Combined nomogram for differentiating adrenal pheochromocytoma from large-diameter lipid-poor adenoma using multiphase CT radiomics and clinico-radiological features.

Journal: BMC medical imaging
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Adrenal incidentalomas (AIs) are predominantly adrenal adenomas (80%), with a smaller proportion (7%) being pheochromocytomas(PHEO). Adenomas are typically non-functional tumors managed through observation or medication, with some cases requiring surgical removal, which is generally safe. In contrast, PHEO secrete catecholamines, causing severe blood pressure fluctuations, making surgical resection the only treatment option. Without adequate preoperative preparation, perioperative mortality risk is significantly high.A specialized adrenal CT scanning protocol is recommended to differentiate between these tumor types. However, distinguishing patients with similar washout characteristics remains challenging, and concerns about efficiency, cost, and risk limit its feasibility. Recently, radiomics has demonstrated efficacy in identifying molecular-level differences in tumor cells, including adrenal tumors. This study develops a combined nomogram model, integrating key clinical-radiological and radiomic features from multiphase CT, to enhance accuracy in distinguishing pheochromocytoma from large-diameter lipid-poor adrenal adenoma (LP-AA).

Authors

  • Zujuan Shan
    Department of Urology, Honghe Hospital Affiliated to Kunming Medical University (South Yunnan Central Hospital of Yunnan Province), No. 1, Xiyuan Road, Gejiu City, Honghe, Yunnan, 661017, China.
  • Xinzhang Zhang
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650100, China.
  • Yiwen Zhang
  • Shuailong Wang
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650100, China.
  • Junfeng Wang
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Xin Shi
    Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Zhenhui Li
    College of Information Sciences and Technology, Pennsylvania State University.
  • Liuyang Yang
    College of Communication Engineering, Chongqing University, Chongqing, 400044, China.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Wenliang Li
    School of Civil Engineering, Liaoning Technical University, Fuxin, Liaoning, China.
  • Junfeng Yang
    The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650100, China. yjfkmmc@163.com.
  • Liansheng Yang
    Department of Urology, Honghe Hospital Affiliated to Kunming Medical University (South Yunnan Central Hospital of Yunnan Province), No. 1, Xiyuan Road, Gejiu City, Honghe, Yunnan, 661017, China. hhzyykyk@163.com.