“I have been thinking about these vessels, a lot. I am excited to finally be able to see them in a tissue context.”

Reidunn Edelmann, an MD currently doing postdoctoral research at the University of Bergen at the Centre for Cancer Biomarkers (CCBIO) in Norway, is one of the first successful aiForward applicants. Driven by a passion for clinical research, Reidunn completed his PhD in cell biology after her medical studies and is currently focusing on studying biomarkers and vessel characteristics in cancer. She is interested in changing paradigms over how biomarkers and vessels are studied, from simple “very much just focused on one marker,” as she explains, “to thinking about the wider, more functional picture.”

Deepening cancer research

The vasculature, the arrangement of blood vessels in a tumor, is a significant component of analyzing tumor microenvironment which in turn is considered an important prognostic factor in cancer. “Earlier analyses of tumor vasculature in clinical samples has generally been of “low-content” nature,” Reidunn states. Her mission is to take the analysis of this important biology to the next level by deepening the characterization of these vessels and to ultimately correlate them to patient outcome-related end-points as well as other biological traits of tumors in different cancer types.

In order to fulfill these goals, Reidunn and her lab created a complex, multiplexed fluorescent staining that potentially can visualize distinct vessel phenotypes, based on activation of an essential fate-decisive pathway. However, the next step proved to be difficult, “The samples are so heterogenous, the patterns are so different from each other even for the same staining quality,” Reidunn explains “Current and traditional methods to analyze these were too simplistic, not suitable for this complex staining.” She had the samples, she had the staining, but she did not have the necessary tools to enable analysis and discovery.

aiForward enables analysis

Reidunn came across Aiforia at a conference, intrigued by the possibilities of applying deep learning AI to her research she contacted one of Aiforia’s scientists to describe her problems: “Manually it would definitely not be possible to work on my research, it takes too much time with these complex samples.” She was encouraged to apply to the aiForward program, as she explained: “My motivation is that I really, really want to analyze complex, high-content samples. Deep learning AI can learn the different patterns of what the activation of one pathway looks like.”

Her project with aiForward has started off with uploading the samples of her cohort, 235 colorectal cancer samples of patients to the Aiforia Cloud Platform — the Aiforia scientists were actually able to build a custom solution to support the upload of her unique sample set. When asked how she feels about the next steps, which involves training the AI model to recognize her features of interest, Reidunn excitedly answers: “I think it will be so easy, Aiforia (software) does all the hard work, I don’t need to think about any algorithms. All I need to do is see my slides and look for my feature and annotate it and then the program is going to do the rest. That’s the amazing thing. I myself am not so into digital, technical stuff. I am very much biology focused. So, thanks to Aiforia, I can just focus on what I want to focus on, the biology.”

Applications to the aiForward program are accepted on a continuous basis.

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