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314 results on '"Elhalawani, Hesham"'

1. The impact of using an AI chatbot to respond to patient messages

Author

Chen, Shan, Guevara, Marco, Moningi, Shalini, Hoebers, Frank, Elhalawani, Hesham, Kann, Benjamin H., Chipidza, Fallon E., Leeman, Jonathan, Aerts, Hugo J. W. L., Miller, Timothy, Savova, Guergana K., Mak, Raymond H., Lustberg, Maryam, Afshar, Majid, and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language
Abstract

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation., Comment: 4 figures and tables in main, submitted for review

Published
2023

2. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

3. Artificial Intelligence and Radiology Education

Author

Tejani, Ali S, Elhalawani, Hesham, Moy, Linda, Kohli, Marc, and Kahn, Charles E

Subjects
Information and Computing Sciences, Human-Centred Computing, Biomedical Imaging, Good Health and Well Being, Quality Education, Artificial Intelligence, Imaging Informatics, Impact of AI on Education, Medical Education, Natural Language Processing, Precision Education, Use of AI in Education
Abstract

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.

Published
2023

4. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, Oreiller, Valentin, Boughdad, Sarah, Rest, Catherine Chez Le, Elhalawani, Hesham, Jreige, Mario, Prior, John O., Vallières, Martin, Visvikis, Dimitris, Hatt, Mathieu, and Depeursinge, Adrien

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.

Published
2022

5. Graded Prognostic Assessment (GPA) for Patients With Lung Cancer and Brain Metastases: Initial Report of the Small Cell Lung Cancer GPA and Update of the Non-Small Cell Lung Cancer GPA Including the Effect of Programmed Death Ligand 1 and Other Prognostic Factors.

Author

Sperduto, Paul, De, Brian, Li, Jing, Carpenter, David, Kirkpatrick, John, Milligan, Michael, Shih, Helen, Kutuk, Tugce, Kotecha, Rupesh, Higaki, Hajime, Otsuka, Manami, Aoyama, Hidefumi, Bourgoin, Malie, Roberge, David, Dajani, Salah, Sachdev, Sean, Gainey, Jordan, Buatti, John, Breen, William, Brown, Paul, Gallitto, Matthew, Wang, Tony, Shanley, Ryan, Lou, Emil, Shiao, Jay, Gaspar, Laurie, Tanabe, Satoshi, Nakano, Toshimichi, An, Yi, Chiang, Veronica, Zeng, Liang, Soliman, Hany, Elhalawani, Hesham, Cagney, Daniel, Thomas, Evan, Boggs, Drexell, Ahluwalia, Manmeet, Mehta, Minesh, Braunstein, Steve, and Ni, Lisa

Subjects
Adenocarcinoma, Adenocarcinoma of Lung, Anaplastic Lymphoma Kinase, B7-H1 Antigen, Brain Neoplasms, Carcinoma, Non-Small-Cell Lung, ErbB Receptors, Humans, Lung Neoplasms, Prognosis, Retrospective Studies, Small Cell Lung Carcinoma
Abstract

PURPOSE: Patients with lung cancer and brain metastases represent a markedly heterogeneous population. Accurate prognosis is essential to optimally individualize care. In prior publications, we described the graded prognostic assessment (GPA), but a GPA for patients with small cell lung cancer (SCLC) has never been reported, and in non-small cell lung cancer (NSCLC), the effect of programmed death ligand 1 (PD-L1) was unknown. The 3-fold purpose of this work is to provide the initial report of an SCLC GPA, to evaluate the effect of PD-L1 on survival in patients with NSCLC, and to update the Lung GPA accordingly. METHODS AND MATERIALS: A multivariable analysis of prognostic factors and treatments associated with survival was performed on 4183 patients with lung cancer (3002 adenocarcinoma, 611 nonadenocarcinoma, 570 SCLC) with newly diagnosed brain metastases between January 1, 2015, and December 31, 2020, using a multi-institutional retrospective database. Significant variables were used to update the Lung GPA. RESULTS: Overall median survival for lung adenocarcinoma, SCLC, and nonadenocarcinoma was 17, 10, and 8 months, respectively, but varied widely by GPA from 2 to 52 months. In SCLC, the significant prognostic factors were age, performance status, extracranial metastases, and number of brain metastases. In NSCLC, the distribution of molecular markers among patients with lung adenocarcinoma and known primary tumor molecular status revealed alterations/expression in PD-L1 50% to 100%, PD-L1 1% to 49%, epidermal growth factor receptor, and anaplastic lymphoma kinase in 32%, 31%, 30%, and 7%, respectively. Median survival of patients with lung adenocarcinoma and brain metastases with 0, 1% to 49%, and ≥50% PD-L1 expression was 17, 19, and 24 months, respectively (P < .01), confirming PD-L1 is a prognostic factor. Previously identified prognostic factors for NSCLC (epidermal growth factor receptor and anaplastic lymphoma kinase status, performance status, age, number of brain metastases, and extracranial metastases) were reaffirmed. These factors were incorporated into the updated Lung GPA with robust separation between subgroups for all histologies. CONCLUSIONS: Survival for patients with lung cancer and brain metastases has improved but varies widely. The initial report of a GPA for SCLC is presented. For patients with NSCLC-adenocarcinoma and brain metastases, PD-L1 is a newly identified significant prognostic factor, and the previously identified factors were reaffirmed. The updated indices establish unique criteria for SCLC, NSCLC-nonadenocarcinoma, and NSCLC-adenocarcinoma (incorporating PD-L1). The updated Lung GPA, available for free at brainmetgpa.com, provides an accurate tool to estimate survival, individualize treatment, and stratify clinical trials.

