Victoria Rendell, MD

Assistant Professor

  • Division of Minimally Invasive Surgery

  • Administrative Assistant: 608-263-8604

600 Highland Ave
Madison, WI 53792


  • MD, Duke University School of Medicine, Durham, NC
  • Research Fellowship, University of Wisconsin School of Medicine and Public Health, Madison, WI
  • General Surgery Residency, University of Wisconsin Hospitals and Clinics, Madison, WI
  • Fellowship in Advanced GI/Minimally Invasive Surgery, Penn State Health Milton S. Hershey Medical Center, Hershey, PA

Clinical Specialties

Dr. Rendell is a fellowship trained minimally invasive surgeon. She is a member of the Society of American Gastrointestinal and Endoscopic Surgeons, the American Hernia Society, and the American College of Surgeons. She specializes in complex abdominal wall reconstruction, including robotic and laparoscopic approaches. She also has special expertise in advanced surgical endoscopy, benign foregut diseases, and gallbladder disorders.

Research Interests

Dr. Rendell’s research interests focus on improving clinical and patient-reported outcomes for hernia repairs, identifying and addressing barriers in access to hernia care, and enhancing nationwide quality of hernia care through surgical collaborations.

Recent Publications

  • Laparoscopic Common Bile Duct Exploration.
    Rendell VR, Pauli EM
    JAMA Surg 2023 Jul 01; 158(7): 766-767
    [PubMed ID: 37099282]

  • Parastomal Hernia Repair.
    Rendell VR, Pauli EM
    Surg Clin North Am 2023 Oct; 103(5): 993-1010
    [PubMed ID: 37709401]

  • Radiologic-pathologic correlation of lesions in resected liver specimens with an ex vivo MRI-compatible localization device.
    Rendell VR, Winslow ER, Colgan TJ, Kovacs SK, Mühler MR, Knobloch G, Loeffler AG, Agni RM, Reeder SB
    Eur Radiol 2023 Jan; 33(1): 535-544
    [PubMed ID: 35864349]

  • Deconstructing the Meaning of Multidisciplinary Cancer Care: A mixed-method study of patients and providers.
    Rendell VR, Ricker MM, Winslow ER
    Oncol Issues 2022; 37(2): 52-64
    [PubMed ID: 36845883]

  • Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.
    Awe AM, Vanden Heuvel MM, Yuan T, Rendell VR, Shen M, Kampani A, Liang S, Morgan DD, Winslow ER, Lubner MG
    Abdom Radiol (NY) 2022 Jan; 47(1): 221-231
    [PubMed ID: 34636933]

All Publications on PubMed