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AI can reliably predict the efficacy of immune checkpoint inhibitor therapy

AI can reliably predict the efficacy of immune checkpoint inhibitor therapy

 


HealthDay News — Machine learning system SCORPIO can predict patient outcomes with immune checkpoint inhibitor (ICI) therapy across a variety of cancer types and healthcare settings, according to research published online on January 6. Announced. natural medicine.

Dr. Seong-Keun Yoo and his colleagues at the Icahn School of Medicine at Mount Sinai in New York City developed SCORPIO using routine blood tests and clinical characteristics of 9,745 patients treated with ICIs across 21 cancer types. Evaluated. SCORPIO was trained on data from 1,628 patients across 17 cancer types and tested on an internal test set consisting of 2,511 patients across 19 cancer types. External validation included 10 global Phase 3 trials involving 4,447 patients across six cancer types and a Mount Sinai Health System implementation study involving 1,159 patients across 18 cancer types. It was conducted in a global cohort.

The researchers found that SCORPIO achieved the median time-dependent area under the receiver operating characteristic curve (AUC).

“Despite its limitations, SCORPIO remains a highly accessible model for predicting ICI efficacy and can be used in conjunction with other assessments to support clinical decision-making,” the authors wrote. is writing.

Several authors have filed provisional patent applications, three of whom are co-inventors on a patent filed for the use of TMB to predict immunotherapy response. Some authors also disclosed relationships with the pharmaceutical industry.

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