A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer.
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ABSTRACT:
SUBMITTER: Li H
PROVIDER: S-EPMC9667298 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
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A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer.
Li Huling H Lin Dandan D Yu Zhen Z Li Hui H Zhao Shi S Hainisayimu Tuersun T Liu Lin L Wang Kai K
Frontiers in immunology 20221102
<h4>Background</h4>Increasing evidence suggests that the number of examined lymph nodes (ELNs) is strongly linked to the survivorship of gastric cancer (GC). The goal of this study was to assess the prognostic implications of the ELNs number and to construct an ELNs-based risk signature and nomogram model to predict overall survival (OS) characteristics in GC patients.<h4>Methods</h4>This inception cohort study included 19,317 GC patients from the U.S. Surveillance, Epidemiology, and End Results ...[more]
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Project description:To develop a nomogram to predict the prognosis of gastric cancer patients on the basis of metastatic lymph nodes ratio (mLNR), especially in the patients with total number of examined lymph nodes (TLN) less than 15. The nomogram was constructed based on a retrospective database that included 2,205 patients underwent curative resection in Cancer Center, Sun Yat-sen University (SYSUCC). Resectable gastric cancer (RGC) patients underwent curative resection before December 31, 2008 were assigned as the training set (n=1,470) and those between January 1, 2009 and December 31, 2012 were selected as the internal validation set (n=735). Additional external validations were also performed separately by an independent data set (n=602) from Jiangxi Provincial Cancer Hospital (JXCH) in Jiangxi, China and a data set (n=3,317) from the Surveillance, Epidemiology, and End Results (SEER) database. The Independent risk factors were identified by Multivariate Cox Regression. In the SYSUCC set, TNM (Tumor-node-metastasis) and TRM-based (Tumor-Positive Nodes Ratio-Metastasis) nomograms were constructed respectively. The TNM-based nomogram showed better discrimination than the AJCC-TNM staging system (C-index: 0.73 versus 0.69, p<0.01). When the mLNR was included in the nomogram, the C-index increased to 0.76. Furthermore, the C-index in the TRM-based nomogram was similar between TLN ≥16 (C-index: 0.77) and TLN ≤15 (C-index: 0.75). The discrimination was further ascertained by internal and external validations. We developed and validated a novel TRM-based nomogram that provided more accurate prediction of survival for gastric cancer patients who underwent curative resection, regardless of the number of examined lymph nodes.
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Project description:BackgroundPrevious research on the prognostic effectiveness of examined lymph nodes (ELN), lymph node ratio (LNR), and positive lymph nodes (pN) in postoperative gastric cancer (GC) has yielded inconsistent results despite their widespread use.MethodsThis study used a competing risk model (CRM) to evaluate the prognostic efficacy of these markers in patients with GC. Data from 337 patients with lymph node (LN)-positive stage II GC undergoing resection and chemotherapy between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Optimal cutoff values for ELN and LNR were determined using restricted cubic splines, and pN was divided into three groups based on the AJCC staging system. The survival analyses were conducted using Kaplan-Meier curves, Cox proportional hazards analysis, cumulative incidence curves, and CRM. Subgroup analysis and interaction tests were performed to evaluate the correlation between LN status and survival within subgroups.ResultsThe results indicated that the optimal cutoff values for ELN, LNR, and pN were 16, 0.1, and 2. Multivariate Cox analysis showed that ELN (hazard ratio [HR] = 0.67), LNR (HR = 2.23), and pN (HR = 2.80) were independent predictors of overall survival, whereas only LNR (HR = 2.08) was independently associated with disease-specific survival. The CRM revealed that LNR (sub-distribution hazard ratio [SHR] = 1.89) and pN (SHR = 2.80) were independently associated with disease-specific survival.ConclusionIn conclusion, ELN, LNR, and pN are all significant predictors of overall survival for GC. However, LNR demonstrates stronger robustness in predicting DSS than ELN and pN. The LNR may supplement the TNM staging system in identifying prognostic discrepancies.
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Project description:In patients with gastric cancer (GC), metastatic progression through the lymphatic, haematogenous, peritoneal, and ovarian routes is the ultimate cause of death. We developed a nomogram to estimate cancer-specific survival (CSS) in patients with T1 gastric cancer based on log odds of positive lymph nodes (LODDS). A total of 2