Construction of a nomogram to predict overall survival for patients with M1 stage of colorectal cancer: A retrospective cohort study

An internally and externally validated nomogram for predicting cancer-specific survival in octogenarians after radical resection for colorectal cancer

AIMS

We aimed to develop an elaborative nomogram that predicts cancer-specific survival (CSS) in American and Chinese octogenarians treated with radical resection for CRC.

METHODS

The patient data of newly diagnosed patients aged 80 years or older who underwent radical resection for CRC from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly divided into a training cohort and a validation cohort. The patients collected from our hospital were defined as the external validation cohort. Univariate and multivariate Cox regression was used to select independent predictive factors for the construction of a nomogram to predict 1-, 2- and 3-year CSS.

RESULTS

The multivariate Cox regression model identified age, T stage, N stage, perineural invasion, chemotherapy, tumour deposits, carcinoembryonic antigen level, number of lymph node metastases, and number of solid organ metastases as independent predictors of survival. The C-index of the nomogram for 1-, 2- and 3-year CSS was 0.758, 0.762, and 0.727, respectively, demonstrating significant clinical value and substantial reliability compared to the TNM stage. The calibration curve and area under the curve also indicated considerable predictive accuracy. In addition, decision curve analysis demonstrated desirable net benefits in clinical application.

CONCLUSION

We constructed a nomogram for predicting the CSS of individual octogenarian patients with CRC who underwent radical resection. The nomogram performed better than the TNM staging system in this particular population and could guide clinicians in clinical follow-up and individual therapeutic plan formulation.

Development and validation of comprehensive nomograms from the SEER database for predicting early mortality in metastatic rectal cancer patients

BACKGROUND

Metastatic rectal cancer is an incurable malignancy, which is prone to early mortality. We aimed to establish nomograms for predicting the risk of early mortality in patients with metastatic rectal cancer.

METHODS

In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database.We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Significant independent risk factors for all-cause and cancer-specific early mortality were determined by the univariate and multivariate logistic regression analyses, then we construct two practical nomograms. In order to assess the predictive performance of nomograms, we performed calibration plots, time-dependent receiver-operating characteristic curve (ROC), decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

A total of 2570 metastatic rectal cancer patients were included in the study. Multivariate logistic regression analyses revealed that age at diagnosis, CEA level, tumor size, surgical intervention, chemotherapy, radiotherapy, and metastases to bone, brain, liver, and lung were independently associated with early mortality of metastatic rectal cancer patients in the training cohort. The area under the curve (AUC) values of nomograms for all-cause and cancer-specific early mortality were all higher than 0.700. Calibration curves indicated that the nomograms accurately predicted early mortality and exhibited excellent discrimination. DCA and CIC showed moderately positive net benefits.

CONCLUSIONS

This study successfully generated applicable nomograms that predicted the high-risk early mortality of metastatic rectal cancer patients, which can assist clinicians in tailoring more effective treatment regimens.

Construction of the survival nomograms for colon cancer patients of different ages based on the SEER database

INTRODUCTION

Three nomograms for predicting the outcomes of early- and late-onset colon cancer (COCA) among patients not stratified by age were constructed using data in the Epidemiology and End Results (SEER) database (1975-2019). The accuracy of the nomogram was then assessed.

METHOD

Clinical data of 6107 patients with COCA were obtained from the SEER database. The patients were randomly divided into training and validation cohorts in a ratio of 7:3. Univariate and multivariate COX analyses of factors that could independently impact the prognosis of COCA were performed, and the corresponding nomograms for early-onset and late-onset COCA were constructed. Calibration curves, ROC curves, and C-index were used to determine the predictive accuracy. The discriminatory ability of the nomograms to assess their clinical utility, which was compared with the TNM staging system of the 8th edition of AJCC, was verified using survival analysis.

