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DTSTAMP:20211207T055411Z
LOCATION:223
DTSTART;TZID=America/Chicago:20211114T161500
DTEND;TZID=America/Chicago:20211114T163000
UID:submissions.supercomputing.org_SC21_sess434_ws_cafcw108@linklings.com
SUMMARY:Image-Informed Mathematical Modeling to Predict Patient-Specific T
 reatment Response to Neoadjuvant Systemic Therapy in Triple Negative Breas
 t Cancer
DESCRIPTION:Workshop\n\nImage-Informed Mathematical Modeling to Predict Pa
 tient-Specific Treatment Response to Neoadjuvant Systemic Therapy in Tripl
 e Negative Breast Cancer\n\nWu\n\nPatients with locally advanced, triple-n
 egative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) t
 o downstage the tumor and for improved surgical outcomes. A critical, unme
 t need is a method to accurately predict an individual patient’s response 
 to NAT, thereby allowing for the opportunity to guide further intervention
 s. In this work, we construct and apply a clinical-computational framework
  that integrates quantitative magnetic resonance imaging with physics-base
 d, mathematical modeling to predict the response of TNBC early in the cour
 se of NAT. Preliminary results demonstrate the potential of the clinical-c
 omputational framework as a powerful tool for predicting response to NAT. 
 Ongoing efforts include applying the approach to the whole patient cohort 
 and performing the systematic model selection. Once validated, the approac
 h could also assist in optimizing treatment plans on a patient-specific ba
 sis or guiding patient selection in trials for novel NAT regimens.\n\nTag:
  Applications, Computational Science, Education and Training and Outreach,
  HPC Community Collaboration, HPC Training and Education, Machine Learning
  and Artificial Intelligence, Performance, Workforce\n\nRegistration Categ
 ory: Workshop Reg Pass
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