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. 2008 Jul;207(1):7-12.
doi: 10.1016/j.jamcollsurg.2008.01.002. Epub 2008 Apr 14.

Accuracy of endorectal ultrasonography and computed tomography for restaging rectal cancer after preoperative chemoradiation

Jung Wook Huh  1 , Yoon Ah Park, Eun Joo Jung, Kang Young Lee, Seung-Kook Sohn
Affiliations

Accuracy of endorectal ultrasonography and computed tomography for restaging rectal cancer after preoperative chemoradiation

Jung Wook Huh et al. J Am Coll Surg. 2008 Jul.

Abstract

Background: Preoperative restaging of irradiated rectal cancer is essential for the planning of optimal therapy. The aim of this study was to compare the accuracy of endorectal ultrasonography (ERUS) and CT in restaging rectal cancer after preoperative chemoradiation and to evaluate the factors affecting the accuracy of ERUS.

Study design: Eighty-three patients with initial, locally advanced rectal cancer were prospectively evaluated by ERUS (n=60) and CT (n=80) after preoperative chemoradiation and just before surgery. All patients then underwent subsequent surgical resection and complete pathologic staging.

Results: In restaging the depth of invasion, the overall accuracy was 38.3% (23 of 60) by ERUS and 46.3% (37 of 80) by CT. Overstaging was more common than understaging with both imaging modalities. Accuracy for restaging lymph node metastasis was 72.6% (37 of 51) by ERUS and 70.4% (50 of 71) by CT. The predictive value of node-negative cases by ERUS was somewhat lower than that of CT (81.1% versus 85.4%, respectively). Complete pathology-proved remission was not correctly predicted in any of the 11 patients by any imaging modalities. Pathologic T and N staging correlated with the staging accuracy of ERUS (p=0.028 and p=0.001, respectively).

Conclusions: ERUS and CT may allow good prediction of node-negative rectal cancers, although they are inaccurate modalities for predicting treatment response on the rectal wall. New methods of interpretation and diagnostic criteria for ERUS and CT are essential for increasing the accuracy of cancer prediction in at-risk patients.

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