1.Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review
Chiyoung JEONG ; YoungMoon GOH ; Jungwon KWAK
The Ewha Medical Journal 2024;47(4):e49-
Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology.This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.
2.Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review
Chiyoung JEONG ; YoungMoon GOH ; Jungwon KWAK
The Ewha Medical Journal 2024;47(4):e49-
Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology.This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.
3.Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review
Chiyoung JEONG ; YoungMoon GOH ; Jungwon KWAK
The Ewha Medical Journal 2024;47(4):e49-
Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology.This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.
4.Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review
Chiyoung JEONG ; YoungMoon GOH ; Jungwon KWAK
The Ewha Medical Journal 2024;47(4):e49-
Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology.This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.
5.Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review
Chiyoung JEONG ; YoungMoon GOH ; Jungwon KWAK
The Ewha Medical Journal 2024;47(4):e49-
Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology.This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy, and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.
6.Dual-Blocking of PI3K and mTOR Improves Chemotherapeutic Effects on SW620 Human Colorectal Cancer Stem Cells by Inducing Differentiation.
Min Jung KIM ; Jeong Eun KOO ; Gi Yeon HAN ; Buyun KIM ; Yoo Sun LEE ; Chiyoung AHN ; Chan Wha KIM
Journal of Korean Medical Science 2016;31(3):360-370
Cancer stem cells (CSCs) have tumor initiation, self-renewal, metastasis and chemo-resistance properties in various tumors including colorectal cancer. Targeting of CSCs may be essential to prevent relapse of tumors after chemotherapy. Phosphatidylinositol-3-kinase (PI3K) and mammalian target of rapamycin (mTOR) signals are central regulators of cell growth, proliferation, differentiation, and apoptosis. These pathways are related to colorectal tumorigenesis. This study focused on PI3K and mTOR pathways by inhibition which initiate differentiation of SW620 derived CSCs and investigated its effect on tumor progression. By using rapamycin, LY294002, and NVP-BEZ235, respectively, PI3K and mTOR signals were blocked independently or dually in colorectal CSCs. Colorectal CSCs gained their differentiation property and lost their stemness properties most significantly in dual-blocked CSCs. After treated with anti-cancer drug (paclitaxel) on the differentiated CSCs cell viability, self-renewal ability and differentiation status were analyzed. As a result dual-blocking group has most enhanced sensitivity for anti-cancer drug. Xenograft tumorigenesis assay by using immunodeficiency mice also shows that dual-inhibited group more effectively increased drug sensitivity and suppressed tumor growth compared to single-inhibited groups. Therefore it could have potent anti-cancer effects that dual-blocking of PI3K and mTOR induces differentiation and improves chemotherapeutic effects on SW620 human colorectal CSCs.
AC133 Antigen/genetics/metabolism
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Animals
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Antineoplastic Agents/pharmacology/therapeutic use
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Cell Differentiation/*drug effects
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Cell Line, Tumor
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Cell Survival/drug effects
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Chromones/pharmacology/therapeutic use
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Colorectal Neoplasms/drug therapy/metabolism/pathology
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Humans
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Imidazoles/pharmacology/therapeutic use
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Male
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Mice
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Mice, Inbred BALB C
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Mice, Nude
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Morpholines/pharmacology/therapeutic use
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Neoplastic Stem Cells/cytology/drug effects/metabolism
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Paclitaxel/pharmacology/therapeutic use
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Phosphatidylinositol 3-Kinases/*antagonists & inhibitors/metabolism
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Quinolines/pharmacology/therapeutic use
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SOXB1 Transcription Factors/genetics/metabolism
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Signal Transduction/*drug effects
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Sirolimus/pharmacology/therapeutic use
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TOR Serine-Threonine Kinases/*antagonists & inhibitors/metabolism
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Xenograft Model Antitumor Assays
7.Synthetic Cannabinoid-Induced Immunosuppression Augments Cerebellar Dysfunction in Tetanus-Toxin Treated Mice.
Jaesuk YUN ; Sun Mi GU ; Tac hyung LEE ; Yun Jeong SONG ; Seonhwa SEONG ; Young Hoon KIM ; Hye Jin CHA ; Kyoung Moon HAN ; Jisoon SHIN ; Hokyung OH ; Kikyung JUNG ; Chiyoung AHN ; Hye Kyung PARK ; Hyung Soo KIM
Biomolecules & Therapeutics 2017;25(3):266-271
Synthetic cannabinoids are one of most abused new psychoactive substances. The recreational use of abused drug has aroused serious concerns about the consequences of these drugs on infection. However, the effects of synthetic cannabinoid on resistance to tetanus toxin are not fully understood yet. In the present study, we aimed to determine if the administration of synthetic cannabinoids increase the susceptibility to tetanus toxin-induced motor behavioral deficit and functional changes in cerebellar neurons in mice. Furthermore, we measured T lymphocytes marker levels, such as CD8 and CD4 which against tetanus toxin. JWH-210 administration decreased expression levels of T cell activators including cluster of differentiation (CD) 3ε, CD3γ, CD74p31, and CD74p41. In addition, we demonstrated that JWH-210 induced motor impairment and decrement of vesicle-associated membrane proteins 2 levels in the cerebellum of mice treated with tetanus toxin. Furthermore, cerebellar glutamatergic neuronal homeostasis was hampered by JWH-210 administration, as evidenced by increased glutamate concentration levels in the cerebellum. These results suggest that JWH-210 may increase the vulnerability to tetanus toxin via the regulation of immune function.
Animals
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Cannabinoids
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Cerebellar Diseases*
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Cerebellum
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Glutamic Acid
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Homeostasis
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Immunosuppression*
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Mice*
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Neurons
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R-SNARE Proteins
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T-Lymphocytes
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Tetanus
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Tetanus Toxin