Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Integrating ergonomics into surgical education to enhance surgeon wellbeing and career longevity.

JPMA. The Journal of the Pakistan Medical Association·2026
Same author

Trends of Acute Pericarditis-Related Mortality in the United States from 1999 to 2023: An Observational Analysis.

Avicenna journal of medicine·2026
Same author

Tumor-specific outcomes in spinal metastases: a systematic review and meta-analysis.

Journal of spine surgery (Hong Kong)·2026
Same author

Comparative risk of amyloid-related imaging abnormalities with anti-amyloid-β monoclonal antibodies: A systematic review and penalized likelihood network meta-analysis of randomized trials.

Journal of Alzheimer's disease : JAD·2026
Same author

Genetically Confirmed Osteogenesis Imperfecta (COL1A1) With Unexplained Ambiguous Genitalia in a 46,XY Child: An Index Case Report.

Clinical case reports·2026
Same author

Patterns of Muscle Health in Single- and Multi-Site Chronic Pain: A UK Biobank Normative Modeling Study.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Jun 14, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative

Vivek Sanker1, Suhrud Panchawgh2, Anmol Kaur3

  • 1Department of Neurosurgery, Stanford University, Palo Alto, CA 94304, USA.

Journal of Imaging
|March 27, 2026
PubMed
Summary

Radiomics and machine learning (ML) show promise for diagnosing and managing spinal tumors. This review summarizes current advances and offers a workflow for researchers, focusing on enhancing patient outcomes.

Keywords:
artificial intelligencedeep learningmachine learningradiomicsspinal cord tumors

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

853

Related Experiment Videos

Last Updated: Jun 14, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

853

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • Radiomics, the extraction of quantitative features from medical images, combined with machine learning (ML), offers novel approaches to disease characterization.
  • Spinal tumors represent a complex area where advanced computational analysis can potentially improve diagnostic accuracy and treatment planning.
  • Existing literature highlights the emerging role of AI and radiomics in oncology, but a focused review on spinal tumor applications is needed.

Purpose of the Study:

  • To provide a comprehensive overview of current advancements in radiomics and artificial intelligence (AI) for spinal tumors.
  • To detail the applications of radiomics and AI in the diagnosis and management of spinal tumors.
  • To propose a practical workflow for radiomics and ML analysis in spinal tumor research.

Main Methods:

  • Narrative review of existing literature on radiomics, AI, and spinal tumors.
  • Identification and description of commonly used radiomic features.
  • Development of a suggested workflow for radiomics and ML analysis.

Main Results:

  • Summary of current radiomics and AI applications in spinal tumor diagnosis and management.
  • Compilation of frequently utilized radiomic features relevant to spinal tumors.
  • Outline of a structured approach for researchers entering the field.

Conclusions:

  • Radiomics and ML hold significant potential for advancing the diagnosis, prognosis, and management of spinal tumors.
  • Further validation of algorithms with larger datasets and adherence to ethical standards are crucial.
  • Continued development of novel computational techniques is essential for improving patient outcomes in spinal oncology.