RESULT

Tumor primary site, ethnicity, and serum carcinoembryonic antigen (CEA) level significantly impacted the prognosis of colon cancer. Race, brain metastasis, and CEA were independent factors for predicting COCA prognosis. C-index, ROC, and calibration curves demonstrated that the three nomograms were accurate and superior to the traditional TNM staging system. Among the three nomograms, the early-onset COCA nomogram had the highest predictive accuracy, followed by that of colon cancer not stratified by age.

CONCLUSION

Three nomograms for patients not stratified by age, early-onset colon cancer, and late-onset colon cancer were constructed. The accuracies of the nomograms were good and were all superior to the conventional TNM staging system. The early- and late-onset COCA nomograms are useful for clinical management and individualized treatment of COCA patients at different ages.

Correlations between Demographic, Clinical, and Paraclinical Variables and Outcomes in Patients with KRAS-Mutant or KRAS Wild-Type Metastatic Colorectal Cancer-A Retrospective Study from a Tertiary-Level Center in Romania

Colorectal cancer (CRC) is a significant global public health concern and its characteristics in Eastern Europe are underexplored. In this retrospective study, data of 225 patients with metastatic colorectal cancer (mCRC) from the Colțea Clinical Hospital's Oncology Department in Bucharest were analyzed between 2015 and 2023. They were divided into two groups based on the presence of KRAS mutation. The primary objective of the study was to investigate whether the presence of KRAS mutations influenced the prognosis of mCRC and to identify any demographic, clinical, or paraclinical factors associated with KRAS mutations in stage IV CRC. The overall survival for the entire study population was 29 months. There was a trend towards increased survival in the KRAS wild-type group (31 months) compared to the KRAS-mutant group (26 months), but this difference did not reach statistical significance. We found that lower levels of education, advanced T stage, advanced N stage, and M1 stage at diagnosis negatively impacted prognosis. Real-world data are crucial in shaping public policy strategies to better support patients with metastatic CRC. Understanding the correlations between the demographic, clinical, and paraclinical variables and the outcomes in mCRC patients with KRAS-mutant and KRAS wild-type colorectal cancer is essential for improving patient care and treatment strategies in Romania and beyond.

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Purpose

The purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC.

Method

Medical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models.

Results

The optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC.

Conclusion

ML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.

Nomogram for predicting overall survival time of patients with stage IV colorectal cancer

BACKGROUND

Prognosis varies among stage IV colorectal cancer (CRC). Our study aimed to build a robust prognostic nomogram for predicting overall survival (OS) of patients with stage IV CRC in order to provide evidence for individualized treatment.

METHOD

We collected the information of 16,283 patients with stage IV CRC in the Surveillance, Epidemiology, and End Results (SEER) database and then randomized these patients in a ratio of 7:3 into a training cohort and an internal validation cohort. In addition, 501 patients in the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) database were selected and used as an external validation cohort. Univariate and multivariate Cox analyses were used to screen out significant variables for nomogram establishment. The nomogram model was assessed using time-dependent receiver-operating characteristic curve (time-dependent ROC), concordance index (C-index), calibration curve, and decision curve analysis. Survival curves were plotted using the Kaplan-Meier method.

RESULT

The C-index of the nomogram for OS in the training, internal validation, and external validation cohorts were 0.737, 0.727, and 0.655, respectively. ROC analysis and calibration curves pronounced robust discriminative ability of the model. Further, we divided the patients into a high-risk group and a low-risk group according to the nomogram. Corresponding Kaplan-Meier curves showed that the prediction of the nomogram was consistent with the actual practice. Additionally, model comparisons and decision curve analysis proved that the nomogram for predicting prognosis was significantly superior to the tumor-node-metastasis (TNM) staging system.

CONCLUSIONS

We constructed a nomogram to predict OS of the stage IV CRC and externally validate its generalization, which was superior to the TNM staging system.

