Current Environment: Production

Medical Services

Specialties

Programs & Services

Languages

  • Arabic
  • English

Education

Medical School

Ain Shams University, Faculty of Medicine

2011, Cairo, Egypt

Internship

Internal Medicine

Ain Shams University, Faculty of Medicine

2012, Cairo, Egypt

Residency

Clinical Oncology/Nuclear Medicine

Ain Shams University, Faculty of Medicine

2015, Cairo, Egypt

Fellowship

Radiation Oncology

University of Texas MD Anderson Cancer Center

2019, Houston, TX

Fellowship

Radiation Oncology

Cleveland Clinic Taussig Cancer Institute

2020, Cleveland, OH

Fellowship

CNS Radiation Oncology

Brigham and Women’s Hospital

2022, Boston, MA

Professional History

Dr. Elhalawani is an Attending Physician in the Department of Radiation Oncology and Instructor in Radiation Oncology at Harvard Medical School. He completed medical school, internship and residency at Ain Shams University in Cairo, Egypt, followed by a research fellowship in radiation oncology at the University of Texas MD Anderson Cancer Center, and clinical fellowships in radiation oncology at the Cleveland Clinic and the Dana-Farber Brigham Cancer Center.

Dr. Elhalawani’s clinical focus is on cancers affecting pediatric patients. He directs his research efforts toward leveraging machine learning and imaging informatics to personalize care for cancer patients. He has a particular interest in integrating multi-parametric magnetic resonance imaging (mpMRI) and quantitative imaging analytics to advance image-guided cancer treatment and surveillance and has led efforts to develop clinical applications of radiomics analytics in radiation oncology.

Publications

  1. Impact of brain metastasis size at the time of radiotherapy on local control and radiation necrosis. J Neurooncol. 2025 Apr 15. View Abstract

  2. Treatment Approach for Metastatic Intracranial Germinoma: A Multi-Institutional Experience. Pediatr Blood Cancer. 2025 May; 72(5):e31628. View Abstract

  3. The association between postoperative photon radiotherapy dose and disease control and salvage treatment in pediatric and adolescent ependymoma: a multi-institutional investigation. J Neurooncol. 2025 May; 173(1):167-177. View Abstract

  4. Hypofractionated Palliative Radiotherapy for Relapsed and Refractory High-Risk Neuroblastoma. Curr Oncol. 2025 Feb 22; 32(3). View Abstract

  5. Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas. Neuro Oncol. 2025 Jan 12; 27(1):277-290. View Abstract

  6. The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ArXiv. 2024 Dec 09. View Abstract

  7. Patterns of recurrence after radiotherapy for high-risk neuroblastoma: Implications for radiation dose and field. Radiother Oncol. 2024 09; 198:110384. View Abstract

  8. Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning. Radiol Artif Intell. 2024 May; 6(3):e230333. View Abstract

  9. The effect of using a large language model to respond to patient messages. Lancet Digit Health. 2024 Jun; 6(6):e379-e381. View Abstract

  10. Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning. medRxiv. 2023 Nov 22. View Abstract

  11. A genomic score to predict local control among patients with brain metastases managed with radiation. Neuro Oncol. 2023 10 03; 25(10):1815-1827. View Abstract

  12. Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge. Med Image Anal. 2023 Dec; 90:102972. View Abstract

  13. Radiotherapy for Primary Pediatric Central Nervous System Malignancies: Current Treatment Paradigms and Future Directions. Pediatr Neurosurg. 2023; 58(5):356-366. View Abstract

  14. Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas. Oral Oncol. 2023 09; 144:106460. View Abstract

  15. Incidence proportion and prognosis of leptomeningeal disease among patients with breast vs. non-breast primaries. Neuro Oncol. 2023 05 04; 25(5):973-983. View Abstract

  16. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. Head Neck Tumor Chall (2022). 2023; 13626:1-30. View Abstract

  17. Artificial Intelligence and Radiology Education. Radiol Artif Intell. 2023 Jan; 5(1):e220084. View Abstract

  18. Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy. Int J Biol Markers. 2022 Sep; 37(3):270-279. View Abstract

