Anri Inaki1, Kenichi Nakajima2, Hiroshi Wakabayashi3, Takafumi Mochizuki4, Seigo Kinuya5. 1. Department of Nuclear Medicine, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan. henri@staff.kanazawa-u.ac.jp. 2. Department of Functional Imaging and Artificial Intelligence, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8641, Japan. 3. Department of Nuclear Medicine, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan. 4. Kanazawa Advanced Medical Center, 13-1 Takara-machi, Kanazawa, 920-8641, Japan. 5. Department of Nuclear Medicine, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
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.
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.
Entities:
Keywords:
Artificial neural network; Bone scan; Breast cancer with bone metastasis; FDG-PET