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Kawamura M, Kamomae T, Yanagawa M, Kamagata K, Fujita S, Ueda D, Matsui Y, Fushimi Y, Fujioka T, Nozaki T, Yamada A, Hirata K, Ito R, Fujima N, Tatsugami F, Nakaura T, Tsuboyama T, Naganawa S. Revolutionizing radiation therapy: the role of AI in clinical practice. J Radiat Res 2024; 65:1-9. [PMID: 37996085 PMCID: PMC10803173 DOI: 10.1093/jrr/rrad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/25/2023] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.
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Affiliation(s)
- Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Takeshi Kamomae
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kitaku, Okayama, 700-8558, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Nemoto H, Saito M, Suzuki T, Suzuki H, Sano N, Mochizuki Z, Mochizuki K, Ueda K, Komiyama T, Marino K, Aoki S, Oguri M, Takahashi H, Onishi H. Evaluation of computed tomography metal artifact and CyberKnife fiducial recognition for novel size fiducial markers. J Appl Clin Med Phys 2023; 24:e14142. [PMID: 37672211 PMCID: PMC10691645 DOI: 10.1002/acm2.14142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 06/28/2023] [Accepted: 08/19/2023] [Indexed: 09/07/2023] Open
Abstract
PURPOSE This study aimed to compare fiducial markers used in CyberKnife treatment in terms of metal artifact intensity observed in CT images and fiducial recognition in the CyberKnife system affected by patient body thickness and type of marker. METHODS Five markers, ACCULOC 0.9 mm × 3 mm, Ball type Gold Anchor (GA) 0.28 mm × 10 mm, 0.28 mm × 20 mm, and novel size GA 0.4 mm × 10 mm, 0.4 mm × 20 mm were evaluated. To evaluate metal artifacts of CT images, two types of CT images of water-equivalent gels with each marker were acquired using Aquilion LB CT scanner, one applied SEMAR (SEMAR-on) and the other did not apply this technique (SEMAR-off). The evaluation metric of artifact intensity (MSD ) which represents a variation of CT values were compared for each marker. Next, 5, 15, and 20 cm thickness of Tough Water (TW) was placed on the gel under the condition of overlapping the vertebral phantom in the Target Locating System, and the live image of each marker was acquired to compare fiducial recognition. RESULTS The mean MSD of SEMAR-off was 78.80, 74.50, 97.25, 83.29, and 149.64 HU for ACCULOC, GA0.28 mm × 10 mm, 20 mm, and 0.40 mm × 10 mm, 20 mm, respectively. In the same manner, that of SEMAR-on was 23.52, 20.26, 26.76, 24.89, and 33.96 HU, respectively. Fiducial recognition decreased in the order of 5, 15, and 20 cm thickness, and GA 0.4 × 20 mm showed the best recognition at thickness of 20 cm TW. CONCLUSIONS We demonstrated the potential to reduce metal artifacts in the CT image to the same level for all the markers we evaluated by applying SEMAR. Additionally, the fiducial recognition of each marker may vary depending on the thickness of the patient's body. Particularly, we showed that GA 0.40 × 20 mm may have more optimal recognition for CyberKnife treatment in cases of high bodily thickness in comparison to the other markers.
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Affiliation(s)
- Hikaru Nemoto
- Department of Advanced Biomedical ImagingUniversity of YamanashiYamanashiJapan
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | - Masahide Saito
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | | | - Hidekazu Suzuki
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | - Naoki Sano
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | | | - Koji Mochizuki
- Kasugai CyberKnife Rehabilitation HospitalYamanashiJapan
| | - Koji Ueda
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | | | - Kan Marino
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | - Shinichi Aoki
- Department of RadiologyUniversity of YamanashiYamanashiJapan
| | - Mitsuhiko Oguri
- Department of RadiologyShizuoka General HospitalShizuokaJapan
| | | | - Hiroshi Onishi
- Department of RadiologyUniversity of YamanashiYamanashiJapan
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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