Masakazu Tsujimoto

OBJECTIVE: This study proposes an automated classification of benign and malignant in highly integrated regions in bone single-photon emission computed tomography/computed tomography (SPECT/CT) using a three-dimensional deep convolutional neural network (3D-DCNN). METHODS: We examined 100 regions of 35 patients with bone SPECT/CT classified as benign and malignant by other examinations and follow-ups. First, SPECT and CT images were extracted at the same coordinates in a cube, with a long side two times the diameter of a high concentration in SPECT images. Next, we inputted the extracted image to DCNN and obtained the probability of benignity and malignancy. Integrating the output from DCNN of each SPECT and CT image provided the overall result. To validate the efficacy of the proposed method, the malignancy of all images was assessed using the leave-one-out cross-validation method; besides, the overall classification accuracy was evaluated. Furthermore, we compared the analysis results of SPECT/CT, SPECT alone, CT alone, and whole-body planar scintigraphy in the highly integrated region of the same site. RESULTS: The extracted volume of interest was 50 benign and malignant regions, respectively. The overall classification accuracy of SPECT alone and CT alone was 73% and 68%, respectively, while that of the whole-body planar analysis at the same site was 74%. When SPECT/CT images were used, the overall classification accuracy was the highest (80%), while the classification accuracy of malignant and benign was 82 and 78%, respectively. CONCLUSIONS: This study suggests that DCNN could be used for the direct classification of benign and malignant regions without extracting the features of SPECT/CT accumulation patterns.

OBJECTIVE: Quantitative analyses of gamma-ray accumulation in single-photon emission computed tomography (SPECT), and the evaluation of antiresorptive agent-related osteonecrosis of the jaw (ARONJ) have been reported recently. However, the relationship between the quantitative parameters calculated from SPECT and the detailed morphological changes observed in computed tomography (CT) remains unclear. This study aimed to investigate patients' characteristics and morphological changes observed on CT, and their effects on the quantitative values in SPECT. METHODS: From April 2017 to March 2019, patients diagnosed with ARONJ at our hospital were enrolled. The data obtained before September 2017 were reviewed retrospectively, and other data were collected prospectively. CT scans were evaluated for internal texture, sequestrum formation, periosteal reaction, cortical perforation, bone expansion, and pathological fracture. For quantitative assessment, the ratio of the maximum standardized uptake value (SUV) to the mean SUV in the temporal bone (rSUVmax) was calculated from SPECT images. The factors affecting rSUVmax were investigated by multiple regression analysis. The statistical significance level was set at α = 0.05. RESULTS: Overall, 55 lesions of 42 patients (median age and interquartile range, 75 [67-80 years], 27 female) were evaluated. Male sex (p = 0.007) and bilateral location (p < 0.0001) were selected as variables in the multivariate analysis. Adjusted coefficient of determination R2 was 0.59 (p < 0.0001). CONCLUSION: Sex and horizontal progression of the disease may affect individually calibrated SUVs in SPECT for patients with ARONJ.