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LOCATION:Bldg. 6 - 104
DTSTART;TZID=Europe/Stockholm:20260701T103000
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UID:submissions.pasc-conference.org_PASC26_sess154_msa267@linklings.com
SUMMARY:How to Predict Immunotherapy Using AI in Clinical Practice in NSCL
 C: The I3LUNG and APOLLO 11 Examples
DESCRIPTION:Arsela Prelaj (National Cancer Institute (IRCCS))\n\nRecent in
 itiatives, including I3LUNG and APOLLO-11, have demonstrated the potential
  of AI-driven models to integrate multidimensional data from clinical reco
 rds, imaging, pathology, and molecular profiling to better predict treatme
 nt response and survival outcomes. These approaches use advanced machine l
 earning algorithms to identify complex patterns associated with sensitivit
 y or resistance to immune checkpoint inhibitors, enabling more precise pat
 ient stratification. Early results suggest that AI-based predictive models
  may outperform single biomarkers and support personalized treatment decis
 ions in routine clinical practice. However, beyond predictive performance,
  successful clinical adoption will depend on the usability, interpretabili
 ty, and seamless implementation of these tools within healthcare workflows
 . Prospective validation, regulatory compliance, clinician acceptance, and
  integration into decision-support systems represent critical next steps t
 o translate AI-driven prediction models into effective precision immuno-on
 cology solutions for patients with non-small cell lung cancer (NSCLC).\n\n
 Domain: Applied Social Sciences and Humanities, Life Sciences, Computation
 al Methods and Applied Mathematics\n\nSession Chairs: Justin M. Wozniak (A
 rgonne National Laboratory, University of Chicago) and Thomas Brettin (Arg
 onne National Laboratory, University of Chicago)\n\n
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