Published
2022

6. Patterns of recurrence after radiotherapy for high-risk neuroblastoma: Implications for radiation dose and field

Author

Liu, Kevin X., Shaaban, Sherif G., Chen, Jie Jane, Bagatell, Rochelle, Lerman, Benjamin J., Catalano, Paul J., DuBois, Steven G., Shusterman, Suzanne, Ioakeim-Ioannidou, Myrsini, Yock, Torunn I., Shamberger, Robert C., Mattei, Peter, Vu, Lan, Elhalawani, Hesham, Dusenbery, Kathryn E., Vo, Kieuhoa T., Huang, Mary S., Friedmann, Alison M., Diller, Lisa R., Marcus, Karen J., MacDonald, Shannon M., Terezakis, Stephanie A., Braunstein, Steve E., Hill-Kayser, Christine E., and Haas-Kogan, Daphne A.

Published
2024
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7. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Abobakr, Moamen, Akhavanallaf, Azadeh, Balermpas, Panagiotis, Boughdad, Sarah, Capriotti, Leo, Castelli, Joel, Cheze Le Rest, Catherine, Decazes, Pierre, Correia, Ricardo, El-Habashy, Dina, Elhalawani, Hesham, Fuller, Clifton D., Jreige, Mario, Khamis, Yomna, La Greca, Agustina, Mohamed, Abdallah, Naser, Mohamed, Prior, John O., Ruan, Su, Tanadini-Lang, Stephanie, Tankyevych, Olena, Salimi, Yazdan, Vallières, Martin, Vera, Pierre, Visvikis, Dimitris, Wahid, Kareem, Zaidi, Habib, Hatt, Mathieu, Depeursinge, Adrien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Andrearczyk, Vincent, editor, Oreiller, Valentin, editor, Hatt, Mathieu, editor, and Depeursinge, Adrien, editor

Published
2023
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10. Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning

Author

Tak, Divyanshu, primary, Ye, Zezhong, additional, Zapaischykova, Anna, additional, Zha, Yining, additional, Boyd, Aidan, additional, Vajapeyam, Sridhar, additional, Chopra, Rishi, additional, Hayat, Hasaan, additional, Prabhu, Sanjay P., additional, Liu, Kevin X., additional, Elhalawani, Hesham, additional, Nabavizadeh, Ali, additional, Familiar, Ariana, additional, Resnick, Adam C., additional, Mueller, Sabine, additional, Aerts, Hugo J. W. L., additional, Bandopadhayay, Pratiti, additional, Ligon, Keith L., additional, Haas-Kogan, Daphne A., additional, Poussaint, Tina Y., additional, and Kann, Benjamin H., additional

Published
2024
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11. Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Author

Andrearczyk, Vincent, Oreiller, Valentin, Jreige, Mario, Vallières, Martin, Castelli, Joel, Elhalawani, Hesham, Boughdad, Sarah, Prior, John O., Depeursinge, Adrien, 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, and Depeursinge, Adrien, editor

Published
2021
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12. Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Author

Andrearczyk, Vincent, primary, Oreiller, Valentin, additional, Boughdad, Sarah, additional, Rest, Catherine Cheze Le, additional, Elhalawani, Hesham, additional, Jreige, Mario, additional, Prior, John O., additional, Vallières, Martin, additional, Visvikis, Dimitris, additional, Hatt, Mathieu, additional, and Depeursinge, Adrien, additional

