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1. Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge

Author

Wahid, Kareem A., Dede, Cem, El-Habashy, Dina M., Kamel, Serageldin, Rooney, Michael K., Khamis, Yomna, Abdelaal, Moamen R. A., Ahmed, Sara, Corrigan, Kelsey L., Chang, Enoch, Dudzinski, Stephanie O., Salzillo, Travis C., McDonald, Brigid A., Mulder, Samuel L., McCullum, Lucas, Alakayleh, Qusai, Sjogreen, Carlos, He, Renjie, Mohamed, Abdallah S. R., Lai, Stephen Y., Christodouleas, John P., Schaefer, Andrew J., Naser, Mohamed A., and Fuller, Clifton D.

Subjects
Physics - Medical Physics
Abstract

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on Grand Challenge using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC., Comment: For HNTS-MRG 2024 volume of Lecture Notes in Computer Science

Published
2024

2. DASS Good: Explainable Data Mining of Spatial Cohort Data

Author

Wentzel, Andrew, Floricel, Carla, Canahuate, Guadalupe, Naser, Mohamed A., Mohamed, Abdallah S., Fuller, Clifton David, van Dijk, Lisanne, and Marai, G. Elisabeta

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience., Comment: 10 pages, 9 figures

Published
2023

5. Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data

Author

Naser, Mohamed A., Wahid, Kareem A., Mohamed, Abdallah S. R., Abdelaal, Moamen Abobakr, He, Renjie, Dede, Cem, van Dijk, Lisanne V., Fuller, Clifton D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2022
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6. 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

Author

Wahid, Kareem A., He, Renjie, Dede, Cem, Mohamed, Abdallah S. R., Abdelaal, Moamen Abobakr, van Dijk, Lisanne V., Fuller, Clifton D., Naser, Mohamed A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2022
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7. Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images

Author

Naser, Mohamed A., Wahid, Kareem A., van Dijk, Lisanne V., He, Renjie, Abdelaal, Moamen Abobakr, Dede, Cem, Mohamed, Abdallah S. R., Fuller, Clifton D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2022
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10. Identifying Symptom Clusters Through Association Rule Mining

Author

Biggs, Mikayla, Floricel, Carla, Van Dijk, Lisanne, Mohamed, Abdallah S. R., David Fuller, C., Marai, G. Elisabeta, Zhang, Xinhua, Canahuate, Guadalupe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tucker, Allan, editor, Henriques Abreu, Pedro, editor, Cardoso, Jaime, editor, Pereira Rodrigues, Pedro, editor, and Riaño, David, editor

Published
2021
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18. 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

Author

Wentzel, Andrew, primary, Mohamed, Abdallah S. R., additional, Naser, Mohamed A., additional, van Dijk, Lisanne V., additional, Hutcheson, Katherine, additional, Moreno, Amy M., additional, Fuller, Clifton D., additional, Canahuate, Guadalupe, additional, and Marai, G. Elisabeta, additional

Published
2023
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19. Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers

Author

McDonald, Brigid A., primary, Cardenas, Carlos E., additional, O'Connell, Nicolette, additional, Ahmed, Sara, additional, Naser, Mohamed A., additional, Wahid, Kareem A., additional, Xu, Jiaofeng, additional, Thill, Dan, additional, Zuhour, Raed J., additional, Mesko, Shane, additional, Augustyn, Alexander, additional, Buszek, Samantha M., additional, Grant, Stephen, additional, Chapman, Bhavana V., additional, Bagley, Alexander F., additional, He, Renjie, additional, Mohamed, Abdallah S. R., additional, Christodouleas, John, additional, Brock, Kristy K., additional, and Fuller, Clifton D., additional

Published
2023
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21. Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study

Author

Cardenas, Carlos E., Blinde, Sanne E., Mohamed, Abdallah S. R., Ng, Sweet Ping, Raaijmakers, Cornelis, Philippens, Marielle, Kotte, Alexis, Al-Mamgani, Abrahim A., Karam, Irene, Thomson, David J., Robbins, Jared, Newbold, Kate, Fuller, Clifton D., Terhaard, Chris, Bahig, Houda, Blanchard, Pierre, Dehnad, Homan, Doornaert, Patricia, Elhalawani, Hesham, Frank, Steven J., Garden, Adam, Gunn, G. Brandon, Hamming-Vrieze, Olga, Kamal, Mona, Kasperts, Nicolien, Lee, Lip Wai, McDonald, Brigid A., McPartlin, Andrew, Meheissen, Mohamed AM, Morrison, William H., Navran, Arash, Nutting, Christopher M., Pameijer, Frank, Phan, Jack, Poon, Ian, Rosenthal, David I, Smid, Ernst J., and Sykes, Andrew J.

