Will advances in AI lead to more effective screening practices for ovarian cancer?

Yes.

Screening techniques for ovarian cancer are nonexistent. Studies have looked at combinations of blood markers and ultrasounds, but they did not show a survival improvement. Ultimately, if a patient undergoes screening, it should lead to some improvement in outcome or prevention of the cancer altogether.

nakayama_john_80x106.jpg John M. Nakayama

AI can help us in this area because cancer results from a combination of genetic and environmental factors. We can try to account for more factors using AI, such as other genetic markers, epigenetic markers, changes in lipids, earlier CT or ultrasound findings that could be predictive. Putting those together would generate huge amounts of data. In fact, in a clinical commentary published in Gynecologic Oncology, McDonald and colleagues reported that the amount of genomic data alone doubles every 6 to 7 months and is predicted to exceed 40 exabytes a year within the next decade.

Given the overwhelming amount of complex data, the only way to get through it and make correlations among it all is with the help of machines. AI has generally worked in finding correlations among data.

Two studies explored this issue. One looked at the metabolome, meaning researchers used machine learning to look at different lab values and found they were able to predict ovarian cancer. The use of a support vector machine-based learning algorithm to identify 16 diagnostic metabolites detected early-stage ovarian cancer with 100% accuracy.

The other study screened for microRNAs and found just a handful of them were useful in predicting if a patient had ovarian cancer, borderline cancer or if it was benign. The researchers developed a micro-RNA algorithm for diagnosis that outperformed CA125 screening and appeared accurate regardless of patient age, histology or stage. The network also had 100% specificity for epithelial ovarian cancer when tested in a group of 454 patients with various diagnoses.

This work is preliminary and needs to be validated in a prospective manner, but these are the kinds of changes that have to happen in order for us to get effective screening for ovarian cancer. They have to be done algorithmically.

Deep learning can help with anything as long as it’s coded. The data are out there. It might be cost prohibitive to label it in some situations, but is it possible to find it and turn it into a screening method? The answer is yes.

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References:

Gaul DA, et al. Sci Rep. 2015;doi:10.1038/srep16351.

McDonald JF. Gynecol Oncol. 2018;doi:10.1016/j.ygyno.2018.03.053.

Elias KM, et al. Elife. 2017;doi:10.7554/eLife.28932.

John M. Nakayama, MD, is an obstetrician-gynecologist at University Hospital Cleveland Medical Center. He can be reached at john.nakayama@uhhospitals.org. Disclosure: Nakayama reports no relevant financial disclosures.

Will advances in AI lead to more effective screening practices for ovarian cancer?

Will advances in AI lead to more effective screening practices for ovarian cancer?

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