Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma
Abstract Objective To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. Methods Clinical data were collected from 162 patients that...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-11960-0 |
| _version_ | 1797274333618372608 |
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| author | Ruiqiu Chen Lin Zhu Yibin Zhang Dongyu Cui Ruixiang Chen Hao Guo Li Peng Chaohui Xiao |
| author_facet | Ruiqiu Chen Lin Zhu Yibin Zhang Dongyu Cui Ruixiang Chen Hao Guo Li Peng Chaohui Xiao |
| author_sort | Ruiqiu Chen |
| collection | DOAJ |
| description | Abstract Objective To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. Methods Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. Results The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. Conclusion The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate. |
| first_indexed | 2024-03-07T14:56:56Z |
| format | Article |
| id | doaj.art-e100e507c85c48eab0ce618ae465b01f |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-03-07T14:56:56Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-e100e507c85c48eab0ce618ae465b01f2024-03-05T19:23:00ZengBMCBMC Cancer1471-24072024-02-0124111010.1186/s12885-024-11960-0Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinomaRuiqiu Chen0Lin Zhu1Yibin Zhang2Dongyu Cui3Ruixiang Chen4Hao Guo5Li Peng6Chaohui Xiao7Medical School of Chinese PLAMedical School of Chinese PLADepartment of Hepatobiliary Surgery, Zhongshan Hospital of Xiamen UniversityThe Fourth Hospital of Hebei Medical UniversityHebei Medical UniversityThe Fourth Hospital of Hebei Medical UniversityThe Fourth Hospital of Hebei Medical UniversityFaculty of Hepato-Biliary-Pancreatic Surgery, the First Medical Centre, Chinese People s Liberation Army (PLA) General HospitalAbstract Objective To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. Methods Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. Results The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. Conclusion The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.https://doi.org/10.1186/s12885-024-11960-0Ampullary CarcinomaRecurrenceLasso-Cox regressionPrediction modelNomogram |
| spellingShingle | Ruiqiu Chen Lin Zhu Yibin Zhang Dongyu Cui Ruixiang Chen Hao Guo Li Peng Chaohui Xiao Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma BMC Cancer Ampullary Carcinoma Recurrence Lasso-Cox regression Prediction model Nomogram |
| title | Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| title_full | Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| title_fullStr | Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| title_full_unstemmed | Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| title_short | Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| title_sort | predicting the unpredictable a robust nomogram for predicting recurrence in patients with ampullary carcinoma |
| topic | Ampullary Carcinoma Recurrence Lasso-Cox regression Prediction model Nomogram |
| url | https://doi.org/10.1186/s12885-024-11960-0 |
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