Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients

Peiyuan, Guo; xuhua, Hu; Ganlin, Guo; Xu, Yin; Zining, Liu; Jiachao, Han; Bin, Yu; Guiying, Wang

doi:10.1186/s12893-023-02018-2

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Furthermore, the nomogram model construction process is both faster and less reliant on extensive training data, which is very beneficial to our model construction. The nomogram model is a reliable and convenient tool widely used in the medical field [19][20][21][22][23]. In the field of agronomy, its application is relatively rare, but because of its intuitive, rapid construction and reliability, it is considered to be a potential tool.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Wang,

Yang,

Zhang

et al. 2023

Agronomy

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To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.

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Section: Introductionmentioning

confidence: 99%

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Wang,

Yang,

Zhang

et al. 2023

Agronomy

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Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III–IV Colorectal Cancer Patients

Lou,

Luo,

Huang

et al. 2024

Cancer Medicine

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BackgroundMost colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.MethodsA total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1‐, 3‐, 5‐, 8‐ and 10‐year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan–Meier curves were used to plot the survival probabilities for different strata of each risk factor.ResultsThere was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1‐, 3‐, 5‐, 8‐ and 10‐year OS, which in the training group were 0.826 (95% CI: 0.807–0.845), 0.836 (0.819–0.853), 0.839 (0.820–0.859), 0.835 (0.809–0.862) and 0.825 (0.779–0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786–0.852), 0.831 (0.804–0.858), 0.830 (0.799–0.861), 0.815 (0.774–0.857) and 0.802 (0.723–0.882) respectively. Finally, the 1‐, 3‐, 5‐, 8‐ and 10‐year OS rates for advanced stage CRC patients were 73.4 (71.8–75.0), 49.5 (47.8–51.4), 43.3 (41.5–45.2), 40.1 (38.1–41.9) and 38.6 (36.6–40.8) respectively.ConclusionWe constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high‐risk patients.

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Serum non-high-density lipoprotein cholesterol predicts distant metastases following resection of stages I to III colorectal cancer

Fang,

Shi,

Cong

et al. 2024

Medicine

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The aim of this study was to examine the relationship between levels of non-high-density lipoprotein cholesterol (non-HDL-C) and postoperative distant metastasis for stages I to III colorectal cancer (CRC). Demographic, clinicopathological, and lipid data were collected from 588 patients, who were subsequently grouped according to their non-HDL-C levels. The primary endpoint was distant metastasis, survival without distant metastasis-free survival (DMFS). The association between non-HDL-C and pathological features, as well as postoperative distant metastasis, was assessed using a chi-square test, Mann–Whitney U test, and Cox proportional hazard regression model. The correlation between DMFS and non-HDL-C levels was analyzed employing the Kaplan–Meier method and log-rank test. The incidence of postoperative distant metastasis was significantly higher in the high non-HDL-C group (34.8%) compared to the low non-HDL-C group (18.2%) (P < .001). Non-HDL-C levels were significantly higher in the metastasis group than in the nonmetastasis group (P = .001). Multivariate Cox proportional hazards identified non-HDL-C ≥ 4.1mmol/L(HR: 2.604; 95% CI: 1.584–4.282; P = .001) as independent risk factors for postoperative distant metastasis. The high non-HDL-C group exhibited a higher rate of distant metastasis and a shorter duration of DMFS (HR: 2.133; 95% CI: 1.404–3.240; P < .001). Our study suggests that high levels of non-HDL-C (≥4.1 mmol/L) may potentially serve as predictors for postoperative distant metastasis in stages I to III CRC.

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Construction and validation of a nomogram model for predicting the overall survival of colorectal cancer patients

Cited by 4 publications

References 41 publications

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III–IV Colorectal Cancer Patients

Serum non-high-density lipoprotein cholesterol predicts distant metastases following resection of stages I to III colorectal cancer

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