  19. 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. Int J Radiat Oncol Biol Phys. 2022 09 01; 114(1):60-74. View Abstract

  20. 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. Int J Radiat Oncol Biol Phys. 2022 06 01; 113(2):426-436. View Abstract

  21. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal. 2022 04; 77:102336. View Abstract

  22. 18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer. Clin Transl Radiat Oncol. 2021 Jul; 29:93-101. View Abstract

  23. Are Artificial Intelligence Challenges Becoming Radiology's New "Bee's Knees"? Radiol Artif Intell. 2021 May; 3(3):e210056. View Abstract

  24. Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients. Front Artif Intell. 2021; 4:618469. View Abstract

  25. Real-world evaluation of the impact of radiotherapy and chemotherapy in elderly patients with glioblastoma based on age and performance status. Neurooncol Pract. 2021 Apr; 8(2):199-208. View Abstract

  26. Tobacco exposure as a major modifier of oncologic outcomes in human papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma. BMC Cancer. 2020 Sep 23; 20(1):912. View Abstract

  27. Biomechanical modeling of radiation dose-induced volumetric changes of the parotid glands for deformable image registration. Phys Med Biol. 2020 08 31; 65(16):165017. View Abstract

  28. PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines. Med Phys. 2020 Nov; 47(11):5941-5952. View Abstract

  29. A predictive model of radiation-related fibrosis based on the radiomic features of magnetic resonance imaging and computed tomography. Transl Cancer Res. 2020 Aug; 9(8):4726-4738. View Abstract

  30. Quantitative Dynamic Contrast-Enhanced MRI Identifies Radiation-Induced Vascular Damage in Patients With Advanced Osteoradionecrosis: Results of a Prospective Study. Int J Radiat Oncol Biol Phys. 2020 12 01; 108(5):1319-1328. View Abstract

  31. How Might AI and Chest Imaging Help Unravel COVID-19's Mysteries? Radiol Artif Intell. 2020 May; 2(3):e200053. View Abstract

  32. Comparison of tumor delineation using dual energy computed tomography versus magnetic resonance imaging in head and neck cancer re-irradiation cases. Phys Imaging Radiat Oncol. 2020 Apr; 14:1-5. View Abstract

  33. Optimal Timing of Radiotherapy Following Gross Total or Subtotal Resection of Glioblastoma: A Real-World Assessment using the National Cancer Database. Sci Rep. 2020 03 18; 10(1):4926. View Abstract

  34. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Sci Rep. 2020 03 11; 10(1):4542. View Abstract

  35. Simultaneously spatial and temporal Higher-Order Total Variations for noise suppression and motion reduction in DCE and IVIM. Proc SPIE Int Soc Opt Eng. 2020 Feb; 11313. View Abstract

  36. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction. Sci Rep. 2020 03 02; 10(1):3811. View Abstract

  37. Detection of Glioblastoma Subclinical Recurrence Using Serial Diffusion Tensor Imaging. Cancers (Basel). 2020 Feb 29; 12(3). View Abstract

  38. Data from a terminated study on iron oxide nanoparticle magnetic resonance imaging for head and neck tumors. Sci Data. 2020 02 21; 7(1):63. View Abstract

  39. A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine. J Biomed Inform. 2020; 112S:100067. View Abstract

  40. Real-world applications of deep convolutional neural networks in diagnostic cancer imaging. Chin Clin Oncol. 2020 Dec; 9(6):82. View Abstract

  41. CD8 infiltration is associated with disease control and tobacco exposure in intermediate-risk oropharyngeal cancer. Sci Rep. 2020 01 14; 10(1):243. View Abstract

  42. Lymphopenia during radiotherapy in patients with oropharyngeal cancer. Radiother Oncol. 2020 04; 145:95-100. View Abstract

  43. Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer. Clin Transl Radiat Oncol. 2020 Mar; 21:11-18. View Abstract

  44. Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am. 2020 02; 34(1):293-306. View Abstract

  45. Automatic detection of contouring errors using convolutional neural networks. Med Phys. 2019 Nov; 46(11):5086-5097. View Abstract

