Jun Watanabe, Katsuro Ichimasa, Yuki Kataoka, Shoko Miyahara, Atsushi Miki, Khay Guan Yeoh, Shigeo Kawai, Fernando Martínez de Juan, Isidro Machado, Kazuhiko Kotani, Naohiro Sata
Clinical and translational gastroenterology 2024年1月2日
INTRODUCTION: Treatment guidelines for colorectal cancer (CRC) suggest two classifications for histological differentiation-highest-grade and predominant. However, the optimal predictor of lymph node metastasis (LNM) in T1 CRC remains unknown. This systematic review aimed to evaluate the impact of the use of highest-grade or predominant differentiation on LNM determination in T1 CRC. METHODS: The study protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42023416971) and was published in OSF (https://osf.io/TMAUN/) on April 13, 2023.We searched five electronic databases for studies assessing the diagnostic accuracy of highest-grade or predominant differentiation to determine LNM in T1 CRC. The outcomes were sensitivity and specificity. We simulated 100 T1 CRC cases, with an LNM incidence of 11.2%, to calculate the differences in false positives and negatives between the highest-grade and predominant differentiations using a bootstrap method. RESULTS: In 42 studies involving 41,290 patients, the differentiation classification had a pooled sensitivity of 0.18 (95% confidence interval [CI], 0.13-0.24) and 0.06 (95% CI, 0.04-0.09) (P<0.0001) and specificity of 0.95 (95% CI, 0.93-0.96) and 0.98 (95% CI, 0.97-0.99) (P<0.0001) for the highest-grade and predominant differentiations, respectively. In the simulation, the differences in false positives and negatives between the highest-grade and predominant differentiations were 3.0% (range, 1.6-4.4) and -1.3% (range, -2.0 to -0.7), respectively. CONCLUSIONS: Highest-grade differentiation may reduce the risk of misclassifying LNM cases as negative, whereas predominant differentiation may prevent unnecessary surgeries. Further studies should examine differentiation classification using other predictive factors.