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Study of the Usefulness of Bone Scan Index Calculated From 99m-Technetium- Hydroxymethylene Diphosphonate (99mtc-HMDP) Bone Scintigraphy for Bone Metastases from Prostate Cancer Using Deep Learning Algorithms

Author(s):

Shigeaki Higashiyama, Atsushi Yoshida and Joji Kawabe*   Pages 1 - 8 ( 8 )

Abstract:


Background: BSI calculated from bone scintigraphy using 99mtechnetium-methylene diphosphonate (99mTc-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 99mTc-MDP.

Aims: 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 99mtechnetiumhydroxymethylene diphosphonate (99mTc-HMDP). We used a convolutional neural network (CNN) enabling the simultaneous processing of anterior and posterior bone scintigraphy images named CNNapis.

Objectives: 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.

Methods: At our hospital, 121 bone scintigraphy scans using 99mTc-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.

Results: 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.

Conclusion: 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.

Keywords:

BSI, 99mTechnetium-hydroxymethylene diphosphonate, PSA, prostate cancer, bone metastases, convolutional neural network.

Affiliation:

Department of Nuclear Medicine, Graduate School of Medicine, Osaka City University, Osaka, Department of Nuclear Medicine, Graduate School of Medicine, Osaka City University, Osaka, Department of Nuclear Medicine, Graduate School of Medicine, Osaka City University, Osaka



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