Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists

Linda C. Chu, Seyoun Park, Sahar Soleimani, Daniel F. Fouladi, Shahab Shayesteh, Jin He, Ammar A. Javed, Christopher L. Wolfgang, Bert Vogelstein, Kenneth W. Kinzler, Ralph H. Hruban, Elham Afghani, Anne Marie Lennon, Elliot K. Fishman, Satomi Kawamoto

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: A wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist. Methods: In this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis. Results: 214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist. Conclusion: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions. Graphical abstract: [Figure not available: see fulltext.]

Original languageEnglish (US)
Pages (from-to)4139-4150
Number of pages12
JournalAbdominal Radiology
Volume47
Issue number12
DOIs
StatePublished - Dec 2022

Keywords

  • CT
  • Intraductal pancreatic mucinous neoplasm (IPMN)
  • Machine learning
  • Pancreatic cysts
  • Pancreatic neoplasms
  • Radiomics

ASJC Scopus subject areas

  • Gastroenterology
  • Urology
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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