Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics

Translational Oncology

Kariem Sharaf¹  Alexandra Petukhova²  Benjamin L. Judson³  Philipp Baumeister⁴  Benjamin H. Kann⁵  Christoph Reichel⁶  Reza Forghani⁶  Tal Zeevi⁶  Amit Mahajan⁷  Manju L. Prasad⁸  Seyedmehdi Payabvash⁹  Barbara Burtness^1,10  Stefan P. Haider^1,11  Chi Liu^1,11 

[1] Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany;;Precision Health Laboratory (AIPHL), McGill University Health Centre &Research Institute, 1650 Cedar Avenue, Montreal, Quebec QC H3G 1A4, Canada;Center for Translational Imaging Analysis and Machine Learning, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, United States;;Department of Diagnostic Radiology and Augmented Intelligence &Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany;Department of Pathology, Yale School of Medicine, 310 Cedar Street, New Haven, CT 06520, United States;Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, United States;Division of Bioimaging Sciences, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, United States;Division of Otolaryngology, Department of Surgery, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, United States;Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT 06519, United States;

关键词: Radiomics;    Oropharyngeal squamous cell carcinoma;    PET/CT;    HPV;    Imaging biomarker;    Risk stratification;   

DOI  :  

来源: DOAJ

【 摘 要 】

Locoregional failure remains a therapeutic challenge in oropharyngeal squamous cell carcinoma (OPSCC). We aimed to devise novel objective imaging biomarkers for prediction of locoregional progression in HPV-associated OPSCC. Following manual lesion delineation, 1037 PET and 1037 CT radiomic features were extracted from each primary tumor and metastatic cervical lymph node on baseline PET/CT scans. Applying random forest machine-learning algorithms, we generated radiomic models for censoring-aware locoregional progression prognostication (evaluated by Harrell's C-index) and risk stratification (evaluated in Kaplan-Meier analysis). A total of 190 patients were included; an optimized model yielded a median (interquartile range) C-index of 0.76 (0.66-0.81; p = 0.01) in prognostication of locoregional progression, using combined PET/CT radiomic features from primary tumors. Radiomics-based risk stratification reliably identified patients at risk for locoregional progression within 2-, 3-, 4-, and 5-year follow-up intervals, with log-rank p-values of p = 0.003, p = 0.001, p = 0.02, p = 0.006 in Kaplan-Meier analysis, respectively. Our results suggest PET/CT radiomic biomarkers can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. Pending validation in large, independent cohorts, such objective biomarkers may improve patient selection for treatment de-intensification trials in this prognostically favorable OPSCC entity, and eventually facilitate personalized therapy.

【 授权许可】

Unknown