Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods
Abstract Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical fou...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53145-6 |
| _version_ | 1797247321331728384 |
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| author | Qingquan Chen Yiming Hu Wen Lin Zhimin Huang Jiaxin Li Haibin Lu Rongrong Dai Liuxia You |
| author_facet | Qingquan Chen Yiming Hu Wen Lin Zhimin Huang Jiaxin Li Haibin Lu Rongrong Dai Liuxia You |
| author_sort | Qingquan Chen |
| collection | DOAJ |
| description | Abstract Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan–Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan–Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC. |
| first_indexed | 2024-03-07T15:08:01Z |
| format | Article |
| id | doaj.art-32bcb74cccab4ce4a06dd74a77f53c0b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:56:50Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-32bcb74cccab4ce4a06dd74a77f53c0b2024-03-24T12:16:28ZengNature PortfolioScientific Reports2045-23222024-03-0114111310.1038/s41598-024-53145-6Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methodsQingquan Chen0Yiming Hu1Wen Lin2Zhimin Huang3Jiaxin Li4Haibin Lu5Rongrong Dai6Liuxia You7The Second Affiliated Hospital of Fujian Medical UniversityThe School of Public Health, Fujian Medical UniversityFuzong Clinical Medical College of Fujian Medical UniversityThe School of Public Health, Fujian Medical UniversityAnyang UniversityThe School of Public Health, Fujian Medical UniversityThe School of Public Health, Fujian Medical UniversityThe Second Affiliated Hospital of Fujian Medical UniversityAbstract Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan–Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan–Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC.https://doi.org/10.1038/s41598-024-53145-6 |
| spellingShingle | Qingquan Chen Yiming Hu Wen Lin Zhimin Huang Jiaxin Li Haibin Lu Rongrong Dai Liuxia You Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods Scientific Reports |
| title | Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| title_full | Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| title_fullStr | Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| title_full_unstemmed | Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| title_short | Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| title_sort | studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods |
| url | https://doi.org/10.1038/s41598-024-53145-6 |
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