Published
2022
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13. 18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer

Author

Elhalawani, Hesham, Cardenas, Carlos E., Volpe, Stefania, Barua, Souptik, Stieb, Sonja, Rock, Calvin B., Lin, Timothy, Yang, Pei, Wu, Haijun, Zaveri, Jhankruti, Elgohari, Baher, Abdallah, Lamiaa E., Jethanandani, Amit, Mohamed, Abdallah S.R., Court, Laurence E., Hutcheson, Katherine A., Brandon Gunn, G., Rosenthal, David I., Frank, Steven J., Garden, Adam S., Rao, Arvind, and Fuller, Clifton D.

Published
2021
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14. Quantitative Dynamic Contrast-Enhanced MRI Identifies Radiation-Induced Vascular Damage in Patients With Advanced Osteoradionecrosis: Results of a Prospective Study

Author

Mohamed, Abdallah S.R., He, Renjie, Ding, Yao, Wang, Jihong, Fahim, Joly, Elgohari, Baher, Elhalawani, Hesham, Kim, Andrew D., Ahmed, Hoda, Garcia, Jose A., Johnson, Jason M., Stafford, R. Jason, Bankson, James A., Chambers, Mark S., Sandulache, Vlad C., Fuller, Clifton D., and Lai, Stephen Y.

Published
2020
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15. Comparison of tumor delineation using dual energy computed tomography versus magnetic resonance imaging in head and neck cancer re-irradiation cases

Author

Ng, Sweet Ping, Cardenas, Carlos E, Elhalawani, Hesham, Pollard, Courtney, III, Elgohari, Baher, Fang, Penny, Meheissen, Mohamed, Guha-Thakurta, Nandita, Bahig, Houda, Johnson, Jason M., Kamal, Mona, Garden, Adam S, Reddy, Jay P., Su, Shirley Y., Ferrarotto, Renata, Frank, Steven J., Brandon Gunn, G., Moreno, Amy C., Rosenthal, David I., Fuller, Clifton D., and Phan, Jack

Published
2020
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19. Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

Author

Christopherson, Kaitlin M., Ghosh, Alokananda, Mohamed, Abdallah Sherif Radwan, Kamal, Mona, Gunn, G. Brandon, Dale, Timothy, Kalpathy-Cramer, Jayashree, Messer, Jay, Garden, Adam S., Elhalawani, Hesham, Frank, Steven J., Lewin, Jan, Morrison, William H., Phan, Jack, Gross, Neil, Ferrarotto, Renata, Weber, Randal S., Rosenthal, David I., Lai, Stephen Y., Hutcheson, Katherine, Fuller, Clifton David, Radwan Mohamed, Abdallah Sherif, Marai, G. Elisabeta (Liz), Canahuate, Guadalupe, Vock, David M., and Fuller, David

Published
2019
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20. Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients

Author

Kiser, Kendall, Meheissen, Mohamed A.M., Mohamed, Abdallah S.R., Kamal, Mona, Ng, Sweet Ping, Elhalawani, Hesham, Jethanandani, Amit, He, Renjie, Ding, Yao, Rostom, Yousri, Hegazy, Neamat, Bahig, Houda, Garden, Adam, Lai, Stephen, Phan, Jack, Gunn, Gary B., Rosenthal, David, Frank, Steven, Brock, Kristy K., Wang, Jihong, and Fuller, Clifton D.

Published
2019
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21. An in-silico quality assurance study of contouring target volumes in thoracic tumors within a cooperative group setting

Author

Elhalawani, Hesham, Elgohari, Baher, Lin, Timothy A., Mohamed, Abdallah S.R., Fitzgerald, Thomas J., Laurie, Fran, Ulin, Kenneth, Kalpathy-Cramer, Jayashree, Guerrero, Thomas, Holliday, Emma B., Russo, Gregory, Patel, Abhilasha, Jones, William, Walker, Gary V., Awan, Musaddiq, Choi, Mehee, Dagan, Roi, Mahmoud, Omar, Shapiro, Anna, Kong, Feng-Ming (Spring), Gomez, Daniel, Zeng, Jing, Decker, Roy, Spoelstra, Femke O.B., Gaspar, Laurie E., Kachnic, Lisa A., Thomas, Charles R., Jr., Okunieff, Paul, and Fuller, Clifton D.

