Dataset Information

A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study.

ABSTRACT:

Background

Precisely predicting the short- and long-term survival of patients with cancer is important. The tumor-node-metastasis (TNM) stage can accurately predict the long-term, but not short-term, survival of cancer. Nutritional status can affect the individual status and short-term outcomes of patients with cancer. Our hypothesis was that incorporating TNM stage and nutrition-related factors into one nomogram improves the survival prediction for patients with colorectal cancer (CRC).

Method

This multicenter prospective primary cohort included 1373 patients with CRC, and the internal validation cohort enrolled 409 patients with CRC. Least absolute shrinkage and selection operator regression analyses were used to select prognostic indicators and develop a nomogram. The concordance (C)-index, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the prognostic discriminative ability of the nomogram, TNM stage, Patient-Generated Subjective Global Assessment (PGSGA), and TNM stage + PGSGA models. The overall survival (OS) curve of risk group stratification was calculated based on the nomogram risk score.

Results

TNM stage, radical resection, reduced food intake, activities and function declined, and albumin were selected to develop the nomogram. The C-index and calibration plots of the nomogram showed good discrimination and consistency for CRC. Additionally, the ROC curves and DCA of the nomogram showed better survival prediction abilities in CRC than the other models. The stratification curves of the different risk groups of the different TNM categories were significantly different.

Conclusion

The novel nomogram showed good short- and long-term outcomes of OS in patients with CRC. This model provides a personalized and convenient prognostic prediction tool for clinical applications.

SUBMITTER: Ruan GT

PROVIDER: S-EPMC9814216 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study.

Ruan Guo-Tian GT Song Meng-Meng MM Zhang Kang-Ping KP Xie Hai-Lun HL Zhang Qi Q Zhang Xi X Tang Meng M Zhang Xiao-Wei XW Ge Yi-Zhong YZ Yang Ming M Zhu Li-Chen LC Shi Han-Ping HP

Nutrition & metabolism 20230104 1

<h4>Background</h4>Precisely predicting the short- and long-term survival of patients with cancer is important. The tumor-node-metastasis (TNM) stage can accurately predict the long-term, but not short-term, survival of cancer. Nutritional status can affect the individual status and short-term outcomes of patients with cancer. Our hypothesis was that incorporating TNM stage and nutrition-related factors into one nomogram improves the survival prediction for patients with colorectal cancer (CRC). ...[more]

PMID: 36600242

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Project description:BackgroundAlthough the incidence of late-onset colorectal cancer (LOCRC) has decreased, the incidence of early-onset colorectal cancer (EOCRC) is still rising dramatically. Heterogeneity in the genomic, biological, and clinicopathological characteristics between EOCRC and LOCRC has been revealed. Therefore, the previous prognostic models based on the total CRC patient population might not be suitable for EOCRC patients. Here, we constructed a prognostic classifier to enhance the precision of individualized treatment and management of EOCRC patients.MethodsEOCRC expression data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The regulatory pathways were explored by gene set enrichment analysis (GSEA). The prognostic model was developed by univariate Cox-LASSO-multivariate Cox regression analyses of GEO samples. TCGA samples were used to verify the model. The expression and mutation profiles and immune landscape of the high-risk and low-risk cohorts were analyzed and compared. Finally, the expression and prognostic value of the model genes were verified by immunohistochemistry and qRT‒PCR analysis.ResultsThe cell cycle was identified as the most significantly enriched oncological signature of EOCRC. Then, a 4-gene prognostic signature comprising MCM2, INHBA, CGREF1, and KLF9 was constructed. The risk score was an independent predictor of overall survival. The area under the curve values of the classifier for 1-, 3-, and 5-year survival were 0.856, 0.893, and 0.826, respectively, in the training set and 0.749, 0.858, and 0.865, respectively, in the validation set. Impaired DNA damage repair capability (p < 0.05) and frequent PIK3CA mutations (p < 0.05) were found in the high-risk cohort. CD8 T cells (p < 0.05), activated memory CD4 T cells (p < 0.01), and activated dendritic cells (p < 0.05) were clustered in the low-risk group. Finally, we verified the expression of MCM2, INHBA, CGREF1, and KLF9. Their prognostic value was closely related to age.ConclusionIn this study, a robust prognostic classifier for EOCRC was established and validated. The findings may provide a reference for individualized treatment and medical decision-making for patients with EOCRC.

Project description:ObjectiveTo develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC).Methods357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor.ResultsThe rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively.ConclusionBoth 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance.Advances in knowledgeThis is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.

Project description:BackgroundFruquintinib is a third-line and subsequent targeted therapy for patients with metastatic colorectal cancer (mCRC). Identifying survival predictors after fruquintinib is crucial for optimizing the clinical use of this medication.ObjectivesWe aimed to identify factors influencing the prognosis of patients with mCRC treated with fruquintinib and to leverage these insights to develop a nomogram model for estimating survival rates in this patient population.DesignMulticenter retrospective observational study.MethodsWe collected patient data from January 2019 to October 2023, with one healthcare institution's data serving as the training cohort and the other three hospitals' data serving as the multicenter validation cohort. The nomogram for overall survival was calculated from Cox regression models, and variable selection was screened using the univariate Cox regression analysis with additional variables based on clinical experience. Model performance was measured by the concordance index (C-index), calibration curves, decision curve analyses (DCA), and utility (patient stratification into low-risk vs high-risk groups).ResultsData were ultimately collected on 240 patients, with 144 patients included in the training cohort and 96 included in the multicenter validation cohort. Predictors included in the nomogram were CA199, body mass index, T stage, the primary site of the tumor, and other metastatic and pathological differentiation. The C-index of the nomogram in the training set and multicenter validation was 0.714 and 0.729, respectively. The models were fully calibrated and their predictions aligned closely with the observed data. DCA curves indicated the promising clinical benefits of the predictive model. Finally, the reliability of the model was also verified through the risk classification using the nomogram.ConclusionsWe constructed a nomogram for mCRC treated with fruquintinib based on six variables that may be used to assist in personalizing the use of the drug.

Dataset Information

A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study.

Background

Method

Results

Conclusion

Publications

A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study.

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