A systematic review of automated feeder detection software for locoregional treatment of hepatic tumors

Z. Cui, P. A. Shukla, P. Habibollahi, H. S. Park, A. Fischman, M. K. Kolber

Research output: Contribution to journalReview articlepeer-review

22 Scopus citations


Purpose: The purpose of this study was to perform a systematic review of current literature describing the efficacy and technical outcomes of transarterial liver therapies using automated feeder detection (AFD) software. Materials and methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A structured search was performed in the PubMed, SCOPUS, and Embase databases of patients undergoing locoregional therapy of liver tumors utilizing AFD software. Demographic data, procedure data (including radiometrics) and tumor response rate were recorded. Where available, performance of AFD was compared to conventional digital subtraction angiography (DSA) and cone-beam CT (CBCT) without AFD. Results: A total of 14 full-text manuscripts met inclusion criteria, comprising 1042 tumors in 604 patients (305 men, 156 women; mean age, 68.6 ± 6.0 [SD] years), including 537 patients with hepatocellular carcinoma, 8 with metastases from neuroendocrine tumors, and 59 patients without reported etiology. Reported sensitivity of AFD ranged between 86% and 98.5%, compared to DSA alone (38% – 64%) or DSA in combination with CBCT (69% – 81%). Three studies reported tumor response by modified response evaluation criteria in solid tumors (mRECIST) guidelines, with complete response in the range of 60% – 69%. Conclusion: AFD is a promising new technology for the identification of intrahepatic and extrahepatic tumor-feeding arteries and should be considered a useful adjunct to conventional DSA and CBCT in the treatment of liver tumors.

Original languageEnglish
Pages (from-to)439-449
Number of pages11
JournalDiagnostic and interventional imaging
Issue number7-8
StatePublished - 1 Jul 2020


  • Angiography
  • Automated feeder detection
  • Cone-beam computed tomography
  • Digital subtraction
  • Liver neoplasm


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