Papers
5
Total Citations
62
H-Index
4
About
Ziyun Shen is a surgical researcher whose work centers on advancing minimally invasive techniques in pancreatic and thoracic oncology, with a particular focus on reducing postoperative complications and improving oncologic outcomes. Shen’s most impactful contribution is the development of machine learning algorithms as early diagnostic tools for clinically relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy—a study that has garnered 30 citations and offers a data-driven approach to guide drain removal. In pancreatic surgery, Shen demonstrated through propensity score-matched analyses that robotic distal pancreatectomy significantly reduces pancreatic fistula rates in patients without visceral obesity (19 citations), and provided early evidence that robotic pancreatectomy yields favorable oncological outcomes in pancreatic cancer patients receiving adjuvant chemotherapy. Expanding into thoracic surgery, Shen has pioneered comparisons of robotic versus video-assisted thoracoscopic surgery (VATS) for non-small cell lung cancer, including after neoadjuvant chemoimmunotherapy and for complex sleeve lobectomies. With a publication record spanning 2022 to 2025, Shen’s work is at the forefront of integrating robotics, big data, and personalized risk stratification to improve surgical safety and long-term survival.
Research Focus
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Top Papers
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