Survival Nomogram for Metastasis Colon Cancer Patients Based on SEER Database

Introduction: A prediction model for the 1-, 3-, and 5-year survival rates of metastatic colon cancer (mCC) patients was developed by analyzing important risk factors for the prognosis of mCC patients based on the SEER database.

Method: The characteristic of 10,946 patients diagnosed with mCC between 2010 and 2015 was obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio. Univariate and multivariate cox for independent predictors of mCC prognosis were performed, and nomogram was constructed. The accuracy of the model was verified by calibration curves, ROC curves, and C-index, and the clinical utility of the model was analyzed using decision analysis curves.

Result: Age, primary site, grade, surgery, and other eight factors were significantly associated with the prognosis of mCC patients, and these predictors were included in the construction of the nomogram. The C-index was 0.731 (95% CI 0.725–0.737) and 0.736 (95% CI 0.726–0.746) for the training cohort and the validation set, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) exceeded 0.7 for both the training cohort and the validation set at 1, 3, and 5 years.

Conclusion: The constructed prediction model had an excellent predictive accuracy, which will help clinical decision-making of mCC patients after surgery and individualized treatment.

Prognostic nomograms for predicting overall survival and cause-specific survival of signet ring cell carcinoma in colorectal cancer patients

BACKGROUND

Signet ring cell carcinoma (SRCC) is an uncommon subtype in colorectal cancer (CRC), with a short survival time. Therefore, it is imperative to establish a useful prognostic model. As a simple visual predictive tool, nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years. Until now, there has been no nomogram to predict the outcome of CRC patients with SRCC.

AIM

To build effective nomograms for predicting overall survival (OS) and cause-specific survival (CSS) of CRC patients with SRCC.

METHODS

Data were extracted from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms. Performance of the nomograms was assessed by concordance index, calibration curves, and receiver operating characteristic (ROC) curves. ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis (TNM) staging system. Patients were classified as high-risk, moderate-risk, and low-risk groups using the novel nomograms. Kaplan-Meier curves were plotted to compare survival differences.

RESULTS

In total, 1230 patients were included. The concordance index of the nomograms for OS and CSS were 0.737 (95% confidence interval: 0.728-0.747) and 0.758 (95% confidence interval: 0.738-0.778), respectively. The calibration curves and ROC curves demonstrated good predictive accuracy. The 1-, 3-, and 5-year area under the curve values of the nomogram for predicting OS were 0.796, 0.825 and 0.819, in comparison to 0.743, 0.798, and 0.803 for the TNM staging system. In addition, the 1-, 3-, and 5-year area under the curve values of the nomogram for predicting CSS were 0.805, 0.847 and 0.863, in comparison to 0.740, 0.794, and 0.800 for the TNM staging system. Based on the novel nomograms, stratified analysis showed that the 5-year probability of survival in the high-risk, moderate-risk, and low-risk groups was 6.8%, 37.7%, and 67.0% for OS (P < 0.001), as well as 9.6%, 38.5%, and 67.6% for CSS (P < 0.001), respectively.

CONCLUSION

Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC, which are superior to the conventional TNM staging system.

Nomogram for predicting overall survival in colorectal cancer with distant metastasis

Background

Colorectal cancer (CRC) is a major cancer burden, and prognosis is determined by many demographic and clinicopathologic factors. The present study aimed to construct a prognostic nomogram for colorectal cancer patients with distant metastasis.

Methods

Colorectal cancer patients with distant metastasis diagnosed between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression was used to identify independent prognostic factors. A nomogram was constructed to predict survival, and validation was performed.

Results

A total of 7099 stage IV colorectal cancer patients were enrolled in the construction cohort. The median overall survival was 20.0 (95% CI 19.3–20.7) months. Age at diagnosis, marital status, race, primary tumour site, tumour grade, CEA level, T stage, N stage, presence of bone, brain, liver and lung metastasis, surgery for primary site and performance of chemotherapy were independent prognostic factors. The nomogram was constructed and the calibration curve showed satisfactory agreement. The C-index was 0.742 (95% CI 0.726–0.758). In the validation cohort (7098 patients), the nomogram showed satisfactory discrimination and calibration with a C-index of 0.746 (95% CI 0.730–0.762).

