Otorhinolaryngologic Diseases Commons^™

Computed Tomography Radiomics-Based Cross-Sectional Detection Of Mandibular Osteoradionecrosis In Head And Neck Cancer Survivors, Serageldin Kamel, Laia Humbert-Vidan, Zaphanlene Kaffey, Sarah Mirbahaeddin, Abdulrahman Abusaif, David T A Fuentes, Kareem Wahid, Cem Dede, Mohamed A Naser, Renjie He, Ahmed W Moawad, Khaled M Elsayes, Melissa M Chen, Adegbenga O Otun, Jillian Rigert, Mark S Chambers, Andrew Hope, Erin Watson, Kristy K Brock, Katherine Hutcheson, Lisanne Van Dijk, Amy C Moreno, Stephen Y Lai, Clifton D Fuller, Abdallah S R Mohamed, Md Anderson Head And Neck Cancer Symptom Working Group

Faculty, Staff and Student Publications

Purpose: This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).

Materials and methods: CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions. Correlation analysis (r > 0.95) reduced features for model training. A random Forest (RF) classifier with Recursive Feature Elimination identified discriminative features. Explainability was assessed …

Targeted Inhibition Of Aurora Kinase A Promotes Immune Checkpoint Inhibition Efficacy In Human Papillomavirus-Driven Cancers, Soma Ghosh, Madison P O'Hara, Pragya Sinha, Tuhina Mazumdar, Lacin Yapindi, Jagannadha K Sastry, Faye M Johnson

Faculty, Staff and Student Publications

Background: Human papillomavirus (HPV)-driven cancers include head and neck squamous cell carcinoma and cervical cancer and represent approximately 5% of all cancer cases worldwide. Standard-of-care chemotherapy, radiotherapy, and immune checkpoint inhibitors (ICIs) are associated with adverse effects and limited responses in patients with HPV-driven cancers. The integration of targeted therapies with ICIs may improve outcomes. In a previous study, we demonstrated that Aurora kinase A (AURKA, Aurora A) inhibitors lead to apoptosis of human HPV-positive cancer cells in vitro and in vivo. Here, we explored the potential of Aurora A inhibition to enhance response to ICIs in immune-competent …

Tongue Electrical Impedance Myography Correlates With Functional, Neurophysiologic, And Clinical Outcome Measures In Long-Term Oropharyngeal Cancer Survivors With And Without Hypoglossal Neuropathy: An Exploratory Study, Nathan J Hansen, Karin Woodman, Sheila Buoy, Shitong Mao, Carly E A Barbon, Stephen Y Lai, C David Fuller, Katherine A Hutcheson, Benjamin Sanchez

Faculty, Staff and Student Publications

Background: This pilot study analyzed correlations between tongue electrical impedance myography (EIM), standard tongue electromyography (EMG), and tongue functional measures in N = 4 long-term oropharyngeal cancer (OPC) survivors.

Methods: Patients were screened for a supportive care trial (NCT04151082). Hypoglossal nerve function was evaluated with genioglossus needle EMG, functional measures with the Iowa oral performance instrument (IOPI), and multi-frequency tissue composition with tongue EIM.

Results: Tongue EIM conductivity was higher for patients with EMG-confirmed cranial nerve XII neuropathy than those without (p = 0.005) and in patients with mild versus normal EMG reinnervation ratings (16 kHz EIM: p …

Innovations In Molecular Biomarkers And Biomaterial-Based Immunotherapies For Head & Neck Cancer, Sarah Anne Wong, Victoria A Manon, Simon Young, Chi T Viet

Faculty, Staff and Student Publications

Purpose of review: Oral squamous cell carcinoma (OSCC) survival rates have remained stagnant due to a lack of targeted therapies and diagnostic tools. Patient risk is currently determined solely through clinicopathologic features, primarily tumor staging, which lacks the necessary precision to stratify patients by risk and accurately dictate adjuvant treatment. Similarly, conventional OSCC therapies have well-established toxicities and limited efficacy.

