Oncology Data Advisor

The mission of Oncology Data Advisor is to provide the multidisciplinary team with expert insights into clinical best practices and research progress in cancer care.

Oncology Data Advisor is a science podcast hosted by Unknown Host, with 448 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Oncology Data Advisor, a science show with 448 episodes published.

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  Oncology Data Advisor

  Clinical Trial Enrollment Barriers: Awareness Isn’t the Same as Access

  Sep 3, 202622 min

  Check out the full article at: https://oncdata.com/oncology-unfiltered-clinical-trial-enrollment-barriers While clinical trials represent the cutting edge of modern cancer therapy, significant disparities persist in determining who actually enrolls in them. In this episode of Oncology Unfiltered, Melissa Cotrona, Co-Founder and General Manager of i3 Health and Oncology Data Advisor, sat down with Editor in Chief Matthew Hadfield, DO, Medical Oncologist and Assistant Professor of Medicine at Brown University Legorreta Cancer Center, to unpack the practical, structural, and clinical hurdles that prevent eligible patients from accessing potentially lifesaving investigational therapies. Together, they explore how the oncology community can translate trial availability into meaningful access.

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  Oncology Data Advisor

  Building Trust in Real-World Evidence With AI and High-Quality Data

  Aug 11, 202614 min

  Read the full article at: https://oncdata.com/real-world-evidence-ai-in-oncology Artificial intelligence (AI) is rapidly transforming oncology research, but its impact depends on high-quality data. As researchers increasingly rely on real-world evidence (RWE) to complement clinical trials, ensuring that data is accurate, complete, and transparent has become just as important as developing new AI tools. In this episode of Exploring AI in Oncology, Waqas Haque, MD, MPH, Hematology/Oncology Fellow at the University of Chicago, speaks with Saamir Pasha, Senior Biostatistician at Ontada, about two complementary areas of innovation—the establishment of rigorous frameworks to assess the quality of oncology real-world data, and the exploration into how generative AI can accelerate complex biostatistical workflows while maintaining scientific rigor and regulatory transparency. Together, these efforts illustrate how AI is poised to enhance—not replace—the expertise required to generate reliable evidence in oncology.

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  Oncology Data Advisor

  One Size Fits One: Personalizing Cancer Prevention Through AI-Enhanced Imaging

  Aug 10, 202620 min

  Read the full article at: https://oncdata.com/personalizing-cancer-prevention-ai-enhanced-imaging As healthcare increasingly shifts from reactive treatment to proactive prevention, artificial intelligence (AI) is emerging as a powerful tool for identifying disease earlier and personalizing patient care. In this episode of Exploring AI in Oncology, Dr. Waqas Haque, Hematology/Oncology Fellow at the University of Chicago, spoke with Dr. Sean Raj, Chief Innovation Officer and Chief Medical Officer at SimonMed, about how AI is transforming cancer detection, risk assessment, and patient education. Their conversation highlights how advanced imaging technologies are helping patients gain earlier insights into their health while empowering clinicians with new tools for preventive care.

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  Oncology Data Advisor

  AI-Driven Pathological Subtyping for Pancreatic Cancer: Moving From Promise to Practical Application

  Aug 10, 202614 min

  Read the full article at: https://oncdata.com/ai-driven-pathological-subtyping-pancreatic-cancer-ashish-manne Artificial intelligence (AI) continues to generate excitement across oncology, but its real-world clinical impact remains inconsistent. In this episode of Exploring AI in Oncology, Dr. Ashish Manne, a Gastrointestinal Medical Oncologist at The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, shares his American Association for Cancer Research (AACR) presentation on AI-driven pathological subtyping in pancreatic cancer and highlights the critical shift that is moving AI from theoretical promise to practical application.

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  Oncology Data Advisor

  Advancing Extrapulmonary NEC: DLL3 & Beyond

  Jul 30, 202656 min

  Are you treating extrapulmonary neuroendocrine carcinomas based on where they start—or what they actually are? In this faculty follow-up to their i3 Health activity (Clinical Challenges in Extrapulmonary NEC: Expert Insights on Diagnosis, Treatment, and Emerging Targets), Andrew Hendifar, MD, and Danielle Hutchings, MD (Cedars-Sinai Medical Center), unpack why treating these rare, aggressive tumors according to site of origin rather than tumor lineage can compromise outcomes. They clarify crucial pathologic distinctions between well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs) while emphasizing the essential role of multidisciplinary collaboration in precision care. The experts also explore the novel therapeutic landscape of delta-like ligand 3 (DLL3)–targeted therapies, highlighting the promise of DLL3 T-cell engagers in extrapulmonary disease. They address ongoing diagnostic hurdles—such as standardized immunohistochemistry (IHC) expression thresholds and expanding send-out testing access—while sharing key insights on emerging clinical trials evaluating T-cell engager monotherapies and chemo-immunotherapy combinations. Claim your CME credit: bit.ly/488fPhK Download and review the companion Clinical Challenges in Extrapulmonary NEC Slide Deck: bit.ly/4w2Pni3

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