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AI imaging
August 2018
by Kelsey Kaustinen  |  Email the author
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NEW YORK—The Society of Nuclear Medicine and Molecular Imaging Annual Meeting is a gathering for molecular imaging scientists, physicians, radiologists and more to share advancements in the field of imaging technology, and among the presenters this year was Progenics Pharmaceuticals Inc. The company shared in an oral presentation data supporting its imaging analysis technology, which applies artificial intelligence and machine learning to quantify and automate the reading of prostate-specific membrane antigen (PSMA)-targeted imaging.
 
Specifically, the data validated an algorithm developed by Progenics’ imaging analysis technology for automatic detection and quantification of 1404 uptake from single-photon emission computerized tomography (SPECT) or positron emission tomography (PET) images.
 
“1404 targets the extracellular domain of PSMA, which is over-expressed in aggressive prostate cancer cells. Therefore, 1404 is one of the few, if not the only, imaging modalities that is based on the tumor biology of prostate cancer,” says a Progenics spokesperson, adding that the company has a Phase 3 study underway assessing 1404 SPECT/CT as a diagnostic vs. histopathology in patients with intermediate or low-risk prostate cancer who are eligible for radical prostatectomy or active surveillance.
 
The data shared at the recent conference came from a Phase 2 study of 1404 in 102 high-risk prostate cancer patients who had PSMA imaging performed before a radical prostatectomy. According to a Progenics press release, “The validation scans were manually quantified by measuring the maximum uptake of 1404 in a circular region of interest of the prostate where the highest uptake values were determined visually. The algorithm used volumetric segmentation to measure uptake at every voxel in the prostate and determined the maximum uptake of 1404 automatically. The Pearson correlation coefficient was used to assess the concordance between manual and automated quantification of uptake. The automated maximum uptake value was significantly correlated to the manually obtained uptake value (p<0.0001). The algorithm was fully automated and deterministic, resulting in 100 percent repeatability.”
 
“This study successfully validates Progenics’ imaging analysis technology platform for use with PSMA-targeted SPECT/CT, and shows the promise of using artificial intelligence to automate the reading and interpretation of prostate cancer scans,” said Dr. Lars Edenbrandt, professor and senior specialist in the Department of Molecular and Clinical Medicine at the University of Gothenburg. “PSMA-targeted imaging, together with sophisticated algorithms and machine learning, have the potential to significantly improve how clinicians stage prostate cancer, monitor disease progression and manage treatment, which could potentially lead to better patient outcomes.”
 
SPECT and PET scans are nuclear imaging tests that use radioactive agents to provide internal imaging of organs. As Progenics notes on its website, the company “is developing radiolabeled small molecules that bind to specific receptors, enzymes and proteins in the body that are altered during the evolution of disease. After administration to a patient, these molecules circulate in the blood until they find their intended target. The bound radiopharmaceutical remains at the site of disease, while the rest of the agent clears rapidly from the body.”
 
As the website further explains, the radioactive portion of the molecule serves as a kind of “beacon” that allows an image to be obtained that depicts the disease location and concentration, using relatively common nuclear medicine cameras of the SPECT of PET variety.
 
“Image analysis is one of the success cases of the revolution in machine learning and AI that we have witnessed over the last decade. Medical image analysis is an exciting application of deep learning and is supported by a growing body of research,” the Progenics spokesperson tells DDNews. “Our research is among the first to utilize AI to analyze SPECT/CT images in prostate cancer. Our approach is novel in that it extracts quantitative assessments useful for both the detection and staging of prostate cancer by applying AI to the full 3D SPECT/CT volumes. The anatomy of the prostate is variable and the delineation of the prostate in 3D from low-dose CT images—a prerequisite for obtaining quantitative SPECT assessments—can be challenging. We believe that automating this procedure using AI reduces variability and the risk of oversight.”
 
“Healthcare is becoming more quantitative, a prerequisite for accurately following patients’ treatment over time and across physicians and caregivers,” the spokesperson continues. “AI is an immensely powerful tool in building systems for quantitation, since these are able to extract structure and meaning from the large amounts of data produced at modern healthcare facilities. AI-enabled and -assisted healthcare has obvious benefits in terms of faster and more accurate diagnoses and better-targeted therapies. It can also make it possible to provide specialized care in geographic regions where specialists are hard to find.”
 
Code: E081820

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