Subjects
Observer Variation, Cancer Research, Magnetic Resonance Spectroscopy, Radiation, Radiotherapy Planning, Computer-Assisted, Magnetic Resonance Imaging, Article, Tumor Burden, Oropharyngeal Neoplasms, Oncology, Head and Neck Neoplasms, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Retrospective Studies
Abstract

Tumor and target volume manual delineation remains a challenging task in head and neck cancer radiation therapy. The purpose of this study was to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation magnetic resonance scans of patients with oropharyngeal cancer.We retrospectively collected pretreatment T1-weighted, T1-weighted with gadolinium contrast, and T2-weighted magnetic resonance imaging scans for 4 patients with oropharyngeal cancer under an institution review board-approved protocol. We provided the scans to 26 radiation oncologists from 7 international cancer centers that participated in this delineation study. We also provide the patients' clinical history and physical examination findings, along with a medical photographic image and radiologic results. We used both the Simultaneous Truth and Performance Level Estimation algorithm and pair-wise comparisons of the contours, using overlap/distance metrics. Lastly, to assess experience and CTV delineation institutional practices, we had participants complete a brief questionnaire.Large variability was measured between observers' delineations for GTVs and CTVs. The mean Dice similarity coefficient values across all physicians' delineations for GTVp, GTVn, CTVp, and CTVn were 0.77, 0.67, 0.77, and 0.69, respectively, for Simultaneous Truth and Performance Level Estimation algorithm comparison, and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap/distance metrics. The median radiation oncology clinical experience was 7 years. The median experience delineating on magnetic resonance imaging was 3.5 years. The GTV-to-CTV margin used was 10 mm for 6 of 7 participant institutions. One institution used 8 mm, and 3 participants (from 3 different institutions) used a margin of 5 mm.The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of magnetic resonance-based treatment planning for head and neck cancers. Such efforts should play a critical role in reducing delineation variation and ensure standardization of target design across clinical practices.

Published
2022
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22. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

Author

Milgrom, Sarah A., Elhalawani, Hesham, Lee, Joonsang, Wang, Qianghu, Mohamed, Abdallah S. R., Dabaja, Bouthaina S., Pinnix, Chelsea C., Gunther, Jillian R., Court, Laurence, Rao, Arvind, Fuller, Clifton D., Akhtari, Mani, Aristophanous, Michalis, Mawlawi, Osama, Chuang, Hubert H., Sulman, Erik P., Lee, Hun J., Hagemeister, Frederick B., Oki, Yasuhiro, Fanale, Michelle, and Smith, Grace L.

Published
2019
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23. Investigation of autosegmentation techniques on T2‐weighted MRI for off‐line dose reconstruction in MR‐linac workflow for head and neck cancers.

Author

McDonald, Brigid A., Cardenas, Carlos E., O'Connell, Nicolette, Ahmed, Sara, Naser, Mohamed A., Wahid, Kareem A., Xu, Jiaofeng, Thill, Dan, Zuhour, Raed J., Mesko, Shane, Augustyn, Alexander, Buszek, Samantha M., Grant, Stephen, Chapman, Bhavana V., Bagley, Alexander F., He, Renjie, Mohamed, Abdallah S. R., Christodouleas, John, Brock, Kristy K., and Fuller, Clifton D.

