European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2025
Lymph node metastasis prediction model for each lymph node station in gastric cancer patients.
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Kim Jong Won, Hong Hyunsook, Park Shin-Hoo, Choi Jong-Ho, Suh Yun-Suhk, Kong Seong-Ho, Park Do Joong, Lee Hyuk-Joon, Lee Hye Seung, Kwak Yoonjin, Kim Woo Ho, Sano Takeshi, Yang Han-Kwang
Аннотация
BACKGROUND: Lymph node metastasis (LNM) prediction for each LN station is required for tailored surgery for patient safety or improving quality of life in gastric cancer. This retrospective review was performed to develop a prediction program for calculating the probability of LNM according to LN stations in patients with gastric cancer.
METHOD: Among patients who underwent gastrectomy for primary gastric cancer between 2003 and 2017 at Seoul National University Hospital, 4660 patients up to 2013 were used as the development set, and 2564 patients after 2013 were used as the validation set. Not only the center of tumor but also all locations of stomach by tumor were included in the analysis. A multiple logistic regression analysis was used to develop an LNM prediction program for each LN station in development set. The program was validated using C-statistics and a calibration plot of the validation set.
RESULTS: Multivariate analysis identified tumor depth, gross type, and involved locations as covariates associated with LNM. However, the significant factors differed slightly according to the LN station. The prediction equations were developed for each LN station. In the validation set, the prediction equation exhibited good discriminant C-statistics of over 0.8 for all stations. The calibration plot of the prediction equation predicted the LNM rate, which corresponded closely to the actual rate.
CONCLUSIONS: A program was developed to predict LNM at LN stations. Predictive power was confirmed via internal validation. Predicting the LN metastatic rate for each LN station could help in planning more customized surgery.
METHOD: Among patients who underwent gastrectomy for primary gastric cancer between 2003 and 2017 at Seoul National University Hospital, 4660 patients up to 2013 were used as the development set, and 2564 patients after 2013 were used as the validation set. Not only the center of tumor but also all locations of stomach by tumor were included in the analysis. A multiple logistic regression analysis was used to develop an LNM prediction program for each LN station in development set. The program was validated using C-statistics and a calibration plot of the validation set.
RESULTS: Multivariate analysis identified tumor depth, gross type, and involved locations as covariates associated with LNM. However, the significant factors differed slightly according to the LN station. The prediction equations were developed for each LN station. In the validation set, the prediction equation exhibited good discriminant C-statistics of over 0.8 for all stations. The calibration plot of the prediction equation predicted the LNM rate, which corresponded closely to the actual rate.
CONCLUSIONS: A program was developed to predict LNM at LN stations. Predictive power was confirmed via internal validation. Predicting the LN metastatic rate for each LN station could help in planning more customized surgery.