Masashi Takeuchi1, Hiroya Takeuchi2,3, Hirofumi Kawakubo1, Ayako Shimada1, Tadaki Nakahara4, Shuhei Mayanagi1, Masahiro Niihara5, Kazumasa Fukuda1, Rieko Nakamura1, Koichi Suda1, Norihito Wada1, Yuko Kitagawa1. 1. Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. 2. Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. htakeuchi@a6.keio.jp. 3. Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka, 431-3192, Japan. htakeuchi@a6.keio.jp. 4. Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. 5. Division of Esophageal Surgery, Shizuoka Cancer Center Hospital, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.
Abstract
BACKGROUND: Sentinel node (SN) concept is being applied to early gastric cancer. However, when SNs are positive for metastasis, it is unclear how often LNs in other LN basins show metastasis. We aimed to investigate LN metastasis possibility in LN basins without SNs (non-SN basins). We determined risk factors for metastasis in non-SN basins and identified a prediction model for non-SN basin metastasis using classification and regression tree (CART) analysis. METHODS: We enrolled 550 patients who were diagnosed with cT1N0M0 or cT2N0M0 gastric cancer with a single lesion and underwent SN mapping. We adopted a dual-tracer method using a radioactive colloid and blue dye to detect SNs. RESULTS: Of all, 45 (8.2%) patients had SN metastasis; we divided them into two groups: LN metastasis positive and LN metastasis negative in non-SN basins. Univariate analysis showed that the groups differed significantly regarding lymphatic invasion (p = 0.007), number of identified SNs (p = 0.032), and macrometastasis in SN basins (p = 0.005). The CART decision tree for predicting LN metastasis in non-SN basins had area under the curve value of 0.86. Moreover, there were significantly differences in cancer-specific survival (CSS) between the two groups (p = 0.028). CONCLUSIONS: Macrometastasis in SN basins, lymphatic invasion, and number of identified SNs ≥ 5 are risk factors for LN metastasis in non-SN basins among gastric cancer patients. We identified a prediction model with CART analysis; patients with macrometastasis in SN basins and lymphatic invasion were considered to be at the highest risk for LN metastasis.
BACKGROUND: Sentinel node (SN) concept is being applied to early gastric cancer. However, when SNs are positive for metastasis, it is unclear how often LNs in other LN basins show metastasis. We aimed to investigate LN metastasis possibility in LN basins without SNs (non-SN basins). We determined risk factors for metastasis in non-SN basins and identified a prediction model for non-SN basin metastasis using classification and regression tree (CART) analysis. METHODS: We enrolled 550 patients who were diagnosed with cT1N0M0 or cT2N0M0 gastric cancer with a single lesion and underwent SN mapping. We adopted a dual-tracer method using a radioactive colloid and blue dye to detect SNs. RESULTS: Of all, 45 (8.2%) patients had SN metastasis; we divided them into two groups: LN metastasis positive and LN metastasis negative in non-SN basins. Univariate analysis showed that the groups differed significantly regarding lymphatic invasion (p = 0.007), number of identified SNs (p = 0.032), and macrometastasis in SN basins (p = 0.005). The CART decision tree for predicting LN metastasis in non-SN basins had area under the curve value of 0.86. Moreover, there were significantly differences in cancer-specific survival (CSS) between the two groups (p = 0.028). CONCLUSIONS: Macrometastasis in SN basins, lymphatic invasion, and number of identified SNs ≥ 5 are risk factors for LN metastasis in non-SN basins among gastric cancerpatients. We identified a prediction model with CART analysis; patients with macrometastasis in SN basins and lymphatic invasion were considered to be at the highest risk for LN metastasis.