Subjects
HEAD & neck cancer, WORKFLOW, MAGNETIC resonance imaging, PAROTID glands, RANDOM forest algorithms
Abstract

Background: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)‐linear accelerator (MR‐linac), the low‐resolution T2‐weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. Purpose: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on‐board setup MRIs from the MR‐linac for off‐line reconstruction of delivered dose. Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1–9) atlas‐based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10–19) autosegmentation using images from a patient's 1–4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter‐observer variability using Dunn's test with control. Methods were compared pairwise using the Steel‐Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high‐performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low‐performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. Results: DL and IPP methods performed best overall, all significantly outperforming inter‐observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter‐observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7–13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on‐board T2‐weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end‐to‐end dose accumulation workflow. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Patterns of Failure for Recurrent Head and Neck Squamous Cell Carcinoma Treated with Salvage Surgery and Postoperative IMRT Reirradiation

Author

Mohamed, Abdallah S. R., primary, Martin, Geoffrey V., additional, Ng, Sweet Ping, additional, Takiar, Vinita, additional, Beadle, Beth M., additional, Zafereo, Mark, additional, Garden, Adam S., additional, Frank, Steven J., additional, Fuller, C. David, additional, Gunn, G. Brandon, additional, Morrison, William H., additional, Rosenthal, David I., additional, Reddy, Jay, additional, Moreno, Amy, additional, Lee, Anna, additional, and Phan, Jack, additional

Published
2023
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25. Cluster-Based Toxicity Estimation of Osteoradionecrosis via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

Author

Hosseinian, Seyedmohammadhossein, primary, Hemmati, Mehdi, additional, Dede, Cem, additional, Salzillo, Travis C., additional, van Dijk, Lisanne V., additional, Mohamed, Abdallah S. R., additional, Lai, Stephen Y., additional, Schaefer, Andrew J., additional, and Fuller, Clifton D., additional

Published
2023
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27. Multi-Specialty Expert Physician Identification of Extranodal Extension in Computed Tomography Scans of Oropharyngeal Cancer Patients: Prospective Blinded Human Inter-Observer Performance Evaluation

Author

Sahin, Onur, Wahid, Kareem A., Taku, Nicolette, He, Renjie, Naser, Mohamed A., Mohamed, Abdallah S. R., Mäkitie, Antti, Kann, Benjamin H., Kaski, Kimmo, Sahlsten, Jaakko, Jaskari, Joel, Amit, Moran, Chen, Melissa M., Chronowski, Gregory M., Diaz, Eduardo M., Garden, Adam S., Goepfert, Ryan P., Guenette, Jeffrey P., Gunn, G. Brandon, Hirvonen, Jussi, Hoebers, Frank, Guha-Thakurta, Nandita, Johnson, Jason, Kaya, Diana, Khanpara, Shekhar D., Nyman, Kristofer, Lai, Stephen Y., Lango, Miriam, Learned, Kim O., Lee, Anna, Lewis, Carol M., Maniakas, Anastasios, Moreno, Amy C., Myers, Jeffery N., Phan, Jack, Pytynia, Kristen B., Rosenthal, David I., Sandulache, Vlad, Schellingerhout, Dawid, Shah, Shalin J., Sikora, Andrew G., Wintermark, Max, and Fuller, Clifton D.

Subjects
Article
Abstract

BackgroundExtranodal extension (ENE) is an important adverse prognostic factor in oropharyngeal cancer (OPC) and is often employed in therapeutic decision making. Clinician-based determination of ENE from radiological imaging is a difficult task with high inter-observer variability. However, the role of clinical specialty on the determination of ENE has been unexplored.MethodsPre-therapy computed tomography (CT) images for 24 human papillomavirus-positive (HPV+) OPC patients were selected for the analysis; 6 scans were randomly chosen to be duplicated, resulting in a total of 30 scans of which 21 had pathologically-confirmed ENE. 34 expert clinician annotators, comprised of 11 radiologists, 12 surgeons, and 11 radiation oncologists separately evaluated the 30 CT scans for ENE and noted the presence or absence of specific radiographic criteria and confidence in their prediction. Discriminative performance was measured using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician. Statistical comparisons of discriminative performance were calculated using Mann Whitney U tests. Significant radiographic factors in correct discrimination of ENE status were determined through a logistic regression analysis. Interobserver agreement was measured using Fleiss’ kappa.ResultsThe median accuracy for ENE discrimination across all specialties was 0.57. There were significant differences between radiologists and surgeons for Brier score (0.33 vs. 0.26), radiation oncologists and surgeons for sensitivity (0.48 vs. 0.69), and radiation oncologists and radiologists/surgeons for specificity (0.89 vs. 0.56). There were no significant differences between specialties for accuracy or AUC. Indistinct capsular contour, nodal necrosis, and nodal matting were significant factors in regression analysis. Fleiss’ kappa was less than 0.6 for all the radiographic criteria, regardless of specialty.ConclusionsDetection of ENE in HPV+OPC patients on CT imaging remains a difficult task with high variability, regardless of clinician specialty. Although some differences do exist between the specialists, they are often minimal. Further research in automated analysis of ENE from radiographic images is likely needed.

