Usefulness of Elastica van Gieson staining and the number of samples prepared for venous invasion of colorectal cancer (pT2-pT4) - Kota Nakashima; Jun Akiba; Shinji Mizuochi; Masamichi Nakayama; Naohiro Yoshida; Kenichi Koushi; Takefumi Yoshida; Fumihiko Fujita; Hitoshi Obara; Tatsuyuki Kakuma; Yoshito Akagi; Hirohisa Yano (Elsevier, 2023)
Aims: Although venous invasion is an important prognostic factor for colorectal cancer, it may be significantly underestimated in actual daily diagnosis. In this study, hematoxylin-eosin (HE)- and Elastica van Gieson (EVG)-stained specimens of colorectal cancer (pT2-pT4) were obtained, and the influence of the number of specimens and the number of EVG-stained specimens on the prognosis were examined. Methods and results: The presence or absence of venous invasion in 100 colon cancer (pT2-pT4) specimens obtained after surgical resection was observed. Assessments were made by comparing the sections, of either the specimens of the deepest part of the tumor or the whole tumor, stained with only HE, as well as a combination of HE and EVG stains. There was a relative low agreement rate between the assessments made using whole-tumor EVG-stained sections and those employing other methods. With respect to relapse-free survival, no significant difference was observed in the prognosis of cases evaluated using HE-stained samples alone relative to the presence of venous invasion. However, for evaluations made using EVG staining, a significant difference was seen even for deepest-section assessments, and this trend was even stronger when whole-tumor sections were evaluated (EVG [deepest]: P = 0.0128, EVG [whole sections]: P = 0.0069). When the whole-tumor sections were observed with EVG staining, all 15 cases without venous invasion showed no recurrence within the observation period. Conclusions: The addition of EVG staining allowed the identification of venous invasion in patients with colorectal cancer, which eventually affects prognosis. Increasing the number of EVG-stained samples improves the possibility of accurate prediction.