Daisuke Kawahara

Conventional radiomics models often treat tumors as spatially homogeneous entities, limiting their ability to capture intratumoral heterogeneity and its impact on prognosis and treatment resistance. Habitat imaging addresses this limitation by explicitly mode...

Tumor regression during radiotherapy reflects underlying radiosensitivity, yet adaptive radiotherapy is largely guided by geometric changes and physical dose metrics. We developed a biologically adaptive radiation therapy (BART) framework that derives tumor-s...

High beam delivery accuracy is essential for reliable IMRT delivery and efficient QA. This study aimed to develop a deep learning-based automatic planning system to improve delivery accuracy using predicted fluence maps and to evaluate its performance and gen...

Accurate delineation of the gross tumor volume of the primary lesion (GTVp) during radiotherapy is essential for ART, particularly in head-and-neck cancer where tumor regression and anatomical deformation occur during treatment. However, mid-treatment contour...

To develop an image-to-drug framework for glioblastoma (GBM) that translates multi-sequence MRI radiomics–based survival risk into actionable therapeutic hypotheses, by integrating ensemble survival modeling with multi-omics reinforcement learning.