Published
2023
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28. E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation

Author

Lin, Diana, primary, Wahid, Kareem A., additional, Nelms, Benjamin E., additional, He, Renjie, additional, Naser, Mohammed A., additional, Duke, Simon, additional, Sherer, Michael V., additional, Christodouleas, John P., additional, Mohamed, Abdallah S. R., additional, Cislo, Michael, additional, Murphy, James D., additional, Fuller, Clifton D., additional, and Gillespie, Erin F., additional

Published
2023
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29. Quality assurance assessment of intra‐acquisition diffusion‐weighted and T2‐weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy

Author

Naser, Mohamed A., primary, Wahid, Kareem A., additional, Ahmed, Sara, additional, Salama, Vivian, additional, Dede, Cem, additional, Edwards, Benjamin W., additional, Lin, Ruitao, additional, McDonald, Brigid, additional, Salzillo, Travis C., additional, He, Renjie, additional, Ding, Yao, additional, Abdelaal, Moamen Abobakr, additional, Thill, Daniel, additional, O'Connell, Nicolette, additional, Willcut, Virgil, additional, Christodouleas, John P., additional, Lai, Stephen Y., additional, Fuller, Clifton D., additional, and Mohamed, Abdallah S. R., additional

Published
2022
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30. Author Correction: Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy

Author

Grossberg, Aaron J., Mohamed, Abdallah S. R., Elhalawani, Hesham, Bennett, William C., Smith, Kirk E., Nolan, Tracy S., Williams, Bowman, Chamchod, Sasikarn, Heukelom, Jolien, Kantor, Michael E., Browne, Theodora, Hutcheson, Katherine A., Gunn, G. Brandon, Garden, Adam S., Morrison, William H., Frank, Steven J., Rosenthal, David I., Freymann, John B., and Fuller, Clifton D.

Published
2018
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31. Predicting treatment Response based on Dual assessment of magnetic resonance Imaging kinetics and Circulating Tumor cells in patients with Head and Neck cancer (PREDICT-HN): matching ‘liquid biopsy’ and quantitative tumor modeling

Author

Ng, Sweet Ping, Bahig, Houda, Wang, Jihong, Cardenas, Carlos E., Lucci, Anthony, Hall, Carolyn S., Meas, Salyna, Sarli, Vanessa N., Yuan, Ying, Urbauer, Diana L., Ding, Yao, Ikner, Shane, Dinh, Vi, Elgohari, Baher A., Johnson, Jason M., Skinner, Heath D., Gunn, G. Brandon, Garden, Adam S., Phan, Jack, Rosenthal, David I., Morrison, William H., Frank, Steven J., Hutcheson, Katherine A., Mohamed, Abdallah S. R., Lai, Stephen Y., Ferrarotto, Renata, MacManus, Michael P., and Fuller, Clifton D.

Published
2018
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32. Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy

Author

Wahid, Kareem A., primary, Xu, Jiaofeng, additional, El-Habashy, Dina, additional, Khamis, Yomna, additional, Abobakr, Moamen, additional, McDonald, Brigid, additional, O’ Connell, Nicolette, additional, Thill, Daniel, additional, Ahmed, Sara, additional, Sharafi, Christina Setareh, additional, Preston, Kathryn, additional, Salzillo, Travis C., additional, Mohamed, Abdallah S. R., additional, He, Renjie, additional, Cho, Nathan, additional, Christodouleas, John, additional, Fuller, Clifton D., additional, and Naser, Mohamed A., additional

Published
2022
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33. Large-scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites: Interobserver expert/non-expert and multi-observer composite tumor and normal tissue delineation annotation from a prospective educational challenge

Author

Wahid, Kareem A., primary, Lin, Diana, additional, Nelms, Benjamin E., additional, He, Renjie, additional, Naser, Mohammed A., additional, Duke, Simon, additional, Sherer, Michael V., additional, Christodouleas, John P., additional, Mohamed, Abdallah S. R., additional, Cislo, Michael, additional, Murphy, James D., additional, Fuller, Clifton D., additional, and Gillespie, Erin F., additional

