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S6 Ep5: Predicting Palbociclib Outcomes in Breast Cancer Using Deep Learning

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Manage episode 477222220 series 3293365
Content provided by Targeted Talks. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Targeted Talks or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode of Emerging Experts, Xiaojie Zhang, MD, a hematology/oncology fellow, and Akshat Singhal, PhD, a postdoctoral scholar, both at UC San Diego, shed light on innovative research leveraging deep learning to predict how patients with ER-positive/HER2-negative (ER+/HER2-) breast cancer will respond to palbociclib (Ibrance), a common first-line treatment for this patient population. Their work, fueled by the desire to improve precision oncology, demonstrates the significant potential of artificial intelligence (AI) in guiding cancer care.
  continue reading

71 episodes

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Manage episode 477222220 series 3293365
Content provided by Targeted Talks. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Targeted Talks or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
In this episode of Emerging Experts, Xiaojie Zhang, MD, a hematology/oncology fellow, and Akshat Singhal, PhD, a postdoctoral scholar, both at UC San Diego, shed light on innovative research leveraging deep learning to predict how patients with ER-positive/HER2-negative (ER+/HER2-) breast cancer will respond to palbociclib (Ibrance), a common first-line treatment for this patient population. Their work, fueled by the desire to improve precision oncology, demonstrates the significant potential of artificial intelligence (AI) in guiding cancer care.
  continue reading

71 episodes

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