Books and Book Chapters

Follow

• 

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

  Kareem A. Wahid, Cem Dede, Dina M. El-Habashy, Serageldin Kamel, Michael K. Rooney, Yomna Khamis, Moamen R. A. Abdelaal, Sara Ahmed, Kelsey L. Corrigan, and Enoch Chang

  Publication Date: 5-3-2025

  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 final testing hosted on grand-challenge.org 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.

• 

  Trigeminal Neuralgia

  Zaid Siddiqui and Peter Chen

  Publication Date: 11-27-2024

  This new edition elucidates the radiation therapy protocols and procedures for the management of adult patients presenting with primary benign and malignant central nervous system tumors. With the development of new treatment strategies and rapid advancement of radiation technology, it is crucial for radiation oncologists to maintain and refine their knowledge and skills. Dedicated exclusively to adult CNS radiation oncology, this textbook explores CNS tumors ranging from the common to the esoteric as well as secondary cancers of metastatic origin. The first half of the book is organized anatomically: tumors of the brain, spinal cord, leptomeninges, optic pathway, ocular choroid, and skull base. The second half covers primary CNS lymphoma, rare CNS tumors, metastatic brain disease, vascular conditions of the CNS, radiation-associated complications, and radiation modalities. This new edition is updated throughout and includes several new chapters, including: palliative radiation therapy for leptomeningeal disease, preoperative treatment for brain metastases, advanced neuroimaging for brain tumors, and MR-LINAC for brain tumors. Each chapter provides guidance on treatment field design, target delineation, and normal critical structure tolerance constraints in the context of the disease being treated. Learning objectives, case studies, and Self-Assessment questions and answers are incorporated throughout the book. This is an ideal guide for radiation oncologists, residents, and fellows, and medical students may also find value in the text

• 

  Intensity-Modulated Radiation Therapy and Volumetric Modulated Arc Therapy for Lung Cancer

  Jacob S. Parzen and Inga S. Grills

  Publication Date: 2023

  Per 2021 American Cancer Society estimates, lung cancer has the second highest incidence and highest mortality of all malignancies in the United States. Outcomes have improved considerably over the past decade, with radiation therapy (RT) serving as a cornerstone of locoregional therapy. The technical challenges of delivering biologically effective doses of RT capable of achieving local control include accurate target definition and accounting for respiratory tumor motion, tissue heterogeneities, and normal tissue tolerance. Three-dimensional conformal radiation therapy (3D CRT) is now the minimum technical standard for treating NSCLC. Intensity-modulated radiation therapy (IMRT) and volumetrically modulated radiation therapy (VMAT), in addition to four-dimensional (4D) CT simulation and planning techniques, biological targeting via positron emission tomography (PET), and 2D and 3D image-guided delivery methods, have facilitated radiation dose escalation while respecting normal tissue tolerance of organs at risk (OAR). In patients with locally advanced disease, IMRT has demonstrated improved dosimetric and toxicity profiles when compared to 3D CRT. However, this comes at the cost of long treatment times and high integral dose. With improvements in commercial planning software and quality assurance measures, VMAT is now commonly employed to achieve the improved conformality found with IMRT along with shorter treatment times and fewer monitor units delivered. Though no randomized trials comparing 3D CRT to IMRT/VMAT have been performed, these advanced modalities should be strongly considered in patients with locally advanced disease.

• 

  Deep Learning for Medical Image Segmentation

  Yading Yuan, Ronald Levitin, Zaid Siddiqui, Richard Bakst, Michael Buckstein, and Evan Porter

  Publication Date: 12-2023

  Accurate and reliable automated segmentation plays a vital role in improving consistency, efficiency and quality of patient care in clinical radiation therapy process, while also enabling comprehensive quantitative image analysis for assessing treatment outcomes on a large scale. In recent years, deep learning-based methods, which seamlessly integrate information ranging from global semantic context to intricate details within a unified end-to-end framework, have demonstrated substantially superior performance than traditional algorithms in numerous tasks involving tumor and/or organ segmentation. In this chapter, we firstly present the rationale of using deep learning for medical image segmentation, then we discuss several practical considerations when developing a deep learning model for a particular segmentation task, including image pre-processing, image patch selection, data augmentation, model fusion and output uncertainty assessment. Finally, we express our perspectives on the significance of international image analysis competitions in introducing innovative ideas and models, as well as in educating emerging researchers in the field of auto-segmentation in radiotherapy.

• 

  Advanced Particle Therapy Delivery

  Peyman Kabolizadeh, Xuanfeng Ding, and Xiaoqiang Li

  Publication Date: 5-2022

  Topics covered include:

  • Background information related to particle therapy, including the clinically relevant physics, radiobiological, and practical aspects of developing a particle therapy program
  • “Niche” treatments, such as FLASH, BNCT, and GRID therapy
  • The simulation process, target volume delineation, and unique treatment planning considerations for each disease site
  • Less commonly used ions, such as fast neutrons or helium

• 

  Trigeminal Neuralgia

  Peter Y. Chen

  Publication Date: 7-28-2018

  Trigeminal neuralgia (TN) is a pain syndrome typified by paroxysms of intense stabbing, electric shock-like unilateral facial pain provoked by light touch in the distribution of the fifth cranial or trigeminal nerve. Also known as tic douloureux to describe the involuntary facial movement or contortions of the facial features in reaction to the severity/intensity of the pain, the cause is usually idiopathic, but secondary causes such as vascular anomalies, tumors, or multiple sclerosis may be identified by neuroimaging. Antiepileptic drug therapy provides initial pain relief in the majority of patients. However, the TN pain may become refractory to pharmacological agents leading to referrals for evaluation of surgical or irradiation management ranging from operative craniotomy to radiation-based radiosurgical approaches. This chapter will highlight the key features of TN and elucidate state-of-the-art medicinal, surgical and radiotherapeutic regimens in the treatment of TN based on retrospective, observational clinical investigations along with systemic and comparative reviews.

• 

  Neuroradiology: Spectrum and Evolution of Disease

  Daniel L. Noujaim

  Publication Date: 10-29-2018

  Kummel disease was first described by Hermann Kummell in 1891 as delayed posttraumatic vertebral collapse occurring weeks or months after an often minimal injury. The mechanism is still debated. These lesions are most widely believed to be secondary to delayed osteonecrosis of the vertebral body, potentially related to vascular disruption of the anterior watershed territory of the vertebral body following trauma. It is a rarely reported entity, which likely occurs more frequently than recognized.

• 

  Neuroradiology: Spectrum and Evolution of Disease

  Juan Small, Daniel Noujaim, Daniel Thomas Ginat, Hillary R. Kelly, and Pamela W. Schaefer

  Publication Date: 10-29-2018

  Acquire a better understanding of disease evolution and treatment response with Neuroradiology Spectrum and Evolution of Disease. The unique format includes carefully chosen clinical images that depict the pathologic evolution of disease from initial presentation across the continuum of progression. Colorful graphics plot characteristic changes, helping you visualize how normal and abnormal variations alter over time. Extensive illustrations and concise descriptions distill complex concepts, making this first-of-its-kind resource an excellent tool for imaging interpretation and clinical problem solving.