Literature DB >> 34623944

Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases.

Sujana Gottumukkala1, Udayan Srivastava1, Samantha Brocklehurst1, J Travis Mendel1, Kiran Kumar1, Fang F Yu1, Amit Agarwal1, Bhavya R Shah1, Shaleen Vira1, Karuna M Raj1.   

Abstract

The fields of both radiology and radiation oncology have evolved considerably in the past few decades, resulting in an increased ability to delineate between tumor and normal tissue to precisely target and treat vertebral metastases with radiation therapy. These scientific advances have also led to improvements in assessing treatment response and diagnosing toxic effects related to radiation treatment. However, despite technological innovations yielding greatly improved rates of palliative relief and local control of osseous spinal metastases, radiation therapy can still lead to a number of acute and delayed posttreatment complications. Treatment-related adverse effects may include pain flare, esophageal toxic effects, dermatitis, vertebral compression fracture, radiation myelopathy, and myositis, among others. The authors provide an overview of the multidisciplinary approach to the treatment of spinal metastases, indications for surgical management versus radiation therapy, various radiation technologies and techniques (along with their applications for spinal metastases), and current principles of treatment planning for conventional and stereotactic radiation treatment. Different radiologic criteria for assessment of treatment response, recent advances in radiologic imaging, and both common and rare complications related to spinal irradiation are also discussed, along with the imaging characteristics of various adverse effects. Familiarity with these topics will not only assist the diagnostic radiologist in assessing treatment response and diagnosing treatment-related complications but will also allow more effective collaboration between diagnostic radiologists and radiation oncologists to guide management decisions and ensure high-quality patient care. ©RSNA, 2021.

Entities:  

Mesh:

Year:  2021        PMID: 34623944     DOI: 10.1148/rg.2021210052

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  2 in total

1.  Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.

Authors:  James Thomas Patrick Decourcy Hallinan; Lei Zhu; Wenqiao Zhang; Desmond Shi Wei Lim; Sangeetha Baskar; Xi Zhen Low; Kuan Yuen Yeong; Ee Chin Teo; Nesaretnam Barr Kumarakulasinghe; Qai Ven Yap; Yiong Huak Chan; Shuxun Lin; Jiong Hao Tan; Naresh Kumar; Balamurugan A Vellayappan; Beng Chin Ooi; Swee Tian Quek; Andrew Makmur
Journal:  Front Oncol       Date:  2022-05-04       Impact factor: 5.738

2.  Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.

Authors:  James Thomas Patrick Decourcy Hallinan; Lei Zhu; Wenqiao Zhang; Tricia Kuah; Desmond Shi Wei Lim; Xi Zhen Low; Amanda J L Cheng; Sterling Ellis Eide; Han Yang Ong; Faimee Erwan Muhamat Nor; Ahmed Mohamed Alsooreti; Mona I AlMuhaish; Kuan Yuen Yeong; Ee Chin Teo; Nesaretnam Barr Kumarakulasinghe; Qai Ven Yap; Yiong Huak Chan; Shuxun Lin; Jiong Hao Tan; Naresh Kumar; Balamurugan A Vellayappan; Beng Chin Ooi; Swee Tian Quek; Andrew Makmur
Journal:  Cancers (Basel)       Date:  2022-06-30       Impact factor: 6.575

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.