- 著者
- Kiyoyuki Chinzei Akinobu Shimizu Kensaku Mori Kanako Harada Hideaki Takeda Makoto Hashizume Mayumi Ishizuka Nobumasa Kato Ryuzo Kawamori Shunei Kyo Kyosuke Nagata Takashi Yamane Ichiro Sakuma Kazuhiko Ohe Mamoru Mitsuishi
- 出版者
- Japanese Society for Medical and Biological Engineering
- 雑誌
- Advanced Biomedical Engineering (ISSN:21875219)
- 巻号頁・発行日
- vol.7, pp.118-123, 2018 (Released:2018-05-24)
- 参考文献数
- 3
- 被引用文献数
- 30
AI-based medical and healthcare devices and systems have unique characteristics including 1) plasticity causing changes in system performance through learning, and need of creating new concepts about the timing of learning and assignment of responsibilities for risk management; 2) unpredictability of system behavior in response to unknown inputs due to the black box characteristics precluding deductive output prediction; and 3) need of assuring the characteristics of datasets to be used for learning and evaluation. The Subcommittee on Artificial Intelligence and its Applications in Medical Field of the Science Board, the Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan, examined “new elements specific to AI” not included in conventional technologies, thereby clarifying the characteristics and risks of AI-based technologies. This paper summarizes the characteristics and clinical positioning of AI medical systems and their applications from the viewpoint of regulatory science, and presents the issues related to the characteristics and reliability of data sets in machine learning.
- 著者
- Tsukasa Saida Ayumi Shikama Kensaku Mori Toshitaka Ishiguro Takeo Minaguchi Toyomi Satoh Takahito Nakajima
- 出版者
- Japanese Society for Magnetic Resonance in Medicine
- 雑誌
- Magnetic Resonance in Medical Sciences (ISSN:13473182)
- 巻号頁・発行日
- pp.mp.2022-0061, (Released:2022-11-12)
- 参考文献数
- 28
- 被引用文献数
- 1
Purpose: To compare MRI findings of high-grade serous carcinoma (HGSC) with and without breast cancer (BRCA) gene variants to explore the feasibility of MRI as a genetic predictor.Methods: We retrospectively reviewed MRI data from 16 patients with BRCA variant-positive (11 patients of BRCA1 and 5 patients of BRCA2 variant-positive) and 32 patients with BRCA variant-negative HGSCs and evaluated tumor size, appearance, nature of solid components, apparent diffusion coefficient (ADC) value, time-intensity curve, several dynamic contrast-enhanced curve descriptors, and nature of peritoneal metastasis. Age, primary site, tumor stage, bilaterality, presence of lymph node metastasis, presence of peritoneal metastasis, and tumor markers were also compared between the groups with the Mann-Whitney U and chi-square tests.Results: The mean tumor size of BRCA variant-positive HGSCs was 9.6 cm, and that of variant-negative HGSCs was 6.8 cm, with no significant difference (P = 0.241). No significant difference was found between BRCA variant-positive and negative HGSCs in other evaluated factors, except for age (mean age, 53 years old; range, 32–78 years old for BRCA variant-positive and mean age, 61 years old; range, 44–80 years old for BRCA variant-negative, P = 0.033). Comparing BRCA1 variant-positive and BRCA2 variant-positive HGSCs, BRCA1 variant-positive HGSCs were larger (P = 0.040), had greater Max enhancement (P = 0.013), Area under the curve (P = 0.013), and CA125 (P = 0.038), and had a higher frequency of lymph node metastasis (P = 0.049), with significance.Conclusion: There was no significant difference in the MRI findings between patients with HGSCs with and without BRCA variants. Although studied in small numbers, BRCA1 variant-positive HGSCs were larger and more enhanced than BRCA2 variant-positive HGSCs with higher CA125 and more frequent lymph node metastases, and may represent more aggressive features.