Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning
Abstract Background Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil–lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unc...
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BMC
2024-12-01
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| Online Access: | https://doi.org/10.1186/s12885-024-13331-1 |
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| author | Lei Liang Ning Xu Lanfei Ding Xin Li Chengxun Jiang Jianhua Zhang Jun Yang |
| author_facet | Lei Liang Ning Xu Lanfei Ding Xin Li Chengxun Jiang Jianhua Zhang Jun Yang |
| author_sort | Lei Liang |
| collection | DOAJ |
| description | Abstract Background Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil–lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unclear. This study developed a nomogram to predict long-term prognosis for these patients. Methods This retrospective study included 1540 patients (training set) from the First Affiliated Hospital of Kunming Medical University and 152 patients (testing set) from The Honghe Third People's Hospital. Cox regression identified independent prognostic factors, and machine learning established predictive models. Model performance was evaluated by the C-index, area under the curve (AUC), and decision curve analysis (DCA). Results In the training set, a total of 1540 patients with stage II/III colorectal cancer were included. More than 70 years old (HR = 1.830, p = 0.000); SIS = 2 (HR = 1.693, p = 0.002); Preoperative CEA more than 5 ng/mL (HR = 1.614, p = 0.000); and Moderately differentiated (HR = 1.438, p = 0.011); or Low/undifferentiated (HR = 2.126, p = 0.000); The pN1 (HR = 2.040, p = 0.000) and pN2 (HR = 3.297, p = 0.000) stages were considered independent prognostic risk factors of stage II/III colorectal cancer. Negative perineural invasion (HR = 0.733, p = 0.014) and NLR less than 4 (HR = 0.696, p = 0.022) were considered independent prognostic protective factors of stage II/III colorectal cancer. A nomogram was established based on SIS, NLR, and the clinicopathological results for predicting and validating the overall survival in the training and testing sets. The C-index of the training set was 0.746, and the C-index of the testing set was 0.708, indicating the high prediction efficiency of the nomogram. Conclusions A nomogram combining SIS, NLR, and clinicopathological factors provides an effective, cost-efficient tool for predicting the prognosis of stage II/III colorectal cancer. Future studies will validate its long-term predictive performance in larger, multicenter cohorts. |
| format | Article |
| id | doaj-art-502fe26f8dee4946b04544e9f3070bca |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-502fe26f8dee4946b04544e9f3070bca2025-08-20T02:39:50ZengBMCBMC Cancer1471-24072024-12-0124111210.1186/s12885-024-13331-1Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learningLei Liang0Ning Xu1Lanfei Ding2Xin Li3Chengxun Jiang4Jianhua Zhang5Jun Yang6Department of Surgical Oncology, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Surgical Oncology, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Emergency, The Second People’s Hospital of Honghe PrefectureDepartment of Surgical Oncology, The First Affiliated Hospital of Kunming Medical UniversityDepartment of General Surgery, The Third People’s Hospital of Honghe PrefectureDepartment of General Surgery, The Third People’s Hospital of Honghe PrefectureDepartment of Surgical Oncology, The First Affiliated Hospital of Kunming Medical UniversityAbstract Background Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil–lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in stage II/III colorectal cancer remains unclear. This study developed a nomogram to predict long-term prognosis for these patients. Methods This retrospective study included 1540 patients (training set) from the First Affiliated Hospital of Kunming Medical University and 152 patients (testing set) from The Honghe Third People's Hospital. Cox regression identified independent prognostic factors, and machine learning established predictive models. Model performance was evaluated by the C-index, area under the curve (AUC), and decision curve analysis (DCA). Results In the training set, a total of 1540 patients with stage II/III colorectal cancer were included. More than 70 years old (HR = 1.830, p = 0.000); SIS = 2 (HR = 1.693, p = 0.002); Preoperative CEA more than 5 ng/mL (HR = 1.614, p = 0.000); and Moderately differentiated (HR = 1.438, p = 0.011); or Low/undifferentiated (HR = 2.126, p = 0.000); The pN1 (HR = 2.040, p = 0.000) and pN2 (HR = 3.297, p = 0.000) stages were considered independent prognostic risk factors of stage II/III colorectal cancer. Negative perineural invasion (HR = 0.733, p = 0.014) and NLR less than 4 (HR = 0.696, p = 0.022) were considered independent prognostic protective factors of stage II/III colorectal cancer. A nomogram was established based on SIS, NLR, and the clinicopathological results for predicting and validating the overall survival in the training and testing sets. The C-index of the training set was 0.746, and the C-index of the testing set was 0.708, indicating the high prediction efficiency of the nomogram. Conclusions A nomogram combining SIS, NLR, and clinicopathological factors provides an effective, cost-efficient tool for predicting the prognosis of stage II/III colorectal cancer. Future studies will validate its long-term predictive performance in larger, multicenter cohorts.https://doi.org/10.1186/s12885-024-13331-1SISNLRCliniopathological factorsColorectal cancerNomogram |
| spellingShingle | Lei Liang Ning Xu Lanfei Ding Xin Li Chengxun Jiang Jianhua Zhang Jun Yang Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning BMC Cancer SIS NLR Cliniopathological factors Colorectal cancer Nomogram |
| title | Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning |
| title_full | Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning |
| title_fullStr | Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning |
| title_full_unstemmed | Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning |
| title_short | Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning |
| title_sort | combined inflammation related biomarkers and clinicopathological features for the prognosis of stage ii iii colorectal cancer by machine learning |
| topic | SIS NLR Cliniopathological factors Colorectal cancer Nomogram |
| url | https://doi.org/10.1186/s12885-024-13331-1 |
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