Nomogram Predicting the Survival of Young-Onset Patients with Colorectal Cancer Liver Metastases

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.

Construction and validation of a nomogram for predicting overall survival of patients with stage III/IV early-onset colorectal cancer

Purpose

This study aimed to identify prognostic factors and develop a nomogram for predicting overall survival (OS) in stage III/IV early-onset colorectal cancer (EO-CRC).

Methods

Stage III/IV EO-CRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The datasets were randomly divided (2:1) into training and validation sets. A nomogram predicting OS was developed based on the prognostic factors identified by Cox regression analysis in the training cohort. Moreover, the predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Subsequently, the internal validation was performed using the validation cohort. Finally, a risk stratification system was established based on the constructed nomogram.

Results

Of the 10,387 patients diagnosed with stage III/IV EO-CRC between 2010 and 2015 in the SEER database, 8,130 patients were included. In the training cohort (n=3,071), sex, marital status, race/ethnicity, primary site, histologic subtypes, grade, T stage, and N stage were identified as independent prognostic variables for OS. The 1-, 3-, and 5-year area under the curve (AUC) values of the nomogram were robust in both the training (0.751, 0.739, and 0.723) and validation cohorts (0.748, 0.733, and 0.720). ROC, calibration plots, and DCA indicated good predictive performance of the nomogram in both the training and validation sets. Furthermore, patients were categorized into low-, middle-, and high-risk groups based on the nomogram risk score. Kaplan-Meier curve showed significant survival differences between the three groups.

Conclusion

We developed a prognostic nomogram and risk stratification system for stage III/IV EO-CRC, which may facilitate clinical decision-making and individual prognosis prediction.

Survival outcome and prognostic factors for early-onset and late-onset metastatic colorectal cancer: a population based study from SEER database

Colorectal cancer is the third most common cancer worldwide and there has been a concerning increase in the incidence rate of colorectal cancer among individuals under the age of 50. This study compared the survival outcome between early-onset and late-onset metastatic colorectal cancer to find the differences and identify their prognostic factors. We obtained patient data from SEER database. Survival outcome was estimated using Kaplan-Meier survival curves and compared using the log-rank test. Univariate and multivariate analyses were conducted utilizing COX models to identify their independent prognostic factors. A total of 10,036 early-onset metastatic colorectal (EOCRC) cancer patients and 56,225 late-onset metastatic colorectal cancer (LOCRC) patients between 2010 and 2019 were included in this study. EOCRC has more survival benefits than LOCRC. Tumor primary location (p < 0.001), the location of metastasis (p < 0.001) and treatment modalities (p < 0.001) affect the survival outcomes between these two groups of patients. Female patients had better survival outcomes in EOCRC group (p < 0.001), but no difference was found in LOCRC group (p = 0.57). In conclusion, our study demonstrated that EOCRC patients have longer survival time than LOCRC patients. The sex differences in survival of metastatic colorectal cancer patients are associated with patients' age. These findings contribute to a better understanding of the differences between metastatic EOCRC and LOCRC, and can help inform the development of more precise treatment guidelines to improve prognosis.

Nomogram for predicting occurrence and prognosis of liver metastasis in elderly colorectal cancer patients: a population-based study

Introduction

This study aimed to explore independent risk and prognostic factors in elderly patients with colorectal cancer liver metastasis (ECRLM) and generate nomograms for predicting the occurrence and overall survival (OS) rates of such patients.

Method

Elderly colorectal cancer patients (ECRC) from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. External validation relied on Chinese patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were employed to identify liver metastasis (LM) risk variables, which were used to create a nomogram to estimate LM probabilities in patients with ECRC. Univariate and multivariable Cox analyses were performed to identify prognostic variables and further derive nomograms that could predict the OS of patients with ERCLM. Differences in lifespan were assessed using the Kaplan-Meier analysis. Finally, the quality of the nomograms was verified using decision curve analysis (DCA), calibration curves, and receiver operating characteristic curves (ROC).