Published
2022
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34. “E Pluribus Unum”: Prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology (C3RO) Crowdsourced Initiative for Multi-Observer Segmentation

Author

Lin, Diana, primary, Wahid, Kareem A., additional, Nelms, Benjamin E., additional, He, Renjie, additional, Naser, Mohammed A., additional, Duke, Simon, additional, Sherer, Michael V., additional, Christodouleas, John P., additional, Mohamed, Abdallah S. R., additional, Cislo, Michael, additional, Murphy, James D., additional, Fuller, Clifton D., additional, and Gillespie, Erin F., additional

Published
2022
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36. Cognitive function and patient‐reported memory problems after radiotherapy for cancers at the skull base: A cross‐sectional survivorship study using the Telephone Interview for Cognitive Status and the MD Anderson Symptom Inventory‐Head and Neck Module

Author

Hansen, Chase C., Smith, Joshua B., Mohamed, Abdallah S. R., Mulcahy, Collin F., Wefel, Jeffrey S., Hutcheson, Katherine A., Chrane, Kelsey, Phan, Jack, Frank, Steven J., Garden, Adam S., Smith, Blaine D., Eichelberger, Hillary, Anderson, Carthal, McCoy, Colton, Horiates, Marina, Patrick, Conner, Floris, Sarah, French, Chloe, Beadle, Beth M., Morrison, William H., Su, Shirley Y., Lewis, Carol M., Kupferman, Michael E., Johnson, Jason M., Skinner, Heath D., Lai, Stephen Y., Hanna, Ehab Y., Rosenthal, David I., Fuller, Clifton D., and Gunn, G. Brandon

Published
2017
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37. Changes in Apparent Diffusion Coefficient (ADC) in Serial Weekly MRI during Radiotherapy in Patients with Head and Neck Cancer: Results from the PREDICT-HN Study

Author

Ng, Sweet Ping, primary, Cardenas, Carlos E., additional, Bahig, Houda, additional, Elgohari, Baher, additional, Wang, Jihong, additional, Johnson, Jason M., additional, Moreno, Amy C., additional, Shah, Shalin J., additional, Garden, Adam S., additional, Phan, Jack, additional, Gunn, G. Brandon, additional, Frank, Steven J., additional, Ding, Yao, additional, Na, Lumine, additional, Yuan, Ying, additional, Urbauer, Diana, additional, Mohamed, Abdallah S. R., additional, Rosenthal, David I., additional, Morrison, William H., additional, MacManus, Michael P., additional, and Fuller, Clifton D., additional

Published
2022
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38. Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer

Author

Naser, Mohamed A., primary, Wahid, Kareem A., additional, Grossberg, Aaron J., additional, Olson, Brennan, additional, Jain, Rishab, additional, El-Habashy, Dina, additional, Dede, Cem, additional, Salama, Vivian, additional, Abobakr, Moamen, additional, Mohamed, Abdallah S. R., additional, He, Renjie, additional, Jaskari, Joel, additional, Sahlsten, Jaakko, additional, Kaski, Kimmo, additional, and Fuller, Clifton D., additional

Published
2022
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39. Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old

Author

MD Anderson Head and Neck Cancer Symptom Working Group, Eraj, Salman A., Jomaa, Mona K., Rock, Crosby D., Mohamed, Abdallah S. R., Smith, Blaine D., Smith, Joshua B., Browne, Theodora, Cooksey, Luke C., Williams, Bowman, Temple, Brandi, Preston, Kathryn E., Aymard, Jeremy M., Gross, Neil D., Weber, Randal S., Hessel, Amy C., Ferrarotto, Renata, Phan, Jack, Sturgis, Erich M., Hanna, Ehab Y., Frank, Steven J., Morrison, William H., Goepfert, Ryan P., Lai, Stephen Y., Rosenthal, David I., Mendoza, Tito R., Cleeland, Charles S., Hutcheson, Kate A., Fuller, Clifton D., Garden, Adam S., and Gunn, G. Brandon

Published
2017
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40. Three-Dimensional Evaluation of Isodose Radiation Volumes in Cases of Severe Mandibular Osteoradionecrosis for the Prediction of Recurrence after Segmental Resection

