Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Xu, Yali; Chen, Yi; Long, Chenyan; Zhong, Haining; Liang, Fangfang; Huang, Lingling; Wei, Chuanyi; Lu, Shaolong; Tang, Weizhong

doi:10.3389/fonc.2021.667477

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…To facilitate clinicians to calculate the risk of LNM in the individual patient with cN0 CC, we constructed a nomogram incorporating PALR. After taking into account previous research results and the biology of CC ( 27 , 28 ), tumor grade was still included in our prediction model, although it was not an independent factor of LNM in our study. The indicators, including sex, T stage, grade, and PALR were selected in this model, as shown in Figure 5A .…”

Section: Resultsmentioning

confidence: 99%

A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer

Duan

Wang

2023

Front. Oncol.

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BackgroundThe purpose of this study is to investigate the predictive significance of (platelet × albumin)/lymphocyte ratio (PALR) for lymph node metastasis (LNM) in patients with clinically node-negative colon cancer (cN0 CC).MethodsData from 800 patients with primary CC who underwent radical surgery between March 2016 and June 2021 were reviewed. The non-linear relationship between PALR and the risk of LNM was explored using a restricted cubic spline (RCS) function while a receiver operating characteristic (ROC) curve was developed to determine the predictive value of PALR. Patients were categorized into high- and low-PALR cohorts according to the optimum cut-off values derived from Youden’s index. Univariate and multivariate logistic regression analyses were used to identify the independent indicators of LNM. Sensitivity analysis was performed to repeat the main analyses with the quartile of PALR.ResultsA total of eligible 269 patients with primary cN0 CC were retrospectively selected. The value of the area under the ROC curve for PALR for predicting LNM was 0.607. RCS visualized the uptrend linear relationship between PALR and the risk of LNM (p-value for non-linearity > 0.05). PALR (odds ratio = 2.118, 95% confidence interval, 1.182-3.786, p = 0.011) was identified as an independent predictor of LNM in patients with cN0 CC. A nomogram incorporating PALR and other independent predictors was constructed with an internally validated concordance index of 0.637. The results of calibration plots and decision curve analysis supported a good performance ability and the sensitivity analysis further confirmed the robustness of our findings.ConclusionPALR has promising clinical applications for predicting LNM in patients with cN0 CC.

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Section: Resultsmentioning

confidence: 99%

A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer

Duan

Wang

2023

Front. Oncol.

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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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“…CEA is currently the most common predictor of the risk of distant metastases in CRC [ 22 , 23 ], which was also demonstrated in our ROC analysis, and CEA was superior to other serum markers for the diagnosis of liver metastases in CC. Many studies have modeled line graphs with tumor biomarkers [ 11 , 24 ], and no published studies have addressed the relationship between DLR in the diagnosis and prognosis of CRC. This study is the first to reveal the relationship between the DLR and CRCLM.…”

Section: Discussionmentioning

confidence: 99%

D-dimer to lymphocyte ratio can serve as a potential predictive and prognostic value in colorectal cancer patients with liver metastases

Gong

et al. 2023

BMC Surg

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Background The intent of this research was to generate and investigate the D-dimer to lymphocyte ratio (DLR) capacity to forecast the risk and prognosis of colorectal cancer liver metastases (CRCLM). Methods From January 2010 to December 2019, 177 clinicopathologically confirmed colorectal cancer (CRC) patients (89 in the control group and 88 in the experimental group) were identified at the Affiliated Cancer Hospital of Guangxi Medical University. Multivariate Cox regression analysis was used to screen independent predictive diagnostic and prognostic factors of liver metastasis in CRC, and receiver operating characteristic (ROC) curves and Kaplan‒Meier (K‒M) curves were established to analyze the diagnostic and predictive prognostic efficacy of the DLR in the development of CRCLM. Results Patients with CRCLM had higher DLR levels and D-dimer levels in their blood, with statistically significant differences (p < 0.001). DLR might be employed as a predictor for the development of CRCLM, according to ROC curve research (sensitivity 0.670, specificity 0.775, area under the curve 0.765). D-dimer, lymphocyte count CEA, CA125, and CA199 were not linked to prognosis in patients with CRCLM in Cox regression analysis of dichotomous variables. In contrast, DLR level was a possible risk factor for the prognosis of patients with CRCLM (HR = 2.108, p = 0.047), and age, T stage, and DLR level (DLR < 0.4) were connected with the prognosis of patients with CRCLM (p < 0.05). Conclusion DLR serves as a risk indicator for the development of CRCLM.

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Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Cited by 7 publications

References 36 publications

A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer

A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer

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

D-dimer to lymphocyte ratio can serve as a potential predictive and prognostic value in colorectal cancer patients with liver metastases

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