Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer

Wang, Shuanhu; Liu, Yakui; Shi, Yi; Guan, Jiajia; Liu, Mulin; Wang, Wenbin

doi:10.1177/03000605211015023

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…These ndings are consistent with previous studies that have been done in the eld of predicting the survival of patients after surgery in colon cancer (16-18). For instance, Wang et al (2021) developed a nomogram to predict the overall survival of patients after curative surgery for colon cancer, which showed that age, tumor size, differentiation, TNM stage, and chemotherapy were independent predictors of overall survival (18).…”

Section: Discussionmentioning

confidence: 99%

Beyond Traditional Methods: Unleashing the Power of Neural Networks in Predicting Early Recurrence and Mortality after Curative Surgery in Colorectal Cancer Patients

Alinia

Mahmoudi

Norouzi

et al. 2023

Preprint

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Background: Medical research frequently relies on Cox regression to analyze the survival distribution of cancer patients. Nonetheless, in specific scenarios, neural networks hold the potential to serve as a robust alternative. In this study, we aim to scrutinize the effectiveness of Cox regression and neural network models in assessing the survival outcomes of patients who have undergone treatment for colorectal cancer. Materials and Methods: We conducted a retrospective study on 284 colorectal cancer patients who underwent surgery at Imam Khomeini clinic in Hamadan between 2001 and 2017. The data was used to train both Cox regression and neural network models, and their predictive accuracy was compared using diagnostic measures such as sensitivity, specificity, positive predictive value, accuracy, negative predictive value, and area under the receiver operating characteristic curve (ROC). The analyses were performed using STATA 17 and R4.0.4 software. Results: The study revealed that the best neural network model had a sensitivity of 74.5% (95% CI: 61.0-85.0), specificity of 83.3% (65.3-94.4), positive predictive value of 89.1% (76.4-96.4), negative predictive value of 64.1% (47.2-78.8), AUC of 0.79 (0.70-0.88), and accuracy of 0.776 for death prediction. For recurrence, the best neural network model had a sensitivity of 88.1% (74.4%-96.0%), specificity of 83.7% (69.3%-93.2%), positive predictive value of 84.1% (69.9%-93.4%), negative predictive value of 87.8% (73.8%-95.9%), AUC of 0.86 (0.78-0.93), and accuracy of 0.859. The Cox model had comparable results, with a sensitivity of 73.6% (64.8-81.2) and 85.5% (78.3-91.0), specificity of 89.6% (83.8-93.8) and 98.0% (94.4-99.6), positive predictive value of 84.0% (75.6-90.4) and 97.4% (92.6-99.5), negative predictive value of 82.0% (75.6-90.4) and 88.8% (0.83-93.1), AUC of 0.82 (0.77-0.86) and 0.92 (0.89-0.95), and accuracy of 0.88 and 0.92 for death and recurrence prediction, respectively. Conclusion: In conclusion, the study found that both Cox regression and neural network models are effective in predicting early recurrence and death in patients with colorectal cancer after curative surgery. The neural network model showed slightly better sensitivity and negative predictive value for death, while the Cox model had better specificity and positive predictive value for recurrence. Overall, both models demonstrated high accuracy and AUC, indicating their usefulness in predicting these outcomes.

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Section: Discussionmentioning

confidence: 99%

Beyond Traditional Methods: Unleashing the Power of Neural Networks in Predicting Early Recurrence and Mortality after Curative Surgery in Colorectal Cancer Patients

Alinia

Mahmoudi

Norouzi

et al. 2023

Preprint

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“…We suggest that through using the model developed in the present study, surgeons could better assess the risk of individual postoperative complications and may match interventions for patients who need more intensive treatment. To the best of our knowledge, our study is the first to specifically concentrate on a nomogram for predicting postoperative complications after emergency surgery for colon cancer, which is unlike the current nomograms for colon cancer that mainly focus on the evaluation of the overall survival prognosis, from an oncological point of view [ 40 ], or focus on the prediction of anastomosis fistula after colic resections [41].…”

