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Annals of Nuclear Medicine

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01-10-2019 | Metastasis | Original Article

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

Authors: Anri Inaki, Kenichi Nakajima, Hiroshi Wakabayashi, Takafumi Mochizuki, Seigo Kinuya

Published in: Annals of Nuclear Medicine | Issue 10/2019

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 ¹⁸F-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.

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Title: Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer
Authors: Anri Inaki
Kenichi Nakajima
Hiroshi Wakabayashi
Takafumi Mochizuki
Seigo Kinuya
Publication date: 01-10-2019
Publisher: Springer Singapore
Keywords: Metastasis
Breast Cancer
Breast Cancer
Scintigraphy
Bone Metastasis
Published in: Annals of Nuclear Medicine / Issue 10/2019
Print ISSN: 0914-7187
Electronic ISSN: 1864-6433
DOI: https://doi.org/10.1007/s12149-019-01386-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Patients and methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Patients and methods

Results

Conclusion

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