November 18, 2020 -A machine learning algorithm accurately determined how well skin cancer patients would respond to tumor-suppressing drugs in four out of five cases, according to research conducted by a team from NYU Grossman School of Medicine and Perlmutter Cancer Center.
The study focused on metastatic melanoma, a disease that kills nearly 6,800 Americans each year. Immune checkpoint inhibitors, which keep tumors from shutting down the immune systems attack on them, have been shown to be more effective than traditional chemotherapies for many patients with melanoma.
However, half of patients dont respond to these immunotherapies, and these drugs are expensive and often cause side effects in patients.
While immune checkpoint inhibitors have profoundly changed the treatment landscape in melanoma, many tumors do not respond to treatment, and many patients experience treatment-related toxicity, said corresponding study authorIman Osman, medical oncologist in the Departments of Dermatology and Medicine (Oncology) at New York University (NYU) Grossman School of Medicine and director of the Interdisciplinary Melanoma Program at NYU Langones Perlmutter Cancer Center.
An unmet need is the ability to accurately predict which tumors will respond to which therapy. This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity.
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Researchers set out to develop a machine learning model that could help predict a melanoma patients response to immune checkpoint inhibitors. The team collected 302 images of tumor tissue samples from 121 men and women treated for metastatic melanoma with immune checkpoint inhibitors at NYU Langone hospitals.
They then divided these slides into 1.2 million portions of pixels, the small bits of data that make up images. These were fed into the machine learning algorithm along with other factors, such as the severity of the disease, which kind of immunotherapy regimen was used, and whether a patient responded to the treatment.
The results showed that the machine learning model achieved an AUC of 0.8 in both the training and validation cohorts, and was able to predict which patients with a specific type of skin cancer would respond well to immunotherapies in four out of five cases.
Our findings reveal that artificial intelligence is a quick and easy method of predicting how well a melanoma patient will respond to immunotherapy, said study first author Paul Johannet, MD, a postdoctoral fellow at NYU Langone Health and its Perlmutter Cancer Center.
Researchers repeated this process with 40 slides from 30 similar patients at Vanderbilt University to determine whether the results would be similar at a different hospital system that used different equipment and sampling techniques.
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A key advantage of our artificial intelligence program over other approaches such as genetic or blood analysis is that it does not require any special equipment, said study co-author Aristotelis Tsirigos, PhD, director of applied bioinformatics laboratories and clinical informatics at the Molecular Pathology Lab at NYU Langone.
The team noted that aside from the computer needed to run the program, all materials and information used in the Perlmutter technique are a standard part of cancer management that most, if not all, clinics use.
Even the smallest cancer center could potentially send the data off to a lab with this program for swift analysis, said Osman.
The machine learning method used in the study is also more streamlined than current predictive tools, such as analyzing stool samples or genetic information, which promises to reduce treatment costs and speed up patient wait times.
Several recent attempts to predict immunotherapy responses do so with robust accuracy but use technologies, such as RNA sequencing, that are not readily generalizable to the clinical setting, said corresponding study authorAristotelis Tsirigos, PhD, professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langones Perlmutter Cancer Center.
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Our approach shows that responses can be predicted using standard-of-care clinical information such as pre-treatment histology images and other clinical variables.
However, researchers also noted that the algorithm is not yet ready for clinical use until they can boost the accuracy from 80 percent to 90 percent and test the algorithm at more institutions. The research team plans to collect more data to improve the performance of the model.
Even at its current level of accuracy, the model could be used as a screening method to determine which patients across populations would benefit from more in-depth tests before treatment.
There is potential for using computer algorithms to analyze histology images and predict treatment response, but more work needs to be done using larger training and testing datasets, along with additional validation parameters, in order to determine whether an algorithm can be developed that achieves clinical-grade performance and is broadly generalizable, said Tsirigos.
There is data to suggest that thousands of images might be needed to train models that achieve clinical-grade performance.
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