Applied Sciences (Dec 2023)

Radiomic Analysis for Human Papillomavirus Assessment in Oropharyngeal Carcinoma: Lessons and Pitfalls for the Next Future

  • Ilaria Morelli,
  • Carlotta Becherini,
  • Marco Banini,
  • Marianna Valzano,
  • Niccolò Bertini,
  • Mauro Loi,
  • Giulio Francolini,
  • Icro Meattini,
  • Viola Salvestrini,
  • Pierluigi Bonomo,
  • Lorenzo Livi,
  • Isacco Desideri

DOI
https://doi.org/10.3390/app132312942
Journal volume & issue
Vol. 13, no. 23
p. 12942

Abstract

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Background: Oropharyngeal Squamous Cell Carcinoma (OPSCC) is rapidly increasing due to the spread of Human Papillomavirus (HPV) infection. HPV-positive disease has unique characteristics, with better response to treatment and consequent better prognosis. HPV status is routinely assessed via p16 immunohistochemistry or HPV DNA Polymerase Chain Reaction. Radiomics is a quantitative approach to medical imaging which can overcome limitations due to its subjective interpretation and correlation with clinical data. The aim of this narrative review is to evaluate the impact of radiomic features on assessing HPV status in OPSCC patients. Methods: A narrative review was performed by synthesizing literature results from PUBMED. In the search strategy, Medical Subject Headings (MeSH) terms were used. Retrospective mono- or multicentric works assessing the correlation between radiomic features and HPV status prediction in OPSCC were included. Selected papers were in English and included studies on humans. The range of publication date was July 2015–April 2023. Results: Our research returned 23 published papers; the accuracy of radiomic models was evaluated by ROC curves and AUC values. MRI- and CT-based radiomic models proved of comparable efficacy. Also, metabolic imaging showed crucial importance in the determination of HPV status, albeit with lower AUC values. Conclusions: Radiomic features from conventional imaging can play a complementary role in the assessment of HPV status in OPSCC. Both primary tumor- and nodal-related features and multisequencing-based models demonstrated higher accuracy.

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