Leveraging the power of AI and machine learning technologies, researchers co-lead by Dr. Bishoy Morris Faltas, the Gellert Family–John P. Leonard MD Research Scholar in Hematology and Medical Oncology in the division of Hematology & Medical Oncology, has developed a more effective model for predicting how patients with muscle-invasive bladder cancer will respond to chemotherapy. The model harnesses whole-slide tumor imaging and gene expression analyses in a way that outperforms previous models using a single data type.
The study, published March 22 in npj Digital Medicine, identifies key genes and tumor characteristics that may determine treatment success. The ability to accurately anticipate how an individual will react to the standard-of-care therapy for this malignant cancer may help doctors personalize treatment and could potentially save those who respond well from undergoing bladder removal.
“We want to identify the right treatment for the right patient at the right time,” said Dr. Faltas.
Dr. Zilong Bai, research associate in population health sciences, and Dr. Mohamed Osman, postdoctoral associate in medicine at Weill Cornell Medicine, collaboratively spearheaded this work.
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