Development of a predictive model for metachronous liver metastasis in gastric cancer
BackgroundPatients with metachronous liver metastasis (MLM) in gastric cancer generally have a poor prognosis. Early detection and accurate prediction of MLM are crucial for improving clinical outcomes. This study aims to identify the risk factors for MLM through clinical pathological parameters and...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1603471/full |
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| author | Siyuan Wang Siyuan Wang Gaozan Zheng Fengsu Wu Ye Tian Xinyu Qiao Xinyu Dou Hanjun Dan Hanjun Dan Guangming Ren Liaoran Niu Pengfei Wang Lili Duan Yumao Yang Jianyong Zheng Fan Feng |
| author_facet | Siyuan Wang Siyuan Wang Gaozan Zheng Fengsu Wu Ye Tian Xinyu Qiao Xinyu Dou Hanjun Dan Hanjun Dan Guangming Ren Liaoran Niu Pengfei Wang Lili Duan Yumao Yang Jianyong Zheng Fan Feng |
| author_sort | Siyuan Wang |
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| description | BackgroundPatients with metachronous liver metastasis (MLM) in gastric cancer generally have a poor prognosis. Early detection and accurate prediction of MLM are crucial for improving clinical outcomes. This study aims to identify the risk factors for MLM through clinical pathological parameters and develop a predictive model for MLM in gastric cancer.MethodsA retrospective analysis of 1248 gastric cancer patients who underwent radical surgery between December 2016 and December 2020 was conducted. Patients were randomly divided into training (70%, n=873) and validation (30%, n=375) datasets. The optimal cutoff values for the continuous variables were determined using the Youden index. Univariate and multivariate logistic regression analyses were used to identify risk factors for MLM. A nomogram was developed based on the results of multivariate analysis. The model’s value was validated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsThe incidence of MLM was comparable between the training (10.3%, 90/873) and validation set (9.9%, 37/375). The optimal cutoff value was 3.315ng/ml for preoperative alpha-fetoprotein (AFP) level, 16.275U/ml for preoperative cancer antigen 125 (CA125) level, 0.280×109/L for monocyte count and 1.430×109/L for lymphocyte count, respectively. Univariate analysis showed that age, tumor size, pathological type, surgical method, T stage, N stage, TNM stage, neural invasion, lymphatic vascular invasion, number of lymph nodes harvested (LNH), preoperative total protein (TP), hemoglobin (HB), albumin (ALB), preoperative carcinoembryonic antigen (CEA), preoperative cancer antigen 19-9 (CA19-9), CA125, AFP levels, monocyte count, lymphocyte count, red blood cell (RBC) count and platelet count were considered as potential variables. Multivariate logistic regression analysis indicated that T stage, N stage, monocyte count, lymphocyte count, preoperative AFP and CA125 levels were independent predictive factors for MLM. The identified risk factors were further used to develop a predictive nomogram for MLM. The nomogram exhibited robust discriminatory performance, with an area under the curve (AUC) of 0.859 in the training set and 0.803 in the validation set. Moreover, the nomogram demonstrated excellent calibration and significant clinical utility.ConclusionThis study successfully developed a predictive nomogram for MLM in gastric cancer. Besides conventional parameters, we identified and incorporated peripheral blood monocyte and lymphocyte counts as novel predictors, demonstrating their independent predictive value. Integrating these factors into nomogram could enhance predictive accuracy of MLM. |
| format | Article |
| id | doaj-art-f82864b4b4e34ef681fbc311da9e9e14 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-f82864b4b4e34ef681fbc311da9e9e142025-08-20T04:02:51ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-08-011510.3389/fonc.2025.16034711603471Development of a predictive model for metachronous liver metastasis in gastric cancerSiyuan Wang0Siyuan Wang1Gaozan Zheng2Fengsu Wu3Ye Tian4Xinyu Qiao5Xinyu Dou6Hanjun Dan7Hanjun Dan8Guangming Ren9Liaoran Niu10Pengfei Wang11Lili Duan12Yumao Yang13Jianyong Zheng14Fan Feng15Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaInstitute of Anal-Colorectal Surgery, The 989th Hospital of the Joint Logistics Support Force of People's