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Accuracy of an Automated Bone Scan Index Measurement System Enhanced by Deep Learning of the Female Skeletal
Shohei Fukai1, Hiromitsu Daisaki2, Kosuke Yamashita3
1Department of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550 Japan.
Purpose:
VSBONE® BSI (VSBONE), an automated bone scan index (BSI) measurement system was updated from version 2.1 (ver.2) to 3.0 (ver.3). VSBONE ver.3 incorporates deep learning of the skeletal structures of 957 new women, and it can be applied in patients with breast cancer. However, the performance of the updated VSBONE remains unclear. This study aimed to validate the diagnostic accuracy of the VSBONE system in patients with breast cancer.
Methods:
In total, 220 Japanese patients with breast cancer who underwent bone scintigraphy with single-photon emission computed tomography/computed tomography (SPECT/CT) were retrospectively analyzed. The patients were diagnosed with active bone metastases (n = 20) and non-bone metastases (n = 200) according to the physician's radiographic image interpretation. The patients were assessed using the VSBONE ver.2 and VSBONE ver.3, and the BSI findings were compared with the interpretation results by the physicians. The occurrence of segmentation errors, the association of BSI between VSBONE ver.2 and VSBONE ver.3, and the diagnostic accuracy of the systems were evaluated.
Results:
VSBONE ver.2 and VSBONE ver.3 had segmentation errors in four and two patients. Significant positive linear correlations were confirmed in both versions of the BSI (r = 0.92). The diagnostic accuracy was 54.1% in VSBOBE ver.2, and 80.5% in VSBONE ver.3 (P < 0.001), respectively.
Conclusion:
The diagnostic accuracy of VSBONE was improved through deep learning of the female skeletal structures. The updated VSBONE ver.3 can be a reliable automated system for measuring BSI in patients with breast cancer.
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