A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer

Prognostic factors and survival disparities in right-sided versus left-sided colon cancer

Right-sided colon cancer (RCC) and left-sided colon cancer (LCC) differ in features and outcomes because of variations in embryology, epidemiology, pathology, and prognosis. This study sought to identify significant factors impacting patient survival through Bayesian modelling. Data was retrospectively analysed from a colorectal neoplasia database. Data on demographics, perioperative risks, treatment, mortality, and survival was analysed from patients who underwent colon cancer surgery from January 2010 to December 2021. This study involved 2475 patients, with 58.7% having RCC and 41.3% having LCC. RCC patients had a notably higher mortality rate, and their overall survival (OS) rates were slightly lower than those with LCC (P < 0.05). RCC stages I-IV consistently exhibited worse OS and relapse-free survival (RFS) than LCC (P < 0.05). Factors like age, BMI, ASA score, cancer stage, and comorbidities had significant associations with OS and RFS. Poor and moderate differentiation, lower lymph node yield, and organ resection were linked to lower survival while receiving chemotherapy; higher BMI levels and elective surgery were associated with better survival (all P < 0.05). Our study reveals key differences between RCC and LCC, emphasising the impact of age, BMI, ASA score, cancer stage, and comorbidities on patient survival. These findings could inform personalised treatment strategies for colon cancer patients.

Cumulative Incidence, Risk Factors, and Overall Survival of Disease Recurrence after Curative Resection of Stage II-III Colorectal Cancer: A Population-based Study

Real-world data are necessitated to counsel patients about the risk for recurrent disease after curative treatment of colorectal cancer. This study provided a population-based overview of the epidemiology of recurrent disease in patients with surgically resected stage II/III colorectal cancer.Patients diagnosed with stage II/III primary colorectal cancer between July and December 2015 were selected from the Netherlands Cancer Registry (N = 3,762). Cumulative incidence of recurrent disease was estimated, and multivariable competing risk regression was used to identify risk factors for recurrent disease in patients with primary colon and rectal cancer. Moreover, overall survival (OS) after diagnosis of recurrent colorectal cancer was estimated.Median clinical follow-up was 58 months (Q1-Q3: 22-62). Five-year cumulative incidence of recurrent disease was 21.6% [95% confidence interval (CI): 20.0-23.2] and 30.0% (95% CI: 28.3-33.5) for patients with primary colon and rectal cancer, respectively. Stage III disease and incomplete resection margin in patients with primary colon cancer and extramural vascular invasion in patients with primary rectal cancer were strongly (HR ≥ 2) associated with recurrent disease. Median OS of patients with distant, locoregional, or the synchronous combination of distant and locoregional recurrent disease was 29, 27, and 13 months, respectively (P < 0.001). Patients with distant recurrences limited to liver or lung showed a median OS of 46 and 48 months, respectively. The incidence of recurrent disease was higher in patients with rectal cancer than in patients with colon cancer, predominantly due to higher rates of distant recurrences. OS after recurrent disease was impaired, but subgroups of patients diagnosed with recurrent disease limited to one site showed statistically significantly longer OS.

SIGNIFICANCE

Population-based data on recurrent colorectal cancer are rare, but crucial for counseling patients and their physicians. This large nationwide, population-based study provides an up-to-date overview of the epidemiology of recurrent disease in patients with stage II and III primary colon and rectal cancer treated with surgical resection.

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

Colorectal cancer is a prevalent malignancy with global significance. This retrospective study aimed to investigate the influence of stage and tumor site on survival outcomes in 284 colorectal cancer patients diagnosed between 2001 and 2017. Patients were categorized into four groups based on tumor site (colon and rectum) and disease stage (early stage and advanced stage). Demographic characteristics, treatment modalities, and survival outcomes were recorded. Bayesian survival modeling was performed using semi-competing risks illness-death models with an accelerated failure time (AFT) approach, utilizing R 4.1 software. Results demonstrated significantly higher time ratios for disease recurrence (TR = 1.712, 95% CI 1.489-2.197), mortality without recurrence (TR = 1.933, 1.480-2.510), and mortality after recurrence (TR = 1.847, 1.147-2.178) in early-stage colon cancer compared to early-stage rectal cancer. Furthermore, patients with advanced-stage rectal cancer exhibited shorter survival times for disease recurrence than patients with early-stage colon cancer. The interaction effect between the disease site and cancer stage was not significant. These findings, derived from the optimal Bayesian log-normal model for terminal and non-terminal events, highlight the importance of early detection and effective management strategies for colon cancer. Early-stage colon cancer demonstrated improved survival rates for disease recurrence, mortality without recurrence, and mortality after recurrence compared to other stages. Early intervention and comprehensive care are crucial to enhance prognosis and minimize adverse events in colon cancer patients.

Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks

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. 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. The analyses were performed using STATA 17 and R4.0.4 software. 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. 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.

A novel risk prediction nomogram for early death in patients with resected synchronous multiple primary colorectal cancer based on the SEER database

BACKGROUND

Synchronous multiple primary colorectal cancer (SMPCC) involves the simultaneous occurrence of 2 or more independent primary malignant tumors in the colon or rectum. Although SMPCC is rare, it results in a higher incidence of postoperative complications and mortality compared to patients with single primary colorectal cancer (SPCRC).

METHODS

The clinical factors and survival outcomes of SMPCC patients registered on the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2017 were extracted. The patients were divided into the training and validation cohorts using a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors for early death. The performance of the nomogram was evaluated using the concordance index (C-index), calibration curves, and the area under the curve (AUC) of a receiver operating characteristics curve (ROC). A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram and standard TNM system.

RESULTS

A total of 4386 SMPCC patients were enrolled in the study and randomly assigned to the training (n = 3070) and validation (n = 1316) cohorts. The multivariate logistic analysis identified age, chemotherapy, radiotherapy, T stage, N stage, and M stage as independent risk factors for all-cause and cancer-specific early death. The marital status was associated with all-cause early death, and the tumor grade was associated with cancer-specific early death. In the training cohort, the nomogram achieved a C-index of 0.808 (95% CI, 0.784-0.832) and 0.843 (95% CI, 0.816-0.870) for all-cause and cancer-specific early death, respectively. Following validation, the C-index was 0.797 (95% CI, 0.758-0.837) for all-cause early death and 0.832 (95% CI, 0.789-0.875) for cancer-specific early death. The ROC and calibration curves indicated that the model had good stability and reliability. The DCA showed that the nomogram had a better clinical net value than the TNM staging system.

CONCLUSION

Our nomogram can provide a simple and accurate tool for clinicians to predict the risk of early death in SMPCC patients undergoing surgery and could be used to optimize the treatment according to the patient's needs.