  46. Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients. PLoS One. 2019; 14(9):e0222509. View Abstract

  47. Biomechanical modeling of neck flexion for deformable alignment of the salivary glands in head and neck cancer images. Phys Med Biol. 2019 09 05; 64(17):175018. View Abstract

  48. Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. IEEE Trans Vis Comput Graph. 2020 01; 26(1):949-959. View Abstract

  49. Differences between planned and delivered dose for head and neck cancer, and their consequences for normal tissue complication probability and treatment adaptation. Radiother Oncol. 2020 01; 142:100-106. View Abstract

  50. The Potential and Pitfalls of Crowdsourced Algorithm Development in Radiation Oncology. JAMA Oncol. 2019 05 01; 5(5):662-663. View Abstract

  51. Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients. Clin Transl Radiat Oncol. 2019 Sep; 18:120-127. View Abstract

  52. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma. Sci Rep. 2019 02 04; 9(1):1322. View Abstract

  53. Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive. JCO Clin Cancer Inform. 2019 02; 3:1-9. View Abstract

  54. Radiographic retropharyngeal lymph node involvement in HPV-associated oropharyngeal carcinoma: Patterns of involvement and impact on patient outcomes. Cancer. 2019 05 01; 125(9):1536-1546. View Abstract

  55. An in-silico quality assurance study of contouring target volumes in thoracic tumors within a cooperative group setting. Clin Transl Radiat Oncol. 2019 Feb; 15:83-92. View Abstract

  56. Evaluating the Effect of Right-Censored End Point Transformation for Radiomic Feature Selection of Data From Patients With Oropharyngeal Cancer. JCO Clin Cancer Inform. 2018 12; 2:1-19. View Abstract

  57. Author Correction: Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy. Sci Data. 2018 11 27; 5(1):1. View Abstract

  58. Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks. Phys Med Biol. 2018 11 07; 63(21):215026. View Abstract

  59. Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis. Comput Med Imaging Graph. 2018 11; 69:134-139. View Abstract

  60. Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy. Sci Data. 2018 09 04; 5:180173. View Abstract

  61. Patterns of Local-Regional Failure After Intensity Modulated Radiation Therapy or Passive Scattering Proton Therapy With Concurrent Chemotherapy for Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys. 2019 01 01; 103(1):123-131. View Abstract

  62. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol. 2018; 8:294. View Abstract

  63. Utilization of short-course radiation therapy for patients with nonmetastatic rectal adenocarcinoma in the United States. Adv Radiat Oncol. 2018 Oct-Dec; 3(4):611-620. View Abstract

  64. Outcomes of patients in the national cancer database treated non-surgically for localized rectal cancer. J Gastrointest Oncol. 2018 Aug; 9(4):589-600. View Abstract

  65. Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review. Front Oncol. 2018; 8:131. View Abstract

  66. Gemcitabine-based chemotherapy for advanced biliary tract carcinomas. Cochrane Database Syst Rev. 2018 Apr 06; 4:CD011746. View Abstract

  67. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function. Int J Radiat Oncol Biol Phys. 2018 06 01; 101(2):468-478. View Abstract

  68. Treatment at a high-volume centre is associated with improved survival among patients with non-metastatic hepatocellular carcinoma. Liver Int. 2018 04; 38(4):665-675. View Abstract

  69. A large-scale retrospective study of the overall survival outcome in nasopharyngeal carcinoma with hypertension in Chinese population. Oncotarget. 2017 Sep 26; 8(43):75577-75586. View Abstract

  70. Analysis of the correlation among hypertension, the intake of ß-blockers, and overall survival outcome in patients undergoing chemoradiotherapy with inoperable stage III non-small cell lung cancer. Am J Cancer Res. 2017; 7(4):946-954. View Abstract

  71. Risk of Distinctive Hair Changes Associated With Pazopanib in Patients With Renal Cell Carcinoma (RCC) Versus Patients Without RCC: A Comparative Systematic Review and Meta-analysis. Clin Genitourin Cancer. 2017 06; 15(3):e325-e335. View Abstract

