Dr Shengwei Li | Computer-Assisted Surgical Planning | Best Researcher Award

Dr Shengwei Li | Computer-Assisted Surgical Planning | Best Researcher Award

Peking Union Medical College | China

AUTHOR PROFILE

Scopus

🎓EARLY ACADEMIC PURSUITS 

Shengwei Li graduated from the prestigious Top Pilot Class at Southeast University in 2021. He then embarked on a promising academic career as a doctoral student at Peking Union Medical College, starting in 2022. His foundational studies laid a strong groundwork for his future contributions in the field of interventional oncology. Shengwei has gained valuable experience, particularly in the clinical setting of Zhongda Hospital, where he worked for three years.

🏥PROFESSIONAL ENDEAVORS 

Shengwei’s professional experience specializes in the field of interventional oncology, with a focus on treating malignant tumors. His expertise shines in managing complex cases such as unresectable hepatocellular carcinoma and early-stage lung cancer. His clinical acumen is further enriched through his active participation in several clinical trials, including multi-center randomized controlled trials, all of which contribute to cutting-edge oncology treatments.

🧑‍🔬CONTRIBUTIONS AND RESEARCH FOCUS ON COMPUTER-ASSISTED SURGICAL PLANNING

Shengwei’s research efforts are centered on computer-assisted surgical planning, a field where he has made significant advancements. His major contributions include the development of deep learning-based algorithms that assist in dose calculation for 125I seed-loaded stents and automated image segmentation for liver and vascular systems. These innovations directly improve preoperative planning and surgical outcomes for hepatocellular carcinoma (HCC). Additionally, he has introduced an automatic planning system for CT-guided percutaneous liver tumor ablation, which promises to make treatment planning more efficient for healthcare professionals.

🔬IMPACT AND INFLUENCE 

His published works—five research articles in indexed journals—are primarily focused on improving treatments for unresectable HCC. Shengwei’s groundbreaking research on deep learning and medical algorithms is expected to significantly impact the field of interventional oncology, particularly for hard-to-treat liver cancers. His innovations aim to increase the accuracy and effectiveness of tumor ablation procedures, ultimately benefiting patients and advancing the field of oncology.

📚ACADEMIC CITES AND CREDENTIALS 

Shengwei’s research has garnered recognition within the academic community, reflected by his citation index of 2 and the growing influence of his published works. He has completed 3 research projects and holds 3 patents under process, marking his commitment to innovation. His work has been published in 5 indexed journals, and his ongoing efforts ensure that he continues contributing to the field of medical research and technology.

🔮LEGACY AND FUTURE CONTRIBUTIONS 

Looking ahead, Shengwei Li’s work promises to bridge the gap between clinical practice and technological innovation. His vision of integrating advanced computational techniques with interventional oncology has the potential to transform the way complex cancers are treated. His ongoing research will likely continue to influence future medical practices and technologies, making him a promising figure in both medicine and engineering.

CONCLUSION

Shengwei Li is a promising and dedicated researcher whose work in interventional oncology and computer-assisted surgical planning is making significant strides in the treatment of complex cancers like unresectable hepatocellular carcinoma. His innovative approach, combining deep learning algorithms with clinical practice, highlights his potential to revolutionize cancer treatments. With multiple patents in progress and several impactful research publications, he is well-positioned to contribute further to the advancement of medical science. Shengwei’s interdisciplinary expertise and dedication make him a deserving candidate for the Best Researcher Award.

📊🔬NOTABLE PUBLICATION:

    • Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm
      • Authors: Li, S., Li, X.-G., Zhou, F., Peng, J., Li, B.
      • Journal: Journal of Applied Clinical Medical Physics
      • Year: 2024

     

    • Acute-phase plasma proteomics of rabbit lung VX2 tumors treated by image-guided microwave ablation
      • Authors: Cheng, L., Peng, J.-Z., Li, S.-W., Bie, Z.-X., Li, X.-G.
      • Journal: Frontiers in Oncology
      • Year: 2024