Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 39956910

Abstract

BACKGROUND: Immunotherapy has revolutionized the treatment landscape for head and neck squamous cell carcinoma (HNSCC) and PD-L1 combined positivity score (CPS) scoring is recommended as a biomarker for immunotherapy. Therefore, this study aimed to develop an MRI-based deep learning score (DLS) to non-invasively assess PD-L1 expression status in HNSCC patients and evaluate its potential effeciency in predicting prognostic stratification following treatment with immune checkpoint inhibitors (ICI).

Authors

  • Cong Ding
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • Yue Kang
    Linkdoc AI Research (LAIR), Building A, Sinosteel International Plaza, No.8 Haidian Street, Haidian District, Beijing, China.
  • Fan Bai
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Genji Bai
    Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China.
  • 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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