Recent findings: Recent studies show that patient risk can now be assessed using non-invasive techniques, at earlier time points, and with greater accuracy using molecular biomarker panels. Additionally, novel immunotherapies not only utilize the host's immune response to combat disease …

Circulating Short Chain Fatty Acids And Fatigue In Patients With Head And Neck Cancer: A Longitudinal Prospective Study, Canhua Xiao, Veronika Fedirko, Henry Claussen, H Richard Johnston, Gang Peng, Sudeshna Paul, Kristal M Maner-Smith, Kristin A Higgins, Dong M Shin, Nabil F Saba, Evanthia C Wommack, Deborah W Bruner, Andrew H Miller

Faculty, Staff and Student Publications

Fatigue among patients with head and neck cancer (HNC) has been associated with higher inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory and immunoregulatory effects. Therefore, this study aimed to examine the association between SCFAs and fatigue among patients with HNC undergoing treatment with radiotherapy with or without concurrent chemotherapy. Plasma SCFAs and the Multidimensional Fatigue Inventory-20 were collected prior to and one month after the completion of treatment in 59 HNC patients. The genome-wide gene expression profile was obtained from blood leukocytes prior to treatment. Lower butyrate concentrations were significantly associated with higher fatigue (p = …

Outcomes Of Treatment With Neoadjuvant Cemiplimab For Patients With Advanced, Resectable Cutaneous Squamous Cell Carcinoma Of The Head And Neck: Secondary Analysis Of A Phase 2 Clinical Trial, Renata Ferrarotto, Priyadharsini Nagarajan, Jacob M Maronge, Jason M Johnson, David I Rosenthal, Jeffrey N Myers, Neil D Gross

Faculty, Staff and Student Publications

This secondary analysis of a phase 2 clinical trial examines long-term survival for resectable cutaneous squamous cell carcinoma of the head and neck according to pathologic response.

Multi-Organ Spatial Stratification Of 3-D Dose Distributions Improves Risk Prediction Of Long-Term Self-Reported Severe Symptoms In Oropharyngeal Cancer Patients Receiving Radiotherapy: Development Of A Pre-Treatment Decision Support Tool, Andrew Wentzel, Abdallah S R Mohamed, Mohamed A Naser, Lisanne V Van Dijk, Katherine Hutcheson, Amy M Moreno, Clifton D Fuller, Guadalupe Canahuate, G Elisabeta Marai

Faculty, Staff and Student Publications

Purpose: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.

Material and methods: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient's dose histograms for relevant organs. The clusters with the …

Muscle And Adipose Tissue Segmentations At The Third Cervical Vertebral Level In Patients With Head And Neck Cancer, Kareem A Wahid, Brennan Olson, Rishab Jain, Aaron J Grossberg, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S R Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, a set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was …

Advances In Head And Neck Cancer Pain, Y Ye, D D Jensen, C T Viet, H L Pan, W M Campana, M Amit, M D Boada

Faculty, Staff and Student Publications

Head and neck cancer (HNC) affects over 890,000 people annually worldwide and has a mortality rate of 50%. Aside from poor survival, HNC pain impairs eating, drinking, and talking in patients, severely reducing quality of life. Different pain phenotype in patients (allodynia, hyperalgesia, and spontaneous pain) results from a combination of anatomical, histopathological, and molecular differences between cancers. Poor pathologic features (e.g., perineural invasion, lymph node metastasis) are associated with increased pain. The use of syngeneic/immunocompetent animal models, as well as a new mouse model of perineural invasion, provides novel insights into the pathobiology of HNC pain. Glial and immune …

Knowledge-Based Planning For The Radiation Therapy Treatment Plan Quality Assurance For Patients With Head And Neck Cancer, Wenhua Cao, Mary Gronberg, Adenike Olanrewaju, Thomas Whitaker, Karen Hoffman, Carlos Cardenas, Adam Garden, Heath Skinner, Beth Beadle, Laurence Court

Faculty, Staff and Student Publications

This study aimed to investigate the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose-volume histogram (DVH) prediction models using a commercial knowledge-based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally …

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 …

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 …