| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Outline of Final Research Achievements |
We studied the radiomics analysis to establish the new pattern recognition system of accurately predicting the response and prognosis of treatment from image features in medical images such as planning CT, dose distribution, and intra-treatment CBCT. The image quality improvement of medical images is an essential technology for extracting features with high accuracy. As a preprocessing for feature extraction, we constructed a method to improve the image quality of CBCT using deep learning. We investigated the effect of tumor delineation for the values of extracted features. We also examined the dependence on the dose calculation algorithm and the calculation grid size for the features extracted from the dose distribution.
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