Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.

Journal: Academic radiology
PMID: 37985290

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients with head and neck squamous cell carcinoma (HNSCC).

Authors

  • Zheng Li
    Department of Integrated Pulmonology, Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China.
  • Ru Wang
    Dell Technologies, Round Rock, TX, USA.
  • Lingwa Wang
    Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.). Electronic address: 18834183784@163.com.
  • Chen Tan
    Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.). Electronic address: 18510247762@163.com.
  • Jiaqi Xu
  • Jugao Fang
    Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.). Electronic address: fangjugao@163.com.
  • Junfang Xian
    Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No. 1 Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China; Clinical Center for Eye Tumors, Capital Medical University, Beijing, 100730, China. Electronic address: cjr.xianjunfang@vip.163.com.

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