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ABSTRACT: Purpose
To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.Materials and methods
The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.Results
LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.Conclusion
The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.
SUBMITTER: Lee HD
PROVIDER: S-EPMC10582533 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
Lee Hae Dong HD Nam Kyung Han KH Shin Cheol Min CM Lee Hye Seung HS Chang Young Hoon YH Yoon Hyuk H Park Young Soo YS Kim Nayoung N Lee Dong Ho DH Ahn Sang-Hoon SH Kim Hyung-Ho HH
Cancer research and treatment 20230321 4
<h4>Purpose</h4>To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.<h4>Materials and methods</h4>The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were ...[more]