Author

Glas, Haye H., primary, Kraeima, Joep, additional, Tribius, Silke, additional, Leusink, Frank K. J., additional, Rendenbach, Carsten, additional, Heiland, Max, additional, Stromberger, Carmen, additional, Rashad, Ashkan, additional, Fuller, Clifton D., additional, Mohamed, Abdallah S. R., additional, Lai, Stephen Y., additional, and Witjes, Max J. H., additional

Published
2022
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45. Association of Body Composition With Survival and Locoregional Control of Radiotherapy-Treated Head and Neck Squamous Cell Carcinoma

Author

Grossberg, Aaron J., Chamchod, Sasikarn, Fuller, Clifton D., Mohamed, Abdallah S. R., Heukelom, Jolien, Eichelberger, Hillary, Kantor, Michael E., Hutcheson, Katherine A., Gunn, G. Brandon, Garden, Adam S., Frank, Steven, Phan, Jack, Beadle, Beth, Skinner, Heath D., Morrison, William H., and Rosenthal, David I.

Published
2016
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46. Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy.

Author

Naser, Mohamed A., Wahid, Kareem A., Ahmed, Sara, Salama, Vivian, Dede, Cem, Edwards, Benjamin W., Ruitao Lin, McDonald, Brigid, Salzillo, Travis C., Renjie He, Yao Ding, Abdelaal, Moamen Abobakr, Thill, Daniel, O’Connell, Nicolette, Willcut, Virgil, Christodouleas, John P., Lai, Stephen Y., Fuller, Clifton D., and Mohamed, Abdallah S. R.

Subjects
DIFFUSION magnetic resonance imaging, IMAGE registration, HEAD & neck cancer, FUNCTIONAL magnetic resonance imaging, CANCER radiotherapy, SUBMANDIBULAR gland, QUALITY assurance
Abstract

Background/Purpose: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images. Materials/Methods: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the nonregistered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance. Results: 19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods. Conclusions: The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy. [ABSTRACT FROM AUTHOR]

Published
2023
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47. The influence of radiation dose on taste impairment in a prospective observational study cohort of oropharyngeal cancer patients

Author

Stieb, Sonja, primary, Engeseth, Grete M., additional, Mohamed, Abdallah S. R., additional, He, Renjie, additional, Perez-Martinez, Ismael, additional, Rock, Stockton, additional, Deshpande, Tanaya S., additional, Garden, Adam S., additional, Rosenthal, David I., additional, Frank, Steven J., additional, Gunn, G. Brandon, additional, and Fuller, C. David, additional

Published
2022
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48. Head and Neck Cancer Predictive Risk Estimator to Determine Control and Therapeutic Outcomes of Radiotherapy (HNC-PREDICTOR) Development, International Multi-Institutional Validation, and Web-Implementation of Clinic-Ready Model-Based Risk Stratification for Head and Neck Cancer

Author

van Dijk, Lisanne, primary, Mohamed, Abdallah S. R., additional, Ahmed, Sara, additional, Nipu, Nafiul, additional, Marai, Elisabeta, additional, Wahid, Kareem, additional, Sijtsema, Nanna M., additional, Gunn, Brandon, additional, Garden, Adam S., additional, Moreno, A, additional, Hope, Andrew, additional, Langendijk, Johannes A., additional, and Fuller, Clifton D., additional

Published
2022
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49. Quality Assurance Assessment of Intra-Acquisition Diffusion-Weighted and T2-Weighted Magnetic Resonance Imaging Registration and Contour Propagation for Head and Neck Cancer Radiotherapy

Author

Naser, Mohamed A., primary, Wahid, Kareem A., additional, Ahmed, Sara, additional, Salama, Vivian, additional, Dede, Cem, additional, Edwards, Benjamin W., additional, Lin, Ruitao, additional, McDonald, Brigid, additional, Salzillo, Travis C., additional, He, Renjie, additional, Ding, Yao, additional, Abdelaal, Moamen Abobakr, additional, Thill, Daniel, additional, O’Connell, Nicolette, additional, Willcut, Virgil, additional, Christodouleas, John P., additional, Lai, Stephen Y, additional, Fuller, Clifton D., additional, and Mohamed, Abdallah S. R., additional

Published
2021
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