A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features
ObjectiveColorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1186298/full |
| _version_ | 1797803366628196352 |
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| author | Jiang-Hua He Cong Cao Yang Ding Yun Yi Yu-Qing Lv Chun Wang Ying Chang |
| author_facet | Jiang-Hua He Cong Cao Yang Ding Yun Yi Yu-Qing Lv Chun Wang Ying Chang |
| author_sort | Jiang-Hua He |
| collection | DOAJ |
| description | ObjectiveColorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups.MethodsWe retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People’s Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA).ResultsA total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People’s Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level (p = 0.009), carcinoembryonic antigen (CEA) level (p = 0.032), histological grade (p < 0.001), colorectal cancer tumor markers (p < 0.001), N stage (p < 0.001), and tumor site (p = 0.005) were associated with distant metastasis in colorectal cancer patients. Multivariate logistic regression analysis showed that N stage (p < 0.001), histological grade (p = 0.026), and colorectal cancer markers (p < 0.001) were independent predictors of distant metastasis in patients initially diagnosed with colorectal cancer. The above six risk factors were used to predict distant metastasis of newly diagnosed colorectal cancer. The C-indexes for the prediction of the nomogram were 0.902 (95% confidence interval (CI), 0.857–0.948).ConclusionThe nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making. |
| first_indexed | 2024-03-13T05:19:45Z |
| format | Article |
| id | doaj.art-f640f4a48e4c4a1eb7f9053cb4fbf9c4 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-13T05:19:45Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-f640f4a48e4c4a1eb7f9053cb4fbf9c42023-06-15T16:44:14ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-06-011310.3389/fonc.2023.11862981186298A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical featuresJiang-Hua He0Cong Cao1Yang Ding2Yun Yi3Yu-Qing Lv4Chun Wang5Ying Chang6Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Colorectal Surgery, Gansu Provincial People’s Hospital, Gansu, ChinaDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaObjectiveColorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups.MethodsWe retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People’s Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA).ResultsA total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People’s Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level (p = 0.009), carcinoembryonic antigen (CEA) level (p = 0.032), histological grade (p < 0.001), colorectal cancer tumor markers (p < 0.001), N stage (p < 0.001), and tumor site (p = 0.005) were associated with distant metastasis in colorectal cancer patients. Multivariate logistic regression analysis showed that N stage (p < 0.001), histological grade (p = 0.026), and colorectal cancer markers (p < 0.001) were independent predictors of distant metastasis in patients initially diagnosed with colorectal cancer. The above six risk factors were used to predict distant metastasis of newly diagnosed colorectal cancer. The C-indexes for the prediction of the nomogram were 0.902 (95% confidence interval (CI), 0.857–0.948).ConclusionThe nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making.https://www.frontiersin.org/articles/10.3389/fonc.2023.1186298/fullcolorectal cancerdistant metastasisprognosisnomogramplatelet count |
| spellingShingle | Jiang-Hua He Cong Cao Yang Ding Yun Yi Yu-Qing Lv Chun Wang Ying Chang A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features Frontiers in Oncology colorectal cancer distant metastasis prognosis nomogram platelet count |
| title | A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| title_full | A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| title_fullStr | A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| title_full_unstemmed | A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| title_short | A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| title_sort | nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features |
| topic | colorectal cancer distant metastasis prognosis nomogram platelet count |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1186298/full |
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