Otolaryngology Commons^™

Outcomes After Definitive Surgery For Mandibular Osteoradionecrosis, Kevin J Contrera, Steven B Chinn, Randal S Weber, Dianna Roberts, Jeffery N Myers, Stephen Y Lai, Carol M Lewis, Amy C Hessel, Ann M Gillenwater, Collin F Mulcahy, Peirong Yu, Matthew M Hanasono, Clifton D Fuller, Mark S Chambers, Mark E Zafereo

Faculty, Staff and Student Publications

Objectives: To analyze charges, complications, survival, and functional outcomes for definitive surgery of mandibular osteoradionecrosis (ORN).

Materials and methods: Retrospective analysis of 76 patients who underwent segmental mandibulectomy with reconstruction from 2000 to 2009.

Results: Complications occurred in 49 (65%) patients and were associated with preoperative drainage (odds ratio [OR] 4.40, 95% confidence interval [CI] 1.01-19.27). The adjusted median charge was $343 000, and higher charges were associated with double flap reconstruction (OR 8.15, 95% CI 2.19-30.29) and smoking (OR 5.91, 95% CI 1.69-20.72). Improved swallow was associated with age <67 years (OR 3.76, 95% CI 1.16-12.17) and preoperative swallow (OR 3.42, 95% CI 1.23-9.51). Five-year ORN-recurrence-free survival was 93% while overall survival was 63% and associated with pulmonary disease (HR [hazard ratio] 3.57, 95% CI 1.43-8.94).

Conclusions: Although recurrence of ORN is rare, surgical complications are …

Evaluation Of Programmed Death Ligand 1 Expression In Cytology To Determine Eligibility For Immune Checkpoint Inhibitor Therapy In Patients With Head And Neck Squamous Cell Carcinoma, Zhonghua Liu, Michelle Williams, John Stewart, Bonnie S Glisson, Clifton Fuller, Sinchita Roy-Chowdhuri

Faculty, Staff and Student Publications

Background: Immune checkpoint inhibitors targeting the programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway have recently emerged as a frontline treatment for head and neck squamous cell carcinoma (HNSCC). The evaluation of PD-L1 expression by immunohistochemistry in histologic samples is used to determine the eligibility of patients with HNSCC for immunotherapy. Patients with newly diagnosed HNSCC are frequently diagnosed by fine-needle aspiration (FNA) of lymph nodes with metastatic disease. However, the evaluation of PD-L1 expression with the proposed combined positive score (CPS) has not been well established in cytology specimens.

Methods: This study retrospectively identified 21 …

Progression Free Survival Prediction For Head And Neck Cancer Using Deep Learning Based On Clinical And Pet/Ct Imaging Data, Mohamed A Naser, Kareem A Wahid, Abdallah S R Mohamed, Moamen Abobakr Abdelaal, Renjie He, Cem Dede, Lisanne V Van Dijk, Clifton D Fuller

Faculty, Staff and Student Publications

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT …

Combining Tumor Segmentation Masks With Pet/Ct Images And Clinical Data In A Deep Learning Framework For Improved Prognostic Prediction In Head And Neck Squamous Cell Carcinoma, Kareem A Wahid, Renjie He, Cem Dede, Abdallah S R Mohamed, Moamen Abobakr Abdelaal, Lisanne V Van Dijk, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC …

Establishment And Validation Of Pre-Therapy Cervical Vertebrae Muscle Quantification As A Prognostic Marker Of Sarcopenia In Patients With Head And Neck Cancer, Brennan Olson, Jared Edwards, Catherine Degnin, Nicole Santucci, Michelle Buncke, Jeffrey Hu, Yiyi Chen, Clifton D Fuller, Mathew Geltzeiler, Aaron J Grossberg, Daniel Clayburgh

Faculty, Staff and Student Publications

Background: Sarcopenia is prognostic for survival in patients with head and neck cancer (HNC). However, identification of this high-risk feature remains challenging without computed tomography (CT) imaging of the abdomen or thorax. Herein, we establish sarcopenia thresholds at the C3 level and determine if C3 sarcopenia is associated with survival in patients with HNC.

Methods: This retrospective cohort study was conducted in consecutive patients with a squamous cell carcinoma of the head and neck with cross-sectional abdominal or neck imaging within 60 days prior to treatment. Measurement of cross-sectional muscle area at L3 and C3 levels was performed from CT …

Deep-Learning-Based Generation Of Synthetic 6-Minute Mri From 2-Minute Mri For Use In Head And Neck Cancer Radiotherapy, Kareem A Wahid, Jiaofeng Xu, Dina El-Habashy, Yomna Khamis, Moamen Abobakr, Brigid Mcdonald, Nicolette O' Connell, Daniel Thill, Sara Ahmed, Christina Setareh Sharafi, Kathryn Preston, Travis C Salzillo, Abdallah S R Mohamed, Renjie He, Nathan Cho, John Christodouleas, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background: Quick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.

Methods: We used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and …

Proton Image-Guided Radiation Assignment For Therapeutic Escalation Via Selection Of Locally Advanced Head And Neck Cancer Patients [Pirates]: A Phase I Safety And Feasibility Trial Of Mri-Guided Adaptive Particle Radiotherapy, Lisanne V Van Dijk, Steven J Frank, Ying Yuan, Brandon Gunn, Amy C Moreno, Abdallah S R Mohamed, Kathryn E Preston, Yun Qing, Michael T Spiotto, William H Morrison, Anna Lee, Jack Phan, Adam S Garden, David I Rosenthal, Johannes A Langendijk, Clifton D Fuller

Faculty, Staff and Student Publications

Introduction: Radiation dose-escalation for head and neck cancer (HNC) patients aiming to improve cure rates is challenging due to the increased risk of unacceptable treatment-induced toxicities. With "Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients" (PIRATES), we present a novel treatment approach that is designed to facilitate dose-escalation while minimizing the risk of dose-limiting toxicities for locally advanced HPV-negative HNC patients. The aim of this Phase I trial is to assess the safety & feasibility of PIRATES approach.

Methods: The PIRATES protocol employs a multi-faceted dose-escalation approach to minimize the risk …