Conclusion

A series of factors associated with the survival of CRC patients with distant metastasis were found. Based on the identified factors, a nomogram was generated to predict the survival of stage IV colorectal cancer patients. The predictive model showed satisfactory discrimination and calibration, which can provide a reference for survival estimation and individualized treatment decisions.

Survival outcome and prognostic factors for colorectal cancer with synchronous bone metastasis: a population-based study

Prognostic factors of synchronous bone metastatic colorectal cancer (CRC) are still undetermined. We aimed to investigate survival outcome and prognostic factors of patients with synchronous bone metastatic CRC. Information of patients with synchronous bone metastatic CRC were obtained from the Surveillance, Epidemiology, and End Results (SEER) and West China Hospital (WCH) databases. Cases from SEER database composed construction cohort, while cases from WCH database were used as validation cohort. A novel nomogram was constructed to predict individual survival probability based on Cox regression model. The performance of the nomogram was internally and externally validated using calibration curves and concordance index (C-index). Three hundred and eighty-one patients from SEER database were eligible. The median disease specific OS was 9.0 months (95% confidence interval [CI]: 7.3-10.7 months). Multivariate Cox analysis identified seven independent prognostic factors including histological type, differentiation grade, T stage of primary tumor, CEA level, systemic chemotherapy, combined with liver metastasis and combined with lung metastasis. A novel nomogram was established based on these variables. In the internal validation, the C-index (0.72, 95% CI 0.69-0.75) and calibration curve indicated well performance of this nomogram at predicting survival outcome in bone metastatic CRC. In the external validation, the C-index was 0.57 (95% CI 0.46-0.68). The prognosis of synchronous bone metastatic CRC is very poor. Histological type, differentiation grade, T stage of primary tumor, CEA level, systemic chemotherapy, combined with liver metastasis and combined with lung metastasis are independent prognostic factors. Further study is warranted to confirm the practicality of the prognostic nomogram.

Oncological and prognostic impact of lymphovascular invasion in Colorectal Cancer patients

Objectives: Lymphovascular invasion (LVI) is correlated with unfavorable prognoses in several types of cancers. We aimed to identify the informative features associated with LVI and to determine its prognostic value in colorectal cancer (CRC) patients. Methods: We retrospectively analyzed 1,474 CRC patients admitted in Wuhan Union Hospital between 2013 and 2017 as the development cohort and 549 CRC patients from The Cancer Genome Atlas (TCGA) database as the validation cohort. Logistical and Cox regression analyses were conducted to determine the oncological and prognostic significance of LVI in CRC patients. A survival nomogram based on LVI status was established using the Wuhan Union cohort and validated using TCGA cohort. Results: The LVI detection rates were 21.64% in the Wuhan Union cohort and 35.15% in TCGA cohort. LVI was closely correlated with advanced T stage, N stage, and TNM stage. LVI positivity was an independent biomarker for unfavorable overall survival (hazard ratio [HR]=2.25, 95% confidence interval [CI]=1.70-2.96, P<0.0001) and worse disease-free survival (HR=2.34, 95% CI=1.76-3.12, P<0.0001) in CRC patients. The survival nomogram incorporating LVI exhibited good predictive performance and reliability in the Wuhan Union cohort and TCGA cohort. Conclusion: LVI is a significant indicator of advanced stage and is remarkably correlated with worse prognosis in CRC patients. The survival nomogram incorporating LVI may assist clinicians to better strategize the therapeutic options for patients with CRC.

Prediction of mortality in metastatic colorectal cancer in a real-life population: a multicenter explorative analysis

BACKGROUND

Metastatic colorectal cancer (mCRC) remains a lethal disease. Survival, however, is increasing due to a growing number of treatment options. Yet due to the number of prognostic factors and their interactions, prediction of mortality is difficult. The aim of this study is to provide a clinical model supporting prognostication of mCRC mortality in daily practice.

