A Noninvasive Prediction Nomogram for Lymph Node Metastasis of Hepatocellular Carcinoma Based on Serum Long Noncoding RNAs

Ma, Jie; Zhang, Li; Bian, Hairong; Lu, Zhen-Guo; Zhu, Lian; Yang, Ping; Zeng, Zhao‐Chong; Xiang, Zuo-Lin

doi:10.1155/2019/1710670

Cited by 10 publications

(13 citation statements)

References 36 publications

(37 reference statements)

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“…Compared with hematogenous metastasis, lymph node metastasis (LNM) is a rare but equally vital prognostic factor. The reported incidence of LNM varies, from 1.2% to 15.3% in clinical practice and up to 30.3% in autopsy (10)(11)(12)(13)(14)(15)(16)(17)(18). Because of the dismal prognoses, patients with LNM would be considered to have a systemic disease and classified into advanced stages, including stage IVa in the American Joint Committee on Cancer (AJCC) staging system, stage IIIb in the China Liver Cancer (CNLC) staging system and stage C in the Barcelona Clinic Liver Cancer (BCLC) staging system (6,19).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Chen

Shi

et al. 2022

Front. Oncol.

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BackgroundThe evaluation of the nodal status of hepatocellular carcinoma (HCC) is a classic but controversial topic. This study aimed to investigate the incidence of lymph node metastasis (LNM), explore the role of lymph node dissection (LND), and develop and validate a novel model to predict LNM in patients with HCC, not other specified (NOS).MethodsThe study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) was calculated using the Joinpoint regression. Survival analyses adopted the competing risk model. The nomogram was constructed based on the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and validated by calibration curves. The area under the receiver operating characteristic curve (AUROC) was obtained to compare prognostic performance. Decision curve and clinical impact curve analyses were introduced to examine the clinical value of the models.ResultsA total of 8,829 patients were finally enrolled in this study, and 1,346 (15.2%) patients received LND. The LND rate showed no noticeable fluctuation in the last decade, with an APC of 0.5% (P=0.593). LNM was identified in 56 (4.2%) patients and confirmed an independent prognostic factor of HCC patients (P=0.005). There were 2,497 lymph nodes retrieved, and 93 (3.7%) of them were positive. After propensity score matching, LND indicated no direct oncologic benefit and did not worsen competing risks. Moreover, an increased number of lymph nodes retrieved could not improve prognoses. 1,346 patients with LND were further randomly divided into the training and validation sets with the ratio of 1:1. Race, tumor size, clinical T stage, extrahepatic bile duct invasion, and tumor grade were independent risk factors for LNM. The constructed model was well calibrated and showed good discrimination power and net benefits in clinical practice.ConclusionLNM is an independent prognostic factor in HCC, but routine LND seems to be unnecessary in HCC patients. The constructed model could predict the presence of LNM in HCC patients with good performance, which is meaningful to patient stratification and individual treatment strategies optimization.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Chen

Shi

et al. 2022

Front. Oncol.

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“…Nomograms are a graphic description of a predictive model derived from personal predictive information that can be used to evaluate a numerical probability of events such as survival and mortality [ 4 ]. Ma et al [ 9 ] developed a nomogram based on serum lncRNA to identify the biomarkers for diagnostic and treatment of HCC. Chen et al [ 10 ] developed and verified a simple to use nomogram to predict the early survival of HCC for clinicians to promote communication with patients and the personalized evaluation after surgery.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Nomograms to Predict the Cancer-Specific Mortality and Overall Mortality of Patients with Hepatocellular Carcinoma

