Annals of Surgical Oncology

, Volume 22, Issue 11, pp 3574–3581 | Cite as

The Role of FDG-PET in the Initial Staging and Response Assessment of Anal Cancer: A Systematic Review and Meta-analysis

  • Michael Jones
  • George Hruby
  • Michael Solomon
  • Natalie Rutherford
  • Jarad Martin
Colorectal Cancer



The aim of this systematic review and meta-analysis was to compare the role of FDG-positron emission tomography (PET) or PET/computed tomography (CT) with conventional imaging in the detection of primary and nodal disease in anal cancer, and to assess the impact of PET or PET/CT on the management of anal cancer.


A systematic review of the literature was performed. Eligible studies included those comparing PET or PET/CT with conventional imaging in the staging of histologically confirmed anal squamous cell carcinoma (SCC), or studies that performed PET or PET/CT imaging to assess response following treatment.


Twelve studies met the inclusion criteria. For the detection of primary disease, CT and PET had a sensitivity of 60 % (95 % confidence interval [CI] 45.5–75.2) and 99 % (95 % CI 96–100), respectively. Compared with conventional imaging, PET upstaged 15 % (95 % CI 10–21) and downstaged 15 % (95 % CI 10–20) of nodal disease. This led to a change in nodal staging in 28 % of patients (95 % CI 18–38). When only studies performing contemporary PET/CT were considered, the rate of nodal upstaging was 21 % (95 % CI 13–30) and the TNM stage was altered in 41 % of patients. Following chemoradiotherapy, 78 % (95 % CI 65–88) of patients had a complete response on PET.


Compared with conventional imaging, PET or PET/CT alters the nodal status in a sufficient number of cases to justify its routine use in the staging of patients with anal SCC.


Positive Predictive Value Sentinel Node Biopsy Conventional Imaging National Comprehensive Cancer Network National Comprehensive Cancer Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Dr. Christopher Oldmeadow for performing the statistical analysis.




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Copyright information

© Society of Surgical Oncology 2015

Authors and Affiliations

  • Michael Jones
    • 1
  • George Hruby
    • 1
  • Michael Solomon
    • 2
  • Natalie Rutherford
    • 3
  • Jarad Martin
    • 4
  1. 1.Department of Radiation OncologyRoyal Prince Alfred HospitalCamperdownAustralia
  2. 2.Department of SurgeryRoyal Prince Alfred HospitalCamperdownAustralia
  3. 3.Department of Nuclear MedicineCalvary Mater NewcastleWaratahAustralia
  4. 4.Department of Radiation OncologyCalvary Mater NewcastleWaratahAustralia

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