Result

In the SEER cohort, 32,330 patients were selected, of those, 3,012 (9.32%) were diagnosed with LM. A total of 188 ECRLM cases from a Chinese medical center were assigned for external validation. LM occurrence can be affected by 13 factors, including age at diagnosis, marital status, race, bone metastases, lung metastases, CEA level, tumor size, Grade, histology, primary site, T stage, N stage and sex. Furthermore, in ECRLM patients, 10 variables, including age at diagnosis, CEA level, tumor size, lung metastasis, bone metastasis, chemotherapy, surgery, N stage, grade, and race, have been shown to be independent prognostic predictors. The results from both internal and external validation revealed a high level of accuracy in predicting outcomes, as well as significant clinical utility, for the two nomograms.

Conclusion

We created two nomograms to predict the occurrence and prognosis of LM in patients with ECRC, which would contribute significantly to the improvement in disease detection accuracy and the formulation of personalized cures for that particular demographic.

Prognostic value of normal levels of preoperative tumor markers in colorectal cancer

Carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and alpha-fetoprotein (AFP) are widely used tumor markers for colorectal cancer (CRC), but their clinical significance is unknown when the levels of these tumor markers were within the normal range. This retrospective study included 2145 CRC patients. The entire cohort was randomly divided into training and validation datasets. The optimal cut-off values of tumor markers were calculated using X-tile software, and univariate and multivariate analyses were performed to assess its association with overall survival (OS). The nomogram model was constructed and validated. The entire cohort was randomly divided into a training dataset (1502 cases, 70%) and a validation dataset (643 cases,30%). Calculated from the training dataset, the optimal cut-off value was 2.9 ng/mL for CEA, 10.1 ng/mL for CA19-9, 13.4 U/mL for CA125, and 1.8 ng/mL for AFP, respectively. Multivariate analysis revealed that age, tumor location, T stage, N stage, preoperative CA19-9, and CA125 levels were independent prognostic predictors. Even within the normal range, CRC patients with relatively high levels of CA19-9 or CA125 worse OS compared to those with relatively low levels. Then, based on the independent prognostic predictors from multivariate analysis, two models with/without (model I/II) CA19-9 and CA125 were built, model I showed better prediction and reliability than model II. Within the normal range, relatively high levels of preoperative CA19-9 and CA125 were significantly associated with poor OS in CRC patients. The nomogram based on CA19-9 and CA125 levels showed improved predictive accuracy ability for CRC.

Prognostic value of primary tumor location in colorectal cancer: an updated meta-analysis

The clinical, histological, and molecular differences between right-sided colon cancer (RCC) and left-sided colon cancer (RCC) have received considerable attention. Over the past decade, many articles have been published concerning the association between primary tumor location (PTL) of colorectal cancer and survival outcomes. Therefore, there is a growing need for an updated meta-analysis integrating the outcomes of recent studies to determine the prognostic role of right vs left-sidedness of PTL in patients with colorectal cancer. We conducted a comprehensive database review using PubMed, SCOPUS, and Cochrane library databases from February 2016 to March 2023 for prospective or retrospective studies reporting data on overall survival (OS) and cancer-specific survival (CSS) of RCC compared with LCC. A total of 60 cohort studies comprising 1,494,445 patients were included in the meta-analysis. We demonstrated that RCC is associated with a significantly increased risk of death compared with LCC by 25% (hazard ratio (HR), 1.25; 95% confidence interval (CI), 1.19-1.31; I2 = 78.4%; Z = 43.68). Results showed that patients with RCC have a worse OS compared with LCC only in advanced stages (Stage III: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%; Stage IV: HR, 1.34; 95% CI 1.25-1.44; P < 0.0001; I2 = 69.2%) but not in primary stages (Stage I/II: HR, 1.275; 95% CI 1.16-1.4; P = 0.0002; I2 = 85.8%). Moreover, a meta-analysis of 13 studies including 812,644 patients revealed that there is no significant difference in CSS between RCC and LCC (HR, 1.121; 95% CI 0.97-1.3; P = 0.112). Findings from the present meta-analysis highlight the importance of PTL in clinical decision-making for patients with CRC, especially in advanced stages. We provide further evidence supporting the hypothesis that RCC and LCC are distinct disease entities that should be managed differently.