: 10.1007/s13139-024-00883-0. Epub 2024 Sep 19.

DOI:10.1007/s13139-024-00883-0
PMID:40125022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11923344/
Abstract

PURPOSE

For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors. However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome.

METHODS

The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process.

RESULTS

This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes ( < 0.001). A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87 95% confidence interval: 0.81-0.92).

CONCLUSION

The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

摘要

目的

几十年来,骨闪烁显像(BS)一直被用作诊断滑膜炎、痤疮、脓疱病、骨肥厚和骨炎(SAPHO)综合征患者关节炎的工具。人工智能(AI)诊断支持系统在骨闪烁显像中很有效。具有AI诊断支持系统的骨扫描指数(BSI)已用于骨肿瘤。然而,其在关节炎中的应用尚未得到验证。本研究旨在评估使用AI诊断支持系统的BSI对SAPHO综合征患者关节炎的有用性。

方法

使用AI诊断支持系统(VSBONE BSI®)计算SAPHO综合征患者骨闪烁显像中胸锁关节和胸肋关节周围关节炎摄取的区域骨扫描指数(rBSI)。为作比较,对同一区域骨闪烁显像有退行性改变的患者进行评估。rBSI采用相同方法计算。

结果

本研究纳入43例SAPHO综合征患者和48例有退行性改变的患者。诊断支持系统测得的SAPHO综合征患者rBSI为0.19±0.19,有退行性改变的患者为0.043±0.056。SAPHO综合征患者的rBSI显著高于有退行性改变的患者(<0.001)。感兴趣区域rBSI的截断值为0.030时,区分关节炎和退行性改变的敏感性为0.98,特异性为0.63(曲线下面积:0.87;95%置信区间:0.81 - 0.92)。

结论

使用AI诊断支持系统计算的rBSI对关节炎进行客观评估可能是有用的。