Case study: using AI-based image analysis to predict ovarian tumor outcome

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, mainly found in ovaries. These tumors have significant genetic and morphologic heterogeneity and are notable for their diversity and unpredictability in outcome. Treatment methods and research for these tumors have developed very little in the last few decades. Standard therapy includes debulking surgery and platinum-based chemotherapy, while overall 5-year survival rates remain below 50%.

The development of artificial intelligence (AI)-based image analysis has improved the prediction and identification of underlying molecular alterations in other tumor types, such as colorectal, lung, and breast carcinomas. These AI models have been shown to perform as well as expert pathologists and offer increased precision in bias-prone tasks such as metastasis detection. This suggests a promising new treatment model for HGSC tumors as well.

There is an intrinsic difference in HGSC tumors that are platinum-based, chemotherapy-sensitive, and resistant. However, this variation is currently not prospectively identifiable by pathologists. The authors of the publication "Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone" have proposed that some indication of this underlying difference is detectable in the tumor morphology with the use of AI to differentiate these two groups of patients.

“We used AI for this project because this tumor type (high-grade serous carcinoma of the ovary) is a little unusual. When pathologists look at these slides, unlike for nearly every other carcinoma, we provide very little or no information beyond the diagnosis itself. At the same time, there are many different morphologies to look at! We hypothesized that AI might be able to identify something the human eye has not yet recognized.” – Anna Laury, MD, Clinical researcher at the University of Helsinki

Using a patient cohort reflecting the extremes of treatment response, the researchers developed an AI model with Aiforia® Create to differentiate between good-outcome and poor-outcome tumors in whole slide images. AI has the potential to accurately classify high-grade serous carcinoma into outcome groups using tumor morphology alone, with significant benefits to treatment methods and patient survivability predictions.

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