AI and Brain Cancer Tumors: A Recap of Three Studies in the Journal of Neuro-Oncology That Caught Our Attention
Navigating academic articles about brain cancer tumors can be confusing, thanks to complicated medical vocabulary.
From machine learning developments to predicting access to post-op treatment, you don't have to be a neurosurgeon to understand our recap. Check out three clinical studies and reviews from the "Journal of Neuro-Oncology" that we found interesting, with links to the full articles.
Predicting access to postoperative treatment after glioblastoma resection
A clinical study published in May 2022 in the “Journal of Neuro-Oncology” looked to compare outcomes and access to treatment for glioblastoma using the Area Deprivation Index (ADI), a composite of 17 socioeconomic and environmental factors. The ADI had previously been linked to poorer outcomes for patients. Thus, through a 5-year retrospective study of Rhode Island Hospital and Mayo Clinic databases for glioblastoma patients over 18 years old, researchers found that patients with increased social disadvantages were less likely to receive gross-total resection and standard-of-care treatment for this deadly brain cancer tumor.
Click here to read the complete study.
Machine learning in neuro-oncology: toward novel development fields
Researchers throughout Italy contributed to a review of available studies adopting AI in different fields of neuro-oncology to assess the types of applications for this technology when dealing with a brain cancer tumor.
They found that most studies assessing AI were in neuro-radiology and are also being tested in surgery and radiation therapy. Overall, it's likely that AI will be quickly included in some elements of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
Click here to read the complete study.
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Stimulated Raman histology facilitates accurate diagnosis in neurosurgical patients
First of all, what is Stimulated Raman histology (SRH)?
SRH is an innovative strategy offering intraoperative near real-time histopathological analysis. It makes studying a diagnostic specimen during a surgical operation faster and more accurate.
Due to the fact that there are not a lot of one-to-one tissue comparisons between SRH and traditional frozen sectioning (the process where there is rapid tissue section cooled with a cryostat to provide an immediate report of the tissue), neurosurgeons and pathologists at Lenox Hill Hospital decided to explore this comparison on the same piece of tissue in neurosurgical patients.
Their research provided further evidence for the non-inferiority of SRH techniques. It is also the first study to demonstrate SRH accuracy using one-to-one tissue analysis in neuropathological specimens.
Click here to read the complete research study.
Sourced from and in partnership with the Journal of Neuro-Oncology.
Browse more blog posts on our website, including "Neurosurgeons Address the Unmet Challenges of Glioblastoma" along with "How to Spot the Signs of a Deadly Brain Cancer Tumor."
Looking to support our glioblastoma research funding mission? Click here to donate and learn more about our current fully-funded projects at leading cancer centers in the U.S.
*The Glioblastoma Research Organization's informational material is not intended to be a substitute for medical professional advice or treatment for any specific health concerns, as the organization is neither a provider of medical care nor does research itself. You should not use this information to diagnose or treat a health problem without consulting a qualified healthcare provider.