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CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma.

Pritam Mukherjee1, Murilo Cintra1, Chao Huang1

  • 1Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford, Calif (P.M., M.C., C.H., M.Z., O.G.); Department of Radiology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil (M.C.); Department of Nutrition and Food Hygiene, Chronic Disease Research Institute, School of Public Health, School of Medicine, Zhejiang University, Zhejiang, China (C.H., S.Z.); Division of Oncology, Department of Medicine (A.D.C.), Department of Radiology (N.F.), and Department of Biomedical Data Science (O.G.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.

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|June 19, 2020
PubMed
Summary

CT radiomics can noninvasively predict head and neck squamous cell carcinoma (HNSCC) features. This machine learning model shows potential for guiding treatment decisions based on imaging alone.

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Head and neck squamous cell carcinoma (HNSCC) diagnosis relies on invasive histopathologic assessment.
  • Noninvasive methods for predicting tumor characteristics are needed to improve patient management.

Purpose of the Study:

  • To evaluate the performance of CT-based radiomic features for predicting key histopathologic features in HNSCC.
  • To assess the noninvasive prediction of tumor grade, spread, invasion, and HPV status.

Main Methods:

  • A retrospective analysis of CT images and clinical data from two HNSCC cohorts (n=113 and n=71).
  • A machine learning model was developed using 2131 radiomic features, employing principal component analysis and regularized regression.
  • The model was trained to predict histopathologic characteristics.

Main Results:

  • The radiomic model demonstrated moderate predictive performance across various HNSCC features.
  • Areas under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.77 in the training cohort and 0.66 to 0.80 in the test cohort.
  • Specific AUCs were reported for tumor grade, perineural invasion, lymphovascular invasion, extracapsular spread, and HPV status.

Conclusions:

  • CT-based radiomic models show promise for noninvasively predicting HNSCC characteristics.
  • These models could potentially reduce the need for invasive procedures in HNSCC assessment.
  • Further validation is warranted to integrate radiomics into clinical practice.