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Artificial Intelligence Speeds NHL Diagnosis and Prognosis

Artificial Intelligence Speeds NHL Diagnosis and Prognosis
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Published on May 19, 2020

The field of artificial intelligence (AI), sometimes called deep learning, is exploding in many areas of cancer diagnostics and offering new hope to patients with a specific subtype of non-Hodgkin lymphoma (NHL).
 
Diffuse large B-cell lymphoma (DLBCL) is one of the most common subtypes of NHL and a focus of many ongoing studies across the globe. Scientists are exploring new ways to diagnose, treat and lessen toxicities using AI. But how?
 
AI is the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. This emerging field is full of possibilities.

Precision Diagnosis Leads to Better Prognosis

A study published in the journal Blood shows that certain gene expression profiling coupled with AI can increase precision diagnosis in cancers such as DLBCL.1 It doesn’t automate diagnosis, but it speeds it along, which is very important for patients who need to know what the next steps are in the process.
 
Also, a recent study identified a set of genes with prognostic importance in patients with DLBCL. Using AI, researchers identified 25 gene biomarkers—which measure the presence or severity of a disease—that were linked to different outcomes.2 
 
Having this information can help your medical team know how well you will fare on a standard of care chemotherapy cocktail such as R-CHOP, or an immunotherapy regimen such as CAR T-cell therapy.

Keeping the Human Touch in Medicine

The diagnosis and classification of blood cancers can be challenging, and the need for hematologists and pathologists is still very important; so are second opinions. The human, hands-on experience is not being replaced by machines, only improved on.
 
“For all pathology AI development, a pathologist’s diagnosis is the gold standard, underscoring the importance of these professionals. As the understanding of pathobiology expands, pathologists develop and apply new classification schemes that require the training of new computational algorithms,” wrote Dr. Ian Brain and Dr. Annette S. Kim, of Brigham and Women's Hospital, Harvard Medical School.3

Ask Questions

During your next routine visit, ask members of your team, "Are there any tests or analyses that can be used to help inform which treatment might be most likely to work in my situation?" Ask what your genetic biomarkers are, too, and what that means for your cancer journey. Knowledge is a powerful tool for patients.
 
Looking for more information on NHL, navigating cancer or living well with cancer? Be sure to sign up for Patient Power e-newsletters

~Lauren Evoy Davis


Please remember the opinions expressed on Patient Power are not necessarily the views of our sponsors, contributors, partners or Patient Power. Our discussions are not a substitute for seeking medical advice or care from your own doctor. That’s how you’ll get care that’s most appropriate for you.

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References

  1. Carreras J, et al. Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma. Tokai J Exp Clin Med. 2020 Apr 20;45(1):37-48.
  2. Bobee V, et al. Combining Gene Expression Profiling and Artificial Intelligence to Diagnose B-Cell Non-Hodgkin Lymphoma. Blood (2019) 134 (Supplement_1): 1476.
  3. Brain I, Kim A, et al. Using Artificial Intelligence to Enhance Diagnosis: Is Resistance Futile? The Hematologist. May-June 2020, Volume 17, Issue 3.

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