Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

Journal: Future oncology (London, England)
PMID: 39991909

Abstract

PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgkin's lymphoma (NHL) using various machine learning (ML) models.

Authors

  • Naier Lin
    Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
  • Yiqian Shi
    Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
  • Min Ye
    State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
  • Yiyin Zhang
    Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
  • Xianhao Jia
    Department of Otology and Skull Base Surgery, Eye & ENT Hospital, Fudan University, Shanghai, China.

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