Section: Discussionmentioning

confidence: 99%

Nomogram for Prediction of Postoperative Morbidity in Patients with Colon Cancer Requiring Emergency Therapy

et al. 2022

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Background Postoperative complications are the major cause of mortality and prolonged hospitalization after emergency surgery for colon cancer. This study aimed to propose an effective nomogram to predict postoperative complications in order to improve the outcomes. Material/Methods We retrospectively analyzed 449 patients who underwent emergency surgery for complicated colon cancer at the County Emergency Hospital Clinic “St. Apostle Andrei” in Galaţi, in the period from 2008 to 2017. Postoperative complications were intestinal obstruction, leakage, bleeding, peritonitis, wound infection, surgical wound dehiscence, respiratory failure, heart failure, acute renal failure, sepsis, and Clostridium difficile colitis, within a month after surgery. Logistic regression models were used to identify the independent prediction factors, and a nomogram was created, based on the best model. Results A total of 106 patients (21%) presented postoperative complications after emergency surgery for colon cancer; 51 patients (11.36%) died during the postoperative period. After identifying the risk factors through univariate regression analysis, we identified the independent prediction factors in 2 multivariate regression models. The model with the highest accuracy included the following 7 independent prediction factors: Eastern Cooperative Oncology Group performance status, Charlson score, white blood cell count, electrolyte and coagulation disorders, surgery time, and cachexia ( P <0.05 for all). This model showed good precision in predicting postoperative complications, with an area under curve of 0.83 and ideal accordance between the predicted and observed probabilities. Conclusions The nomogram developed in this study, which was based on a multivariate logistic regression model, had good individual prediction of postoperative complications.

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“…However, it ignores some factors that have been proved to be of prognostic value, which results in the prediction system having certain limitations. Therefore, some studies based on Cox proportional hazards regression have improved the accuracy of the clinical prognostic model by including more predictors ( 2 ) or adjusting the weight ( 3 ) of predictors in TNM stage. However, some studies have found that the models built using these linear methods ignore the time-dependent and nonlinear effects between CRC predictors and prognosis ( 4 ), which may cause the phenomenon of “survival paradox”, and result in clinicians being unable to accurately evaluate the prognosis of CRC patients ( 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Tang

Gao

et al. 2022

Front. Oncol.

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BackgroundTo construct prognostic model of colorectal cancer (CRC) recurrence and metastasis (R&M) with traditional Chinese medicine (TCM) factors based on different machine learning (ML) methods. Aiming to offset the defects in the existing model lacking TCM factors.MethodsPatients with stage I-III CRC after radical resection were included as the model data set. The training set and the internal verification set were randomly divided at a ratio of 7: 3 by the “set aside method”. The average performance index and 95% confidence interval of the model were calculated by repeating 100 tests. Eight factors were used as predictors of Western medicine. Two types of models were constructed by taking “whether to accept TCM intervention” and “different TCM syndrome types” as TCM predictors. The model was constructed by four ML methods: logistic regression, random forest, Extreme Gradient Boosting (XGBoost) and support vector machine (SVM). The predicted target was whether R&M would occur within 3 years and 5 years after radical surgery. The area under curve (AUC) value and decision curve analysis (DCA) curve were used to evaluate accuracy and utility of the model.ResultsThe model data set consisted of 558 patients, of which 317 received TCM intervention after radical resection. The model based on the four ML methods with the TCM factor of “whether to accept TCM intervention” showed good ability in predicting R&M within 3 years and 5 years (AUC value > 0.75), and XGBoost was the best method. The DCA indicated that when the R&M probability in patients was at a certain threshold, the models provided additional clinical benefits. When predicting the R&M probability within 3 years and 5 years in the model with TCM factors of “different TCM syndrome types”, the four methods all showed certain predictive ability (AUC value > 0.70). With the exception of the model constructed by SVM, the other methods provided additional clinical benefits within a certain probability threshold.ConclusionThe prognostic model based on ML methods shows good accuracy and clinical utility. It can quantify the influence degree of TCM factors on R&M, and provide certain values for clinical decision-making.

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Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer

Cited by 11 publications

References 29 publications

Beyond Traditional Methods: Unleashing the Power of Neural Networks in Predicting Early Recurrence and Mortality after Curative Surgery in Colorectal Cancer Patients

Beyond Traditional Methods: Unleashing the Power of Neural Networks in Predicting Early Recurrence and Mortality after Curative Surgery in Colorectal Cancer Patients

Nomogram for Prediction of Postoperative Morbidity in Patients with Colon Cancer Requiring Emergency Therapy

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

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