Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy.

Journal: Medical physics

PMID: 39225585

Abstract

BACKGROUND: Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs).

Authors

Bining Yang

National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Yuxiang Liu

National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; School of Physics and Technology, Wuhan University, Wuhan, China.
Ran Wei
Kuo Men

State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
Jianrong Dai

National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Keywords

Cone-Beam Computed Tomography Deep Learning Humans Image Processing, Computer-Assisted Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Time Factors

External Resources

View on PubMed Access via DOI PubMed (39225585)

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy.

Abstract

Authors

Keywords

External Resources

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