Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.

Abstract

Authors

• Yong Dae Lee
  Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea.
• Hyug-Gi Kim
  Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheunggu, Yongin-si, Gyeonggi-do 446-701, Korea.
• Miri Seo
  Department of Medicine, Kyung Hee University College of Medicine, 23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea.
• Sung Kyoung Moon
  Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea.
• Seong Jin Park
  Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, #23 Kyungheedae-ro, Dongdaemun-gu, 02447, Seoul, Republic of Korea.
• Myung-Won You
  From the Department of Radiology and Research Institute of Radiology (H.J.P., S.Y.K., S.H.P., J.H.B., H.J.K.) and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (K.S., S.G.K., N.K.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; and Department of Radiology, Kyung Hee University Hospital, Seoul, Republic of Korea (M.W.Y.).

Keywords

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