Progenics Pharmaceuticals is rolling out a number of machine learning applications for medical imaging analysis as part of a new program created in collaboration with the Veteran Affairs Greater Los Angeles Healthcare System (VAGLAHS).

The program will utilize the tools to extract information from medical images that can be applied toward the improvement of treatment management for veterans with prostate cancer.

"Progress in prostate cancer treatment has been limited because the current clinical imaging assessment provides results that are only semi-quantitative and subjectively determined," Aseem Anand, vice president of digital technology at Progenics, told HCB News. "Remarkable recent advances in artificial intelligence (AI) algorithms for image recognition provide a fully quantitative imaging assessment of tumor burden rapidly, automatically, and reliably. The objective of the collaboration is to clinically evaluate and validate AI algorithms generating quantitative assessment of medical images to determine treatment response in veterans with metastatic prostate cancer."
Among the applications offered are Progenics’ automated Bone Scan Index (aBSI), which offers full quantitative assessments of bone scans that incorporate inferred masses of all lesions and reflects the proportion of total skeleton mass observed to have tumor involvement. The solution was found to be an independent prognostic determinant of overall survival in a multi-institutional phase-three study. The results supported using aBSI in the design and eligibility of clinical trials for systemic therapies for metastatic castration-resistant prostate cancer.

Another is the PSMA-AI technology, the first software tailored to Progenics' PCFPyL, a clinical-stage, fluorinated PSMA-targeted PET imaging agent for prostate cancer. The solution provides automatic and quantitative computations of evaluations used to detect, localize, quantify and stage both localized and advanced prostate cancer lesions. Powering it are deep learning and convolutional neural networks for determining detailed anatomical context from anatomical images. Pairing this information with corresponding PSMA-targeted functional images, users can assess the prevalence and growth of cancer. The information retrieved with the solution has been statically validated to improve hybrid image assessments on prostate cancer patients by humans.

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