Peer-reviewedCC BYFrontiers in oncology16 Oct 2026

AI-assisted 3D thoracic body composition analysis identifies candidate imaging markers associated with overall survival in older patients with lung cancer receiving radiotherapy

Saerom Sung¹
Hyun Joon Choi²
Sangwoon Jeong²
Wonyoung Cho³
Yoon Jo Kim³
Hyejung Cha²
Chul Hee Min¹
Mi Sun Kim²

¹Department of Radiation Convergence Engineering, Yonsei University , ,
²Department of Radiation Oncology, Yonsei University Wonju College of Medicine , ,
³Oncosoft Inc. , ,

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Cite

BibTeX

@article{sung2026aiassisted,
  title = {{AI-assisted 3D thoracic body composition analysis identifies candidate imaging markers associated with overall survival in older patients with lung cancer receiving radiotherapy}},
  author = {Saerom Sung and Hyun Joon Choi and Sangwoon Jeong and Wonyoung Cho and Yoon Jo Kim and Hyejung Cha and Chul Hee Min and Mi Sun Kim},
  journal = {Frontiers in oncology},
  year = {2026},
  month = oct,
  url = {https://europepmc.org/article/PMC/PMC13658111},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - AI-assisted 3D thoracic body composition analysis identifies candidate imaging markers associated with overall survival in older patients with lung cancer receiving radiotherapy
AU  - Saerom Sung
AU  - Hyun Joon Choi
AU  - Sangwoon Jeong
AU  - Wonyoung Cho
AU  - Yoon Jo Kim
AU  - Hyejung Cha
AU  - Chul Hee Min
AU  - Mi Sun Kim
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in oncology
UR  - https://europepmc.org/article/PMC/PMC13658111
SN  - 2234-943X
ER  -

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