A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, Ph.D. Ultrasound exams use high-frequency sound waves passing through tissue to construct real-time images of breast or other tissues.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online today (September 24, 2021) in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.

An AI neural network can accurately predict the prognosis of melanoma patients based on pre-treatment histology imaging data, shows research led by the NYU Grossman School of Medicine. Immune checkpoint inhibitors have revolutionized melanoma treatment, but only some tumors respond well to them and they can be quite toxic to patients. Having a more reliable way to predict who is most likely to respond to these therapies is therefore crucial. "An unmet need is the ability to accurately predict which tumors will respond to which therapy," says Iman Osman, M.D., a medical oncologist based at New York University (NYU) Grossman School of Medicine and NYU Langone's Perlmutter Cancer Center, who co-led the work. "This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity." In collaboration with Aristotelis Tsirigos, Ph.D., professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langone's Perlmutter Cancer Center, Osman and team first trained an artificial neural network using pre-treatment histology images from 121 patients with metastatic melanoma.

Experts trained a computer to tell which patients with skin cancer may benefit from drugs that keep tumors from shutting down the immune system's attack on them, a new study finds. Led by researchers from NYU Grossman School of Medicine and Perlmutter Cancer Center, the study showed that an artificial intelligence (AI) tool can predict which patients with a specific type of skin cancer would respond well to such immunotherapies in four out of five cases. Specifically, the study examined patients with metastatic melanoma, skin cancer that has the capacity to spread to other organs and kills 6,800 Americans each year. The results are important, say the study investigators, because while the drug class studied, immune checkpoint inhibitors, has been more effective for many patients than traditional chemotherapies, half of patients do not respond to them. Adding to the urgency of efforts to determine which patients will respond, researchers say the drugs may cause side effects in many of them and are also expensive.