Radiology Imaging Cancer Podcast | RSNA

Dr. Yashbir Singh speaks with Dr. Ashok Srinivasan about his journey in neuroradiology, the hallmarks of impactful imaging research, and how mentorship and focused clinical questions drive meaningful scientific advances. Dr. Srinivasan also shares expert insights on translating AI and advanced imaging techniques into real-world practice, emphasizing clinical value, standardization, and patient-centered outcomes.
Radiology: Imaging Cancer

Dr. Yashbir Singh sits down with Dr. Lacey McNally to explore her groundbreaking work advancing contrast agents, molecular imaging, and first‑in‑human clinical trials. Together, they discuss the challenges of translational research, the essentials of high‑quality imaging manuscripts, and the evolving landscape of scientific publishing.
Radiology: Imaging Cancer

Dr. Yash Singh sits down with Dr. Sudhakar Venkatesh to explore his global journey in radiology and how innovations like MR elastography are transforming the noninvasive diagnosis and staging of liver disease. They discuss the future of quantitative, AI-enhanced imaging in detecting liver cancer and biliary diseases earlier—shifting radiology from passive observation to a central, collaborative force in precision medicine.
Radiology: Imaging Cancer special collection for Pancreatic Adenocarcinoma, Pancreatic Neuroendocrine, and Hepatobiliary Cancers.

In this episode, Tom DeSilvio, Brennan Flannery, and Yash Singh reflect on their experiences as trainee editorial board members, discussing what they've learned about peer review, clinical relevance, and evaluating innovation in cancer imaging research. They also share highlights from attending RSNA in person, including emerging trends like AI integration, foundation models, and the value of community and collaboration in radiology.
Radiology: Imaging Cancer

Brennan Flannery and Thomas DeSilvio sit down with Pallavi Tiwari to explore how AI is transforming medical imaging toward more individualized and precise care. The conversation dives into imaging the individual, trust in AI systems, and what the future holds for translating advanced models into real-world clinical impact.
Radiology: Imaging Cancer