Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

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*

Volume 17, Issue 1, 2021

Published on: 28 May, 2020

Page: [89 - 96] Pages: 8

DOI: 10.2174/1573405616666200528153453

Price: $65

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 99mtechnetium-hydroxymethylene 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.

Graphical Abstract

[1]
Soloway MS, Hardeman SW, Hickey D, et al. Stratification of patients with metastatic prostate cancer based on extent of disease on initial bone scan. Cancer 1988; 61(1): 195-202.
[http://dx.doi.org/10.1002/1097-0142(19880101)61:1<195::AID-CNCR2820610133>3.0.CO;2-Y] [PMID: 3334948]
[2]
Takahashi Y, Yoshimura M, Suzuki K, et al. Assessment of bone scans in advanced prostate carcinoma using fully automated and semi-automated bone scan index methods. Ann Nucl Med 2012; 26(7): 586-93.
[http://dx.doi.org/10.1007/s12149-012-0617-0] [PMID: 22744807]
[3]
Erdi YE, Humm JL, Imbriaco M, Yeung H, Larson SM. Quantitative bone metastases analysis based on image segmentation. J Nucl Med 1997; 38(9): 1401-6.
[PMID: 9293797]
[4]
Sadik M, Hamadeh I, Nordblom P, et al. Computer-assisted interpretation of planar whole-body bone scans. J Nucl Med 2008; 49(12): 1958-65.
[http://dx.doi.org/10.2967/jnumed.108.055061] [PMID: 18997038]
[5]
Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Improved classifications of planar whole-body bone scans using a computer-assisted diagnosis system: a multicenter, multiple-reader, multiple-case study. J Nucl Med 2009; 50(3): 368-75.
[http://dx.doi.org/10.2967/jnumed.108.058883] [PMID: 19223423]
[6]
Wakabayashi H, Nakajima K, Mizokami A, et al. Bone scintigraphy as a new imaging biomarker: the relationship between bone scan index and bone metabolic markers in prostate cancer patients with bone metastases. Ann Nucl Med 2013; 27(9): 802-7.
[http://dx.doi.org/10.1007/s12149-013-0749-x] [PMID: 23828554]
[7]
Nakajima K, Nakajima Y, Horikoshi H, et al. Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project. EJNMMI Res 2013; 3(1): 83.
[http://dx.doi.org/10.1186/2191-219X-3-83] [PMID: 24369784]
[8]
Sekuboyina A, Rempfler M, Kukacka J, et al. Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior. Proceedings of the 21th International Conference on Medical Image Computing and Computer Assisted Interventions. 2018 Sep 13-17; Granada, Spain. 649-57.
[http://dx.doi.org/10.1007/978-3-030-00937-3_74]
[9]
Kalderstam J, Sadik M, Edenbrandt L, Ohlsson M. Analysis of regional bone scan index measurements for the survival of patients with prostate cancer. BMC Med Imaging 2014; 14(1): 24.
[http://dx.doi.org/10.1186/1471-2342-14-24] [PMID: 25012268]
[10]
Snyder WS, Cook ML, Nasset ES, et al. Report of the Task Group on Reference Man. Oxford: International Commission on Radiological Protection Publication No23 1992.
[11]
Yoshida A, Higashiyama S, Kawabe J. Clinical trial of a new BSI analysis software for 99mTc-HMDP. Japanese Archive of cases conference of clinical nuclear medicine. 1: 28-34.
[12]
Garnero P, Buchs N, Zekri J, Rizzoli R, Coleman RE, Delmas PD. Markers of bone turnover for the management of patients with bone metastases from prostate cancer. Br J Cancer 2000; 82(4): 858-64.
[http://dx.doi.org/10.1054/bjoc.1999.1012] [PMID: 10732759]
[13]
Koizumi M, Miyaji N, Murata T, et al. Evaluation of a revised version of computer-assisted diagnosis system, BONENAVI version 2.1.7, for bone scintigraphy in cancer patients. Ann Nucl Med 2015; 29(8): 659-65.
[http://dx.doi.org/10.1007/s12149-015-0988-0] [PMID: 26033528]
[14]
Mitsui Y, Shiina H, Yamamoto Y, et al. Prediction of survival benefit using an automated bone scan index in patients with castration-resistant prostate cancer. BJU Int 2012; 110(11 Pt B): E628-34.
[http://dx.doi.org/10.1111/j.1464-410X.2012.11355.x] [PMID: 22788759]
[15]
Nakashima J, Tachibana M, Horiguchi Y, et al. Serum interleukin 6 as a prognostic factor in patients with prostate cancer. Clin Cancer Res 2000; 6(7): 2702-6.
[PMID: 10914713]
[16]
Iwase T, Yamamoto N, Ichihara H, Togawa T, Nagashima T, Miyazaki M. The relationship between skeletal-related events and bone scan index for the treatment of bone metastasis with breast cancer patients. Medicine (Baltimore) 2014; 93(28): e269.
[http://dx.doi.org/10.1097/MD.0000000000000269] [PMID: 25526456]
[17]
Imbriaco M, Larson SM, Yeung HW, et al. A new parameter for measuring metastatic bone involvement by prostate cancer: the Bone Scan Index. Clin Cancer Res 1998; 4(7): 1765-72.
[PMID: 9676853]

Rights & Permissions Print Cite
Article Metrics
20
© 2024 Bentham Science Publishers | Privacy Policy