Liberation Army (PLA), Luoyang, Henan, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Gastroenterology, Pingdingshan Medical District, 989 Hospital of People's Liberation Army (PLA) Joint Logistics Support Force, Pingdingshan, Henan, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Digestive Surgery, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, ChinaBackgroundPatients with metachronous liver metastasis (MLM) in gastric cancer generally have a poor prognosis. Early detection and accurate prediction of MLM are crucial for improving clinical outcomes. This study aims to identify the risk factors for MLM through clinical pathological parameters and develop a predictive model for MLM in gastric cancer.MethodsA retrospective analysis of 1248 gastric cancer patients who underwent radical surgery between December 2016 and December 2020 was conducted. Patients were randomly divided into training (70%, n=873) and validation (30%, n=375) datasets. The optimal cutoff values for the continuous variables were determined using the Youden index. Univariate and multivariate logistic regression analyses were used to identify risk factors for MLM. A nomogram was developed based on the results of multivariate analysis. The model’s value was validated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsThe incidence of MLM was comparable between the training (10.3%, 90/873) and validation set (9.9%, 37/375). The optimal cutoff value was 3.315ng/ml for preoperative alpha-fetoprotein (AFP) level, 16.275U/ml for preoperative cancer antigen 125 (CA125) level, 0.280×109/L for monocyte count and 1.430×109/L for lymphocyte count, respectively. Univariate analysis showed that age, tumor size, pathological type, surgical method, T stage, N stage, TNM stage, neural invasion, lymphatic vascular invasion, number of lymph nodes harvested (LNH), preoperative total protein (TP), hemoglobin (HB), albumin (ALB), preoperative carcinoembryonic antigen (CEA), preoperative cancer antigen 19-9 (CA19-9), CA125, AFP levels, monocyte count, lymphocyte count, red blood cell (RBC) count and platelet count were considered as potential variables. Multivariate logistic regression analysis indicated that T stage, N stage, monocyte count, lymphocyte count, preoperative AFP and CA125 levels were independent predictive factors for MLM. The identified risk factors were further used to develop a predictive nomogram for MLM. The nomogram exhibited robust discriminatory performance, with an area under the curve (AUC) of 0.859 in the training set and 0.803 in the validation set. Moreover, the nomogram demonstrated excellent calibration and significant clinical utility.ConclusionThis study successfully developed a predictive nomogram for MLM in gastric cancer. Besides conventional parameters, we identified and incorporated peripheral blood monocyte and lymphocyte counts as novel predictors, demonstrating their independent predictive value. Integrating these factors into nomogram could enhance predictive accuracy of MLM.https://www.frontiersin.org/articles/10.3389/fonc.2025.1603471/fullgastric cancermonocytelymphocytemetachronous liver metastasisnomogram |
| spellingShingle | Siyuan Wang Siyuan Wang Gaozan Zheng Fengsu Wu Ye Tian Xinyu Qiao Xinyu Dou Hanjun Dan Hanjun Dan Guangming Ren Liaoran Niu Pengfei Wang Lili Duan Yumao Yang Jianyong Zheng Fan Feng Development of a predictive model for metachronous liver metastasis in gastric cancer Frontiers in Oncology gastric cancer monocyte lymphocyte metachronous liver metastasis nomogram |
| title | Development of a predictive model for metachronous liver metastasis in gastric cancer |
| title_full | Development of a predictive model for metachronous liver metastasis in gastric cancer |
| title_fullStr | Development of a predictive model for metachronous liver metastasis in gastric cancer |
| title_full_unstemmed | Development of a predictive model for metachronous liver metastasis in gastric cancer |
| title_short | Development of a predictive model for metachronous liver metastasis in gastric cancer |
| title_sort | development of a predictive model for metachronous liver metastasis in gastric cancer |
| topic | gastric cancer monocyte lymphocyte metachronous liver metastasis nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1603471/full |
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