Department of Nuclear Medicine, Graduate School of Medicine, Osaka City University, Osaka, Japan.
Curr Med Imaging. 2021;17(1):89-96. doi: 10.2174/1573405616666200528153453.
BSI calculated from bone scintigraphy using technetium-methylene diphosphonate (Tc-MDP) is used as a quantitative indicator of metastatic bone involvement in bone metastasis diagnosis, therapeutic effect assessment, and prognosis prediction. However, the BONE NAVI, which calculates BSI, only supports bone scintigraphy using Tc-MDP.
We developed a method in collaboration with the Tokyo University of Agriculture and Technology to calculate bone scan index (BSI) employing deep learning algorithms with bone scintigraphy images using technetium-hydroxymethylene diphosphonate (Tc-HMDP). We used a convolutional neural network (CNN), enabling the simultaneous processing of anterior and posterior bone scintigraphy images named CNNapis.
The purpose of this study is to investigate the usefulness of the BSI calculated by CNNapis as bone imaging and bone metabolic biomarkers in patients with bone metastases from prostate cancer.
At our hospital, 121 bone scintigraphy scans using Tc-HMDP were performed and analyzed to examine bone metastases from prostate cancer, revealing the abnormal accumulation of radioisotope (RI) at bone metastasis sites. Blood tests for serum prostate-specific antigen (PSA) and alkaline phosphatase (ALP) were performed concurrently. BSI values calculated by CNNapis were used to quantify the metastatic bone tumor involvement. Correlations between BSI and PSA and between BSI and ALP were calculated. Subjects were divided into four groups by BSI values (Group 1, 0 to <1; Group 2, 1 to <3; Group 3, 3 to <10; Group 4, >10), and the PSA and ALP values in each group were statistically compared.
Patients diagnosed with bone metastases after bone scintigraphy were also diagnosed with bone metastases using CNNapis. BSI corresponding to the range of abnormal RI accumulation was calculated. PSA and BSI (r = 0.2791) and ALP and BSI (r = 0.6814) correlated positively. Significant intergroup differences in PSA between Groups 1 and 2, Groups 1 and 4, Groups 2 and 3, and Groups 3 and 4 and in ALP between Groups 1 and 4, Groups 2 and 4, and Groups 3 and 4 were found.
BSI calculated using CNNapis correlated with ALP and PSA values and is useful as bone imaging and bone metabolic biomarkers, indicative of the activity and spread of bone metastases from prostate cancer.
锝-亚甲基二膦酸盐（Tc-MDP）骨闪烁扫描计算的骨转移比（BSI）被用作骨转移诊断、疗效评估和预后预测中转移性骨受累的定量指标。然而，计算 BSI 的 BONE NAVI 仅支持 Tc-MDP 骨闪烁扫描。
我们与东京农业科技大学合作，开发了一种使用锝-羟甲基二膦酸盐（Tc-HMDP）骨闪烁扫描图像的深度学习算法计算骨扫描指数（BSI）的方法。我们使用了一个卷积神经网络（CNN），可以同时处理前后位骨闪烁扫描图像，命名为 CNNapis。
本研究旨在探讨在前列腺癌骨转移患者中，CNNapis 计算的 BSI 作为骨成像和骨代谢生物标志物的有用性。
在我院对 121 例 Tc-HMDP 骨闪烁扫描进行了分析，以检查前列腺癌的骨转移，显示放射性同位素（RI）在骨转移部位的异常积聚。同时进行了血清前列腺特异性抗原（PSA）和碱性磷酸酶（ALP）的血液检查。使用 CNNapis 计算 BSI 值以量化转移性骨肿瘤的受累程度。计算 BSI 与 PSA 之间以及 BSI 与 ALP 之间的相关性。根据 BSI 值将受试者分为四组（第 1 组，0 至<1；第 2 组，1 至<3；第 3 组，3 至<10；第 4 组，>10），并对每组的 PSA 和 ALP 值进行统计学比较。
骨闪烁扫描后诊断为骨转移的患者也使用 CNNapis 诊断为骨转移。计算了与异常 RI 积聚范围相对应的 BSI。PSA 与 BSI（r=0.2791）和 ALP 与 BSI（r=0.6814）呈正相关。在 PSA 方面，第 1 组与第 2 组、第 1 组与第 4 组、第 2 组与第 3 组以及第 3 组与第 4 组之间，ALP 方面，第 1 组与第 4 组、第 2 组与第 4 组以及第 3 组与第 4 组之间，组间差异有统计学意义。
使用 CNNapis 计算的 BSI 与 ALP 和 PSA 值相关，可作为骨成像和骨代谢生物标志物，提示前列腺癌骨转移的活性和扩散。
