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

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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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Acknowledgements

The authors thank Dr. Masafumi Inokuchi for his precise description of patients’ clinical history

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Authors and Affiliations

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

    Anri Inaki & Hiroshi Wakabayashi

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

    Kenichi Nakajima

  3. Kanazawa Advanced Medical Center, 13-1 Takara-machi, Kanazawa, 920-8641, Japan

    Takafumi Mochizuki

  4. Department of Nuclear Medicine, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8641, Japan

    Seigo Kinuya

Authors
  1. Anri Inaki
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  2. Kenichi Nakajima
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  3. Hiroshi Wakabayashi
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  4. Takafumi Mochizuki
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  5. Seigo Kinuya
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Correspondence to Anri Inaki.

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Disclosure

K. Nakajima has a collaborative research work with FUJIFILM Toyama Chemical for developing BONENAVI software. Other authors have declared no conflicts of interest.

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Inaki, A., Nakajima, K., Wakabayashi, H. et al. Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer. Ann Nucl Med 33, 755–765 (2019). https://doi.org/10.1007/s12149-019-01386-1

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  • DOI: https://doi.org/10.1007/s12149-019-01386-1

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