Published
2019
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23. A prospective longitudinal assessment of MRI signal intensity kinetics of non-target muscles in patients with advanced stage oropharyngeal cancer in relationship to radiotherapy dose and post-treatment radiation-associated dysphagia: Preliminary findings from a randomized trial

Author

Meheissen, Mohamed A.M., Mohamed, Abdallah S.R., Kamal, Mona, Hernandez, Mike, Volpe, Stefania, Elhalawani, Hesham, Barrow, Martha P., Ding, Yao, Wang, Jihong, Davuluri, Raj, Rostom, Yousri, Hegazy, Neamat, Gunn, G. Brandon, Lai, Stephen Y., Garden, Adam S., Lewin, Jan S., Rosenthal, David I., Frank, Steven J., Fuller, Clifton D., and Hutcheson, Katherine A.

Published
2019
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27. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function

Author

Cardenas, Carlos E., McCarroll, Rachel E., Court, Laurence E., Elgohari, Baher A., Elhalawani, Hesham, Fuller, Clifton D., Kamal, Mona J., Meheissen, Mohamed A.M., Mohamed, Abdallah S.R., Rao, Arvind, Williams, Bowman, Wong, Andrew, Yang, Jinzhong, and Aristophanous, Michalis

Published
2018
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33. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

Author

Bogowicz, Marta, Jochems, Arthur, Deist, Timo M., Tanadini-Lang, Stephanie, Huang, Shao Hui, Chan, Biu, Waldron, John N., Bratman, Scott, O’Sullivan, Brian, Riesterer, Oliver, Studer, Gabriela, Unkelbach, Jan, Barakat, Samir, Brakenhoff, Ruud H., Nauta, Irene, Gazzani, Silvia E., Calareso, Giuseppina, Scheckenbach, Kathrin, Hoebers, Frank, Wesseling, Frederik W. R., Keek, Simon, Sanduleanu, Sebastian, Leijenaar, Ralph T. H., Vergeer, Marije R., Leemans, C. René, Terhaard, Chris H. J., van den Brekel, Michiel W. M., Hamming-Vrieze, Olga, van der Heijden, Martijn A., Elhalawani, Hesham M., Fuller, Clifton D., Guckenberger, Matthias, and Lambin, Philippe

Published
2020
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35. 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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37. 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

39. 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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42. Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy

Author

Yang, Pei, primary, Zhao, Yu, additional, Liang, Hao, additional, Zhou, Guanzhi, additional, Youssef, Bassem, additional, Elhalawani, Hesham, additional, Li, Meizhen, additional, Tan, Fengbo, additional, Jin, Yi, additional, Jin, Hekun, additional, Zhu, Hong, additional, Mohamed, Abdallah Sherif Radwan, additional, Chonnipa, Nantavithya, additional, Kannarunimit, Danita, additional, Shi, Yingrui, additional, Wang, Hui, additional, and Fuller, Clifton David, additional

Published
2022
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44. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Author

Oreiller, Valentin, primary, Andrearczyk, Vincent, additional, Jreige, Mario, additional, Boughdad, Sarah, additional, Elhalawani, Hesham, additional, Castelli, Joel, additional, Vallières, Martin, additional, Zhu, Simeng, additional, Xie, Juanying, additional, Peng, Ying, additional, Iantsen, Andrei, additional, Hatt, Mathieu, additional, Yuan, Yading, additional, Ma, Jun, additional, Yang, Xiaoping, additional, Rao, Chinmay, additional, Pai, Suraj, additional, Ghimire, Kanchan, additional, Feng, Xue, additional, Naser, Mohamed A., additional, Fuller, Clifton D., additional, Yousefirizi, Fereshteh, additional, Rahmim, Arman, additional, Chen, Huai, additional, Wang, Lisheng, additional, Prior, John O., additional, and Depeursinge, Adrien, additional

Published
2022
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45. sj-docx-6-jbm-10.1177_03936155221110250 - Supplemental material for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy

Author

Yang, Pei, Zhao, Yu, Liang, Hao, Zhou, Guanzhi, Youssef, Bassem, Elhalawani, Hesham, Li, Meizhen, Tan, Fengbo, Jin, Yi, Jin, Hekun, Zhu, Hong, Mohamed, Abdallah Sherif Radwan, Chonnipa, Nantavithya, Kannarunimit, Danita, Shi, Yingrui, Wang, Hui, and Fuller, Clifton David