Dai

Pan

et al. 2021

BioMed Research International

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Hepatocellular carcinoma (HCC) is the most common type among primary liver cancers (PLC). With its poor prognosis and survival rate, it is necessary for HCC patients to have a long-term follow-up. We believe that there are currently no relevant reports or literature about nomograms for predicting the cancer-specific mortality of HCC patients. Therefore, the primary goal of this study was to develop and evaluate nomograms to predict cancer-specific mortality and overall mortality. Data of 45,158 cases of HCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) program database between 2004 and 2013, which were then utilized to develop the nomograms. Finally, the performance of the nomograms was evaluated by the concordance index (C-index) and the area under the time-dependent receiver operating characteristic (ROC) curve (td-AUC). The categories selected to develop a nomogram for predicting cancer-specific mortality included marriage, insurance, radiotherapy, surgery, distant metastasis, lymphatic metastasis, tumor size, grade, sex, and the American Joint Committee on Cancer (AJCC) stage; while the marriage, radiotherapy, surgery, AJCC stage, grade, race, sex, and age were selected to develop a nomogram for predicting overall mortality. The C-indices for predicted 1-, 3-, and 5-year cancer-specific mortality were 0.792, 0.776, and 0.774; the AUC values for 1-, 3-, and 5-year cancer-specific mortality were 0.830, 0.830, and 0.830. The C-indices for predicted 1-, 3-, and 5-year overall mortality were 0.770, 0.755, and 0.752; AUC values for predicted 1-, 3-, and 5-year overall mortality were 0.820, 0.820, and 0.830. The results showed that the nomograms possessed good agreement compared with the observed outcomes. It could provide clinicians with a personalized predicted risk of death information to evaluate the potential changes of the disease-specific condition so that clinicians can adjust therapy options when combined with the actual condition of the patient, which is beneficial to patients.

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“…Abnormal expression of lncRNAs has been found to be involved in a variety of biological processes of cancer, including LNM (17)(18)(19). For example, Ma et al suggested that a lncRNA-based nomogram can be used to predict LNM risk (20). Although related studies have revealed some risk factors for LNM in HCC, these indicators need additional testing, postoperative sample testing, and even clinical testing.…”

Section: Discussionmentioning

confidence: 99%

Nomogram Based on Preoperative Clinical Characteristics to Predict the Risk of Lymph Node Metastasis in Hepatocellular Carcinoma

Wei¹,

S²,

Ren³

et al. 2020

Preprint

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Background: There are limitations in judging the occurrence of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC) before surgery. The purpose of this study was to establish a preoperative nomogram for predicting the risk of LNM in HCC and to explore its clinical utility.Methods: A total of 195 HCC patients undergoing radical hepatectomy were retrospectively analyzed. According to the presence or absence of LNM, the patients were divided into two groups, and the clinical characteristics of the two groups were compared. Risk factors for LNM were assessed based on logistic regression, and a nomogram was established. The receiver operating characteristic (ROC) curve was used to calculate area under the curve (AUC) of the logistic regression model, and the predictive accuracy of the nomogram was evaluated by the concordance index (C-index). The clinical efficacy of the nomogram was detected by decision curve analysis (DCA).Results: Logistic analysis revealed hepatitis B surface antigen (HBsAg) (HR = 3.50, 95% CI, 1.30-9.42, P = 0.013), globulin (HR = 2.46, 95% CI, 1.05-5.75, P = 0.039), neutrophil to lymphocyte ratio (NLR) (HR = 7.64, 95% CI, 3.22-18.11, P < 0.001) and tumor size (HR = 3.86, 95% CI, 1.26-11.88 P = 0.018) were independent risk factors for lymph node metastasis in HCC. The nomogram was established based on the above 4 variables, and the AUC was 0.835 (95% CI, 0.780-0.890). The calibration curve showed that the model has good predictive ability, and DCA indicates good predictive effect.Conclusions: The nomogram established by analyzing the preoperative clinical characteristics is a simple tool that can predict the risk of lymph node metastasis in HCC patients and guide clinicians to make better clinical decisions.

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A Noninvasive Prediction Nomogram for Lymph Node Metastasis of Hepatocellular Carcinoma Based on Serum Long Noncoding RNAs

Cited by 10 publications

References 36 publications

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Development and Evaluation of Nomograms to Predict the Cancer-Specific Mortality and Overall Mortality of Patients with Hepatocellular Carcinoma

Nomogram Based on Preoperative Clinical Characteristics to Predict the Risk of Lymph Node Metastasis in Hepatocellular Carcinoma

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