A competing risk nomogram to predict cancer-specific mortality of patients with late-onset colorectal cancer

Nomogram for predicting the development of pneumonia after colorectal cancer surgery

The aim of this study was to analyze the factors contributing to the development of pneumonia after colorectal cancer (CRC) surgery and to develop a validated nomogram to predict the risk. We retrospectively collected information on patients who underwent radical CRC resection at a single clinical center from January 2011 to December 2021. The information was then randomly assigned to a training cohort and a validation cohort in a 7:3 ratio. Univariate and multivariate logistic regression analysis were performed on the training cohort to identify independent risk factors for the development of pneumonias, which were then included in the nomogram. Validation was performed in a validation cohort, area under the curve (AUC) and calibration curves were used to determine the predictive accuracy and discriminative power of the graphs, and decision curve analysis (DCA) was used to further substantiate the clinical efficacy of the nomogram. A total of 7130 patients were included in the study. Based on multivariate logistic regression analysis of the training cohort, age, sex, preoperative albumin, surgical methods, and surgical time were identified as independent risk factors for the development of pneumonia after CRC surgery, and a nomogram prediction model was established using the above five variables. The AUC was 0.745 in training cohort and 0.773 in validation cohort. This study established a nomogram that is a good predictor of the risk of developing pneumonia after CRC surgery and provided surgeons with a reference for personalized management of patients in the perioperative period.

Conditional survival analysis and real-time prognosis prediction in stage III T3-T4 colon cancer patients after surgical resection: a SEER database analysis

BACKGROUND

Conditional survival (CS) takes into consideration the duration of survival post-surgery and can provide valuable additional insights. The aim of this study was to investigate the risk factors associated with reduced one-year postoperative conditional survival in patients diagnosed with stage III T3-T4 colon cancer and real-time prognosis prediction. Furthermore, we aim to develop pertinent nomograms and predictive models.

METHODS

Clinical data and survival outcomes of patients diagnosed with stage III T3-T4 colon cancer were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2019. Patients were divided into training and validation cohorts at a ratio of 7:3. The training set consisted of a total of 11,386 patients for conditional overall survival (cOS) and 11,800 patients for conditional cancer-specific survival (cCSS), while the validation set comprised 4876 patients for cOS and 5055 patients for cCSS. Univariate and multivariate Cox regression analyses were employed to identify independent risk factors influencing one-year postoperative cOS and cCSS. Subsequently, predictive nomograms for cOS and cCSS at 2-year, 3-year, 4-year, and 5-year intervals were constructed based on the identified prognostic factors. The performance of these nomograms was rigorously assessed through metrics including the concordance index (C-index), calibration curves, and the area under curve (AUC) derived from the receiver operating characteristic (ROC) analysis. Clinical utility was further evaluated using decision curve analysis (DCA).

RESULTS

A total of 18,190 patients diagnosed with stage III T3-T4 colon cancer were included in this study. Independent risk factors for one-year postoperative cOS and cCSS included age, pT stage, pN stage, pretreatment carcinoembryonic antigen (CEA) levels, receipt of chemotherapy, perineural invasion (PNI), presence of tumor deposits, the number of harvested lymph nodes, and marital status. Sex and tumor site were significantly associated with one-year postoperative cOS, while radiation therapy was notably associated with one-year postoperative cCSS. In the training cohort, the developed nomogram demonstrated a C-index of 0.701 (95% CI, 0.711-0.691) for predicting one-year postoperative cOS and 0.701 (95% CI, 0.713-0.689) for one-year postoperative cCSS. Following validation, the C-index remained robust at 0.707 (95% CI, 0.721-0.693) for one-year postoperative cOS and 0.700 (95% CI, 0.716-0.684) for one-year postoperative cCSS. ROC and calibration curves provided evidence of the model's stability and reliability. Furthermore, DCA underscored the nomogram's superior clinical utility.

CONCLUSIONS

Our study developed nomograms and predictive models for postoperative stage III survival in T3-T4 colon cancer with the aim of accurately estimating conditional survival. Survival bias in our analyses may lead to overestimation of survival outcomes, which may limit the applicability of our findings.