Subjects
FOS: Clinical medicine, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, sj-docx-6-jbm-10.1177_03936155221110250 for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy by Pei Yang, Yu Zhao, Hao Liang, Guanzhi Zhou, Bassem Youssef, Hesham Elhalawani, Meizhen Li, Fengbo Tan, Yi Jin, Hekun Jin, Hong Zhu, Abdallah Sherif Radwan Mohamed, Nantavithya Chonnipa, Danita Kannarunimit, Yingrui Shi, Hui Wang and Clifton David Fuller in The International Journal of Biological Markers

Published
2022
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46. sj-docx-7-jbm-10.1177_03936155221110250 - Supplemental material for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy

Author

Yang, Pei, Zhao, Yu, Liang, Hao, Zhou, Guanzhi, Youssef, Bassem, Elhalawani, Hesham, Li, Meizhen, Tan, Fengbo, Jin, Yi, Jin, Hekun, Zhu, Hong, Mohamed, Abdallah Sherif Radwan, Chonnipa, Nantavithya, Kannarunimit, Danita, Shi, Yingrui, Wang, Hui, and Fuller, Clifton David

Subjects
FOS: Clinical medicine, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, sj-docx-7-jbm-10.1177_03936155221110250 for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy by Pei Yang, Yu Zhao, Hao Liang, Guanzhi Zhou, Bassem Youssef, Hesham Elhalawani, Meizhen Li, Fengbo Tan, Yi Jin, Hekun Jin, Hong Zhu, Abdallah Sherif Radwan Mohamed, Nantavithya Chonnipa, Danita Kannarunimit, Yingrui Shi, Hui Wang and Clifton David Fuller in The International Journal of Biological Markers

Published
2022
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47. sj-docx-8-jbm-10.1177_03936155221110250 - Supplemental material for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy

Author

Yang, Pei, Zhao, Yu, Liang, Hao, Zhou, Guanzhi, Youssef, Bassem, Elhalawani, Hesham, Li, Meizhen, Tan, Fengbo, Jin, Yi, Jin, Hekun, Zhu, Hong, Mohamed, Abdallah Sherif Radwan, Chonnipa, Nantavithya, Kannarunimit, Danita, Shi, Yingrui, Wang, Hui, and Fuller, Clifton David

Subjects
FOS: Clinical medicine, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental material, sj-docx-8-jbm-10.1177_03936155221110250 for Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy by Pei Yang, Yu Zhao, Hao Liang, Guanzhi Zhou, Bassem Youssef, Hesham Elhalawani, Meizhen Li, Fengbo Tan, Yi Jin, Hekun Jin, Hong Zhu, Abdallah Sherif Radwan Mohamed, Nantavithya Chonnipa, Danita Kannarunimit, Yingrui Shi, Hui Wang and Clifton David Fuller in The International Journal of Biological Markers

Published
2022
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49. Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients

Author

Barua, Souptik, primary, Elhalawani, Hesham, additional, Volpe, Stefania, additional, Al Feghali, Karine A., additional, Yang, Pei, additional, Ng, Sweet Ping, additional, Elgohari, Baher, additional, Granberry, Robin C., additional, Mackin, Dennis S., additional, Gunn, G. Brandon, additional, Hutcheson, Katherine A., additional, Chambers, Mark S., additional, Court, Laurence E., additional, Mohamed, Abdallah S. R., additional, Fuller, Clifton D., additional, Lai, Stephen Y., additional, and Rao, Arvind, additional

Published
2021
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50. Additional file 5 of Tobacco exposure as a major modifier of oncologic outcomes in human papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma

Author

Elhalawani, Hesham, Mohamed, Abdallah S. R., Elgohari, Baher, Lin, Timothy A., Sikora, Andrew G., Lai, Stephen Y., Abdelrahman Abusaif, Phan, Jack, Morrison, William H., G. Brandon Gunn, Rosenthal, David I., Garden, Adam S., Fuller, Clifton D., and Sandulache, Vlad C.

Abstract

Additional file 5: Supplementary Table 1. HPV and tobacco impact on overall survival. Uni- and multi-variable analyses and corresponding hazard ratios and 95% confidence intervals (CI) for clinical variables associated with overall survival for the entire cohort stratified by human papillomavirus (HPV) status. *For AJCC stage (7th edition) analysis; stages I-III were collectively compared against stage IV (the majority class) given the imbalanced distribution of patients among AJCC 7th stages.

Published
2020
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