| Project/Area Number |
22K15818
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Gunma University |
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Principal Investigator |
Varnava Maria 群馬大学, 重粒子線医学推進機構, 助教 (40913108)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2024: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2023: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | tumor tracking / pancreatic cancer / deep learning / surface image guided / carbon ion radiotherapy |
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| Outline of Research at the Start |
Current tracking techniques for pancreatic cancer in carbon ion radiotherapy are invasive and require additional irradiation, or have poor robustness. This research will focus on the development of an accurate real-time markerless tracking system, which will be based on surface image guided radiation therapy and deep learning. The tracking system will detect the 3D surface of the patient using a camera and predict the target location at any time during treatment.
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| Outline of Annual Research Achievements |
Pancreatic cancer is one of the most lethal cancers. One in three deaths in pancreatic cancer patients is related to tumor progression. Therefore, treatment methods that improve local tumor control may lead to better overall survival. The purpose for this year was to focus on completing a prediction model for the location of the target based on the surface of the patient, which is the basis for the research. The following steps were taken:
・Use combinations of recurrent and convolutional neural networks to create prediction models for the location of the tumor.
・Data collection/preprocessing/mining to match the nature of the network tested each time accordingly when necessary.
・Check network performance.
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| Current Status of Research Progress |
Current Status of Research Progress
4: Progress in research has been delayed.
Reason
The progress was already slightly delayed from last year.
Also delayed in obtaining satisfactory performance from the model. Different approaches were explored that required the collection and preparation of more data, which took time.
The model is still under development, because of long calculation times and difficulties in solving technical difficulties during implementation.
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| Strategy for Future Research Activity |
Complete the prediction model and continue as initially planned.
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