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基于人工神经网络的骨闪烁全自动分析:骨扫描指数(BSI)在乳腺癌中的应用价值。

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.

机构信息

Department of Nuclear Medicine, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.

Department of Functional Imaging and Artificial Intelligence, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.

出版信息

Ann Nucl Med. 2019 Oct;33(10):755-765. doi: 10.1007/s12149-019-01386-1. Epub 2019 Jul 17.

Abstract

OBJECTIVE

Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It has been reported to improve diagnostic accuracy and reproducibility especially in cases of prostate cancer. The aim of this study was to evaluate the diagnostic usefulness of quantitative bone scintigraphy with ANN in patients having breast cancer.

PATIENTS AND METHODS

We retrospectively evaluated 88 patients having breast cancer who underwent both bone scintigraphy and F-fluorodeoxyglucose (FDG) positron-emission computed tomography/X-ray computed tomography (PET/CT) within an interval of 8 weeks between both examinations for comparison. The whole-body bone images were analyzed with fully automated software that was customized according to a Japanese multicenter database. The region of interest for FDG-PET was set to bone lesions in patients with bone metastasis, while the bone marrow of the ilium and the vertebra was used in patients without bone metastasis.

RESULTS

Thirty of 88 patients had bone metastasis. Extent of disease, bone scan index (BSI) which indicate severity of bone metastasis, the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and serum tumor markers in patients with bone metastasis were significantly higher than those in patients without metastasis. The Kaplan-Meier survival curve showed that the overall survival of the lower BSI group was longer than that with the higher BSI group in patients with visceral metastasis. In the multivariate Cox proportional hazard model, BSI (hazard ratio (HR): 19.15, p = 0.0077) and SUVmax (HR: 10.12, p = 0.0068) were prognostic factors in patients without visceral metastasis, while the BSI was only a prognostic factor in patients with visceral metastasis (HR: 7.88, p = 0.0084), when dividing the sample into two groups with each mean value in patients with bone metastasis.

CONCLUSION

BSI, an easily and automatically calculated parameter, was a well prognostic factor in patients with visceral metastasis as well as without visceral metastasis from breast cancer.

摘要

目的

人工神经网络 (ANN) 技术已被开发用于分析转移性骨肿瘤的骨闪烁扫描,以用于临床。据报道,该技术可提高诊断准确性和可重复性,尤其是在前列腺癌病例中。本研究旨在评估 ANN 定量骨闪烁扫描在乳腺癌患者中的诊断价值。

患者与方法

我们回顾性评估了 88 例乳腺癌患者,这些患者在两次检查之间的 8 周内同时进行了骨闪烁扫描和 F-氟代脱氧葡萄糖 (FDG) 正电子发射断层扫描/X 射线计算机断层扫描 (PET/CT)。全身骨图像由根据日本多中心数据库定制的全自动软件进行分析。对于有骨转移的患者,将 FDG-PET 的感兴趣区域设置为骨病变,而对于没有骨转移的患者,则使用髂骨和椎体的骨髓。

结果

88 例患者中有 30 例患有骨转移。患有骨转移的患者的疾病程度、骨扫描指数 (BSI)(指示骨转移严重程度)、最大标准化摄取值 (SUVmax)、代谢肿瘤体积 (MTV)、总病变糖酵解 (TLG) 和血清肿瘤标志物均显著高于无骨转移的患者。Kaplan-Meier 生存曲线显示,在有内脏转移的患者中,较低 BSI 组的总生存率长于较高 BSI 组。在多变量 Cox 比例风险模型中,BSI(风险比 (HR):19.15,p=0.0077)和 SUVmax(HR:10.12,p=0.0068)是无内脏转移患者的预后因素,而在有内脏转移的患者中,BSI 是唯一的预后因素(HR:7.88,p=0.0084),当将样本分为两组时,两组患者的 BSI 平均值。

结论

BSI 是一个易于计算且自动计算的参数,是乳腺癌有或无内脏转移患者的良好预后因素。

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