Dual-time point 18F-FDG PET/CT for the staging of oesophageal cancer: the best diagnostic performance by retention index for N-staging in ... View Full Text


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Article Info

DATE

2018-03-03

AUTHORS

Sohyun Park, Jin Chul Paeng, Chang Hyun Kang, Gi Jeong Cheon, Keon Wook Kang, June-Key Chung, Dong Soo Lee

ABSTRACT

PurposeThe purpose of this study is to investigate the role of dual time point (DTP) 18F-FDG PET/CT in the staging of oesophageal cancer, especially in lymph node metastasis.MethodsA total of 35 patients with oesophageal squamous cell carcinoma who underwent surgical treatment without neoadjuvant chemotherapy were enrolled as a test set and another 19 patients were enrolled as a validation set. The DTP PET/CT scans were obtained in dual time points at 60 and 120 min each, following the administration of 18F-FDG. Visual analysis was performed and semiquantitative analysis was performed using several PET parameters such as maximal standardized uptake values (SUVmax), peak SUV (SUVpeak) and retention indexes using SUVmax (RImax) and SUVpeak (RIpeak).ResultsPrimary oesophageal lesions exhibited a significant difference for SUVmax at each time point scan (ANOVA, p < 0.001). For nodal staging, a total of 276 non-calcified nodal stations of the test set were evaluated. Sensitivity, specificity and accuracy of visual analysis were 32.0% (8 of 25), 96.8% (243 of 251) and 90.9% (251 of 276) in the test set. Using ROC analysis, RImax had the largest area under the curve (AUC) to detect metastatic lymphadenopathy at the optimal cut-off value of 6% (AUC 0.853, P < 0.001) in the test set (sensitivity, specificity and accuracy; 80.0% (20 of 25), 94.8% (238 of 251) and 93.5% (258 of 276)). In the validation set (179 non-calcified nodal stations), sensitivity, specificity and accuracy of RImax at the optimal cut-off of 6% were 71.4% (5 of 7), 99.4% (171 of 172) and 98.4% (176 of 179), whereas those of visual analysis were 14.3% (1 of 7), 98.8% (170 of 172) and 95.5% (171 of 179).ConclusionsThe best diagnostic performance of nodal staging in patients with oesophageal cancer was achieved by application of RImax with a cut-off of more than 6% on DTP 18F-FDG PET/CT with the exclusion of calcified lymph nodes. Optimal clinical management in surgically-candidate oesophageal cancer patients could be achieved using the diagnostic flow on DTP 18F-FDG PET/CT. More... »

PAGES

1317-1328

References to SciGraph publications

  • 2011-12. Role of F18-FDG PET/CT in the Staging and Restaging of Esophageal Cancer: A Comparison with CECT in INDIAN JOURNAL OF SURGICAL ONCOLOGY
  • 2008-01-22. Staging investigations for oesophageal cancer: a meta-analysis in BRITISH JOURNAL OF CANCER
  • 2010-02-24. The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM in ANNALS OF SURGICAL ONCOLOGY
  • 2009-05-09. 18F-FDG PET/CT in mediastinal lymph node staging of non-small-cell lung cancer in a tuberculosis-endemic country: consideration of lymph node calcification and distribution pattern to improve specificity in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2009-01. Integrated FDG-PET/CT compared with intravenous contrast-enhanced CT for evaluation of metastatic regional lymph nodes in patients with resectable early stage esophageal cancer in ANNALS OF NUCLEAR MEDICINE
  • 2010-08-04. A simple table lookup method for PET/CT partial volume correction using a point-spread function in diagnosing lymph node metastasis in ANNALS OF NUCLEAR MEDICINE
  • 2009-05-15. Differential diagnosis between 18F-FDG-avid metastatic lymph nodes in non-small cell lung cancer and benign nodes on dual-time point PET/CT scan in ANNALS OF NUCLEAR MEDICINE
  • 2001-06. Advantage of delayed whole-body FDG-PET imaging for tumour detection in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2010-04-09. Integrated PET/CT Fusion Imaging and Endoscopic Ultrasound in the Pre-operative Staging and Evaluation of Esophageal Cancer in MOLECULAR IMAGING AND BIOLOGY
  • 2008-02-19. 18F-FDG PET for the lymph node staging of non-small cell lung cancer in a tuberculosis-endemic country: Is dual time point imaging worth the effort? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-01-30. When should we recommend use of dual time-point and delayed time-point imaging techniques in FDG PET? in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
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    http://scigraph.springernature.com/pub.10.1007/s00259-018-3981-8

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    http://dx.doi.org/10.1007/s00259-018-3981-8

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    33 schema:description PurposeThe purpose of this study is to investigate the role of dual time point (DTP) 18F-FDG PET/CT in the staging of oesophageal cancer, especially in lymph node metastasis.MethodsA total of 35 patients with oesophageal squamous cell carcinoma who underwent surgical treatment without neoadjuvant chemotherapy were enrolled as a test set and another 19 patients were enrolled as a validation set. The DTP PET/CT scans were obtained in dual time points at 60 and 120 min each, following the administration of 18F-FDG. Visual analysis was performed and semiquantitative analysis was performed using several PET parameters such as maximal standardized uptake values (SUVmax), peak SUV (SUVpeak) and retention indexes using SUVmax (RImax) and SUVpeak (RIpeak).ResultsPrimary oesophageal lesions exhibited a significant difference for SUVmax at each time point scan (ANOVA, p < 0.001). For nodal staging, a total of 276 non-calcified nodal stations of the test set were evaluated. Sensitivity, specificity and accuracy of visual analysis were 32.0% (8 of 25), 96.8% (243 of 251) and 90.9% (251 of 276) in the test set. Using ROC analysis, RImax had the largest area under the curve (AUC) to detect metastatic lymphadenopathy at the optimal cut-off value of 6% (AUC 0.853, P < 0.001) in the test set (sensitivity, specificity and accuracy; 80.0% (20 of 25), 94.8% (238 of 251) and 93.5% (258 of 276)). In the validation set (179 non-calcified nodal stations), sensitivity, specificity and accuracy of RImax at the optimal cut-off of 6% were 71.4% (5 of 7), 99.4% (171 of 172) and 98.4% (176 of 179), whereas those of visual analysis were 14.3% (1 of 7), 98.8% (170 of 172) and 95.5% (171 of 179).ConclusionsThe best diagnostic performance of nodal staging in patients with oesophageal cancer was achieved by application of RImax with a cut-off of more than 6% on DTP 18F-FDG PET/CT with the exclusion of calcified lymph nodes. Optimal clinical management in surgically-candidate oesophageal cancer patients could be achieved using the diagnostic flow on DTP 18F-FDG PET/CT.
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