  72. Risk of selected gastrointestinal and hepatic toxicities in cancer patients treated with nintedanib: a meta-analysis. Future Oncol. 2016 Sep; 12(18):2163-72. View Abstract

  73. Risk of cardiovascular adverse events in patients with solid tumors treated with ramucirumab: A meta analysis and summary of other VEGF targeted agents. Crit Rev Oncol Hematol. 2016 Jun; 102:89-100. View Abstract

  74. S-1-based regimens and the risk of leucopenic complications; a Meta-analysis with comparison to other fluoropyrimidines and non fluoropyrimidines. Expert Opin Drug Saf. 2016; 15(4):437-48. View Abstract

  75. S-1-based regimens for locally advanced/metastatic non-small-cell lung cancer: a meta-analysis. Future Oncol. 2016 Mar; 12(5):701-13. View Abstract

  76. Risk of endocrine complications in cancer patients treated with immune check point inhibitors: a meta-analysis. Future Oncol. 2016 Feb; 12(3):413-25. View Abstract

  77. Risk of elevated transaminases in non-small cell lung cancer (NSCLC) patients treated with erlotinib, gefitinib and afatinib: a meta-analysis. Expert Rev Respir Med. 2016 Feb; 10(2):223-34. View Abstract

  78. Risk of Selected Cardiovascular Toxicities in Patients With Cancer Treated With MEK Inhibitors: A Comparative Systematic Review and Meta-Analysis. J Glob Oncol. 2015 Dec; 1(2):73-82. View Abstract

  79. Risk of selected dermatological toxicities in cancer patients treated with MEK inhibitors: a comparative systematic review and meta-analysis. Future Oncol. 2015; 11(24):3307-19. View Abstract

  80. Doublet BRAF/MEK inhibition versus single-agent BRAF inhibition in the management of BRAF-mutant advanced melanoma, biological rationale and meta-analysis of published data. Clin Transl Oncol. 2016 Aug; 18(8):848-58. View Abstract

  81. S-1-based regimens and the risk of oral and gastrointestinal mucosal injury: a meta-analysis with comparison to other fluoropyrimidines. Expert Opin Drug Saf. 2016 Jan; 15(1):5-20. View Abstract

  82. Risk of gastrointestinal complications in cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Immunotherapy. 2015; 7(11):1213-27. View Abstract

  83. Risk of hematological toxicities in patients with solid tumors treated with ramucirumab: a meta-analysis. Future Oncol. 2015; 11(21):2949-61. View Abstract

  84. Risk of selected gastrointestinal toxicities in cancer patients treated with MEK inhibitors: a comparative systematic review and meta-analysis. Expert Rev Gastroenterol Hepatol. 2015; 9(11):1433-45. View Abstract

  85. Risk of elevated transaminases in cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Expert Opin Drug Saf. 2015 Oct; 14(10):1507-18. View Abstract

  86. Risk of oral and gastrointestinal mucosal injury in patients with solid tumors treated with ramucirumab: a systematic review and meta-analysis. Expert Opin Drug Saf. 2015 Oct; 14(10):1495-506. View Abstract

  87. Proteinuria in Patients with Solid Tumors Treated with Ramucirumab: A Systematic Review and Meta-Analysis. Chemotherapy. 2014; 60(5-6):325-33. View Abstract

  88. Risk of cutaneous toxicities in patients with solid tumors treated with immune checkpoint inhibitors: a meta-analysis. Future Oncol. 2015; 11(17):2471-84. View Abstract

  89. Critical evaluation of ramucirumab in the treatment of advanced gastric and gastroesophageal cancers. Ther Clin Risk Manag. 2015; 11:1123-32. View Abstract

  90. Risk of fatal pulmonary events in patients with advanced non-small-cell lung cancer treated with EGF receptor tyrosine kinase inhibitors: a comparative meta-analysis. Future Oncol. 2015; 11(7):1109-22. View Abstract

  91. Adjuvant systemic treatment for elderly breast cancer patients; addressing safety concerns. Expert Opin Drug Saf. 2014 Nov; 13(11):1443-67. View Abstract
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