Integrative Machine Learning Approaches for Predicting Cancer Outcomes: A Multimodal Data Perspective

Abstract: In the era of precision medicine, the integration of diverse data modalities holds significant promise for improving the prognostication and personalization of cancer treatment. My research focuses on developing machine learning algorithms that amalgamate clinical data, tissue-based metrics, histopathological images, and radiological images to enhance outcome predictions in prostate and lung cancer. This talk will provide an overview of the approaches developed by our team to extract and integrate radiomic and histopathologic features aimed at predicting treatment responses and disease progression. Specifically, I will discuss recent advances in identifying predictive biomarkers for non-small cell lung cancer (NSCLC) and prostate cancer, showcasing how integrated data strategies can lead to more informed clinical decisions and better patient outcomes. Attendees will gain insights into the critical role of machine learning in the convergence of various imaging modalities, and how these integrative approaches can pave the way for more precise and effective cancer therapies.

Integrative Machine Learning Approaches for Predicting Cancer Outcomes: A Multimodal Data Perspective

Abstract: In the era of precision medicine, the integration of diverse data modalities holds significant promise for improving the prognostication and personalization of cancer treatment. My research focuses on developing machine learning algorithms that amalgamate clinical data, tissue-based metrics, histopathological images, and radiological images to enhance outcome predictions in prostate and lung cancer. This talk will provide an overview of the approaches developed by our team to extract and integrate radiomic and histopathologic features aimed at predicting treatment responses and disease progression. Specifically, I will discuss recent advances in identifying predictive biomarkers for non-small cell lung cancer (NSCLC) and prostate cancer, showcasing how integrated data strategies can lead to more informed clinical decisions and better patient outcomes. Attendees will gain insights into the critical role of machine learning in the convergence of various imaging modalities, and how these integrative approaches can pave the way for more precise and effective cancer therapies.