Development and Validation of a Prognostic Nomogram for Colorectal Cancer Patients With Synchronous Peritoneal Metastasis

Yang, Zifeng; Yong, Li; Qin, Xiusen; Lv, Zejian; Wang, Huaiming; Wu, Deqing; Yuan, Zixu

doi:10.3389/fonc.2021.615321

Cited by 15 publications

(8 citation statements)

References 43 publications

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“…Nomograms, which integrate a variety of significant factors for individualized risk assessment, are extensively utilized in evaluating the prognosis of colorectal cancer (CRC) patients [ 34 – 37 ]. By employing multifactor logistic regression analysis, we identified independent predictive factors associated with perineural invasion (PNI) in CRC patients and based on these factors, developed a nomogram.…”

Section: Discussionmentioning

confidence: 99%

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

Que,

Wu,

et al. 2024

BMC Surg

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Background Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). Materials and methods A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures. Results The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03–0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21–5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22–0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74–6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60–6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08–3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712–0.833). The Hosmer–Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation. Conclusion We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

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Section: Discussionmentioning

confidence: 99%

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

Que,

Wu,

et al. 2024

BMC Surg

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“…This particular factor constitutes a pivotal contributor to unfavorable outcomes [18,19] . Nomograms, adept at amalgamating a plethora of pivotal factors, offer personalized risk assessment and have gained extensive traction for prognostic evaluations among colorectal cancer patients [20][21][22][23] . In the current study, we harnessed the potency of multivariate logistic regression analysis to pinpoint independent predictive factors intricately linked to PNI among CRC patients.…”

Section: Discussionmentioning

confidence: 99%

A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study

Que,

Wu,

et al. 2023

Preprint

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Background Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). Materials and methods A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures. Results The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03–0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21–5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22–0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74–6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60–6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08–3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712–0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13–85%, reaffirming the nomogram's reliability through rigorous internal validation. Conclusion We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

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“…Previous studies have disclosed considerable prognostic factors for CRC survival, such as age [ 35 ], chemotherapy [ 35 ], BMI [ 36 ], histology type [ 37 ], TNM stage [ 37 ], differentiation grade [ 38 ], C-reactive protein [ 39 ], total bile acid [ 40 ], direct bilirubin [ 40 ], neutrophils [ 41 ], and so forth. Therefore, we comprehensively collected the baseline characteristics of the development cohort.…”

Section: Discussionmentioning

confidence: 99%

Mid-Arm Muscle Circumference or Body Weight-Standardized Hand Grip Strength in the GLIM Superiorly Predicts Survival in Chinese Colorectal Cancer Patients

Xu²,

Zou³

et al. 2022

Nutrients

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Our objective was to identify the optimal method to assess reduced muscle mass (RMM) using the Global Leadership Initiative on Malnutrition (GLIM) approach and investigate the roles of the GLIM approach in nutrition assessment and survival prediction in colorectal cancer (CRC) patients. During a median follow-up period of 4.2 (4.0, 4.4) years, a development cohort of 3612 CRC patients with a mean age of 64.09 ± 12.45 years was observed, as well as an external validation cohort of 875 CRC patients. Kaplan–Meier curves and multivariate Cox regression were adopted to analyze the association between GLIM-diagnosed malnutrition and the overall survival (OS) of CRC patients. A nomogram predicting individualized survival was constructed based on independent prognostic predictors. The concordance index, calibration curve, and decision curve were applied to appraise the discrimination, accuracy, and clinical efficacy of the nomogram, respectively. Patients diagnosed with severe malnutrition based on either the mid-arm muscle circumference (MAMC) or body weight-standardized hand grip strength (HGS/W) method had the highest mortality hazard ratio (HR, 1.51; 95% CI, 1.34–1.70; p < 0.001). GLIM-defined malnutrition was diagnosed in 47.6% of patients. Severe malnutrition was an independent mortality risk factor for OS (HR, 1.25; 95% CI, 1.10–1.42; p < 0.001). The GLIM nomogram showed good performance in predicting the survival of CRC patients and was clinically beneficial. Our findings support the effectiveness of GLIM in diagnosing malnutrition and predicting OS in CRC patients.

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Development and Validation of a Prognostic Nomogram for Colorectal Cancer Patients With Synchronous Peritoneal Metastasis

Cited by 15 publications

References 43 publications

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study

Mid-Arm Muscle Circumference or Body Weight-Standardized Hand Grip Strength in the GLIM Superiorly Predicts Survival in Chinese Colorectal Cancer Patients

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