Revolutionizing Cancer Treatment with Digital Pathology Predictions
Personalized medicine relies on accurate and clinically relevant diagnostic tests for effective disease treatment. Traditional genetic marker tests can be costly and time-consuming, often requiring sample shipments to labs for analysis. However, a recent study by researchers at the University of California, San Diego highlights the potential of deep learning platforms in predicting treatment response for breast and ovarian cancers.
The study, published in the Journal of Clinical Oncology, revealed that the AI model DeepHRD, trained on histopathology slides, outperformed FDA-approved companion diagnostics in identifying patients likely to respond to specific therapies. This technology, now licensed to io9, offers a faster, more cost-effective, and accurate alternative to standard genomic sequencing for identifying treatment options for patients with metastatic breast cancer and high-grade serous ovarian cancers.
As CEO Greg Hamilton suggests, the future of solid tumor trials may involve incorporating digital pathology predictions into standard practice. This advancement could streamline the diagnostic process, potentially leading to more effective and targeted cancer treatments. With ongoing research exploring the impact of image and scanner quality on test results, the potential for using smartphones as image scanners in the future is an intriguing possibility.
In conclusion, embracing digital pathology predictions in cancer treatment could revolutionize how we identify and tailor therapies for patients, offering hope for more precise and efficient care.
We have a technology that can predict HRD from digital slides, and when you compare the response to treatment in breast and ovarian cancer, we can show that we capture a larger population group, and they respond better than if you were to use the molecular test for this patient. Just as sequencing has evolved, where we sequence every patient because we want that information for future target identification, we will also use digital pathology for every solid tumor trial in the future because we will want this information for future development as well. It’s going to become a standard.
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Greg Hamilton, CEO of io9.