METHODS

Data from 1104 patients with mCRC in three prospective cancer datasets were used to construct and validate Cox models. Input factors for stepwise backward method variable selection were sex, RAS/BRAF-status, microsatellite status, treatment type (no treatment, systemic treatment with or without resection of metastasis), tumor load, location of primary tumor, metastatic patterns and synchronous or metachronous disease. The final prognostic model for prediction of survival at two and 3 years was validated via bootstrapping to obtain calibration and discrimination C-indices and dynamic time dependent AUC.

RESULTS

Age, sidedness, number of organs with metastases, lung as only site of metastasis, BRAF mutation status and treatment type were selected for the model. Treatment type had the most prominent influence on survival (resection of metastasis HR 0.26, CI 0.21-0.32; any treatment vs no treatment HR 0.31, CI 0.21-0.32), followed by BRAF mutational status (HR 2.58, CI 1.19-1.59). Validation showed high accuracy with C-indices of 72.2 and 71.4%, and dynamic time dependent AUC's of 76.7 ± 1.53% (both at 2 or 3 years), respectively.

CONCLUSION

The mCRC mortality prediction model is well calibrated and internally valid. It has the potential to support both, clinical prognostication for treatment decisions and patient communication.

Development and validation of prognostic nomogram for germ cell testicular cancer patients

The purpose of our study was to establish a reliable and practical nomogram based on significant clinical factors to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with germ cell testicular cancer (GCTC). Patients diagnosed with GCTC between 2004 and 2015 were obtained from the SEER database. Nomograms were constructed using the R software to predict the OS and CSS probabilities and the constructed nomograms were validated and calibrated. A total of 22,165 GCTC patients were enrolled in the study, including the training cohort (15,515 patients) and the validation cohort (6,650 patients). In the training cohort, multivariate Cox regression showed that age, race, AJCC stage, SEER stage and surgery were independent prognostic factors for OS, while age, race, AJCC stage, TM stage, SEER stage and radiotherapy were independent prognostic factors for CSS. Based on the above Cox regression results, we constructed prognostic nomograms of OS and CSS in GCTC patients and found that the OS nomograms had higher C-index and AUC compared to TNM stage in the training and validation cohorts. In addition, in the training and external validation cohorts, the calibration curves showed a good consistency between the predicted and actual 3-, 5- and 10-year OS and CSS rates of the nomogram. The current prognostic nomogram can provide a personalized risk assessment for the survival of GCTC patients.

Marital status and survival of patients with colorectal signet ring cell carcinoma: a population-based study

The prognostic role of marital status on colorectal signet ring cell carcinoma (SRCC) has not been studied. In this study, the correlation of marital status with prognosis of colorectal SRCC was analyzed. Eligible subjects were extracted from the Surveillance, Epidemiology, and End Results (SEER) dataset from 2004 to 2015, followed by comparison of cancer-specific survival (CSS) and overall survival (OS) between married and unmarried group. 3152 patients were identified including 1777 married patients (56.38%). Married populations tended to be more patients aged < 65, male, receiving chemotherapy, and less black race and large tumor size compared to unmarried group (all P < 0.05).Moreover, 5-year CSS (30.04% vs. 28.19%, P = 0.0013) and OS rates (26.68% vs. 22.94%, P < 0.0001) were superior in married population. Multivariate analysis revealed that marital status was an independent favorable prognostic indicator, and married population had better CSS (HR: 0.898; 95% CI: 0.822–0.980; P = 0.016) and OS (HR: 0.898; 95%CI: 0.827–0.975; P = 0.011).In addition, CSS as well as OS were superior in married populations than unmarried ones in most subgroups. Marital status was an independent prognostic factor for survival in patients with colorectal SRCC. Additionally, married patients obtained better survival advantages.