Stefan P Haider1,2, Amit Mahajan1, Tal Zeevi3, Philipp Baumeister2, Christoph Reichel2, Kariem Sharaf2, Reza Forghani4, Ahmet S Kucukkaya1, Benjamin H Kann5, Benjamin L Judson6, Manju L Prasad7, Barbara Burtness8, Seyedmehdi Payabvash9. 1. Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT, 06519, USA. 2. Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany. 3. Center for Translational Imaging Analysis and Machine Learning, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA. 4. Department of Diagnostic Radiology and Augmented Intelligence & Precision Health Laboratory, McGill University Health Centre & Research Institute, Montreal, Quebec, Canada. 5. Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. 6. Division of Otolaryngology, Department of Surgery, Yale School of Medicine, New Haven, CT, USA. 7. Department of Pathology, Yale School of Medicine, New Haven, CT, USA. 8. Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA. 9. Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT, 06519, USA. sam.payabvash@yale.edu.
Abstract
PURPOSE: To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (OPSCC). METHODS: We analyzed 435 primary tumors (326 for training, 109 for validation) and 741 metastatic cervical lymph nodes (518 for training, 223 for validation) using FDG-PET and non-contrast CT from a multi-institutional and multi-national cohort. Utilizing 1037 radiomics features per imaging modality and per lesion, we trained, optimized, and independently validated machine-learning classifiers for prediction of HPV association in primary tumors, lymph nodes, and combined "virtual" volumes of interest (VOI). PET-based models were additionally validated in an external cohort. RESULTS: Single-modality PET and CT final models yielded similar classification performance without significant difference in independent validation; however, models combining PET and CT features outperformed single-modality PET- or CT-based models, with receiver operating characteristic area under the curve (AUC) of 0.78, and 0.77 for prediction of HPV association using primary tumor lesion features, in cross-validation and independent validation, respectively. In the external PET-only validation dataset, final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes, and an AUC of 0.73 for a virtual VOI combining all lymph nodes. CONCLUSION: We found that PET-based radiomics signatures yielded similar classification performance to CT-based models, with potential added value from combining PET- and CT-based radiomics for prediction of HPV status. While our results are promising, radiomics signatures may not yet substitute tissue sampling for clinical decision-making.
PURPOSE: To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (OPSCC). METHODS: We analyzed 435 primary tumors (326 for training, 109 for validation) and 741 metastatic cervical lymph nodes (518 for training, 223 for validation) using FDG-PET and non-contrast CT from a multi-institutional and multi-national cohort. Utilizing 1037 radiomics features per imaging modality and per lesion, we trained, optimized, and independently validated machine-learning classifiers for prediction of HPV association in primary tumors, lymph nodes, and combined "virtual" volumes of interest (VOI). PET-based models were additionally validated in an external cohort. RESULTS: Single-modality PET and CT final models yielded similar classification performance without significant difference in independent validation; however, models combining PET and CT features outperformed single-modality PET- or CT-based models, with receiver operating characteristic area under the curve (AUC) of 0.78, and 0.77 for prediction of HPV association using primary tumor lesion features, in cross-validation and independent validation, respectively. In the external PET-only validation dataset, final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes, and an AUC of 0.73 for a virtual VOI combining all lymph nodes. CONCLUSION: We found that PET-based radiomics signatures yielded similar classification performance to CT-based models, with potential added value from combining PET- and CT-based radiomics for prediction of HPV status. While our results are promising, radiomics signatures may not yet substitute tissue sampling for clinical decision-making.
Authors: Stefan P Haider; Adnan I Qureshi; Abhi Jain; Hishan Tharmaseelan; Elisa R Berson; Shahram Majidi; Christopher G Filippi; Adrian Mak; David J Werring; Julian N Acosta; Ajay Malhotra; Jennifer A Kim; Lauren H Sansing; Guido J Falcone; Kevin N Sheth; Seyedmehdi Payabvash Journal: Int J Stroke Date: 2021-10-13 Impact factor: 6.948
Authors: Emily W Avery; Jonas Behland; Adrian Mak; Stefan P Haider; Tal Zeevi; Pina C Sanelli; Christopher G Filippi; Ajay Malhotra; Charles C Matouk; Christoph J Griessenauer; Ramin Zand; Philipp Hendrix; Vida Abedi; Guido J Falcone; Nils Petersen; Lauren H Sansing; Kevin N Sheth; Seyedmehdi Payabvash Journal: Neuroimage Clin Date: 2022-05-07 Impact factor: 4.891
Authors: Clara F Weber; Evelyn M R Lake; Stefan P Haider; Ali Mozayan; Pratik Mukherjee; Dustin Scheinost; Nigel S Bamford; Laura Ment; Todd Constable; Seyedmehdi Payabvash Journal: Front Neurosci Date: 2022-09-07 Impact factor: 5.152
Authors: Stefan P Haider; Adnan I Qureshi; Abhi Jain; Hishan Tharmaseelan; Elisa R Berson; Tal Zeevi; Shahram Majidi; Christopher G Filippi; Simon Iseke; Moritz Gross; Julian N Acosta; Ajay Malhotra; Jennifer A Kim; Lauren H Sansing; Guido J Falcone; Kevin N Sheth; Seyedmehdi Payabvash Journal: Eur J Neurol Date: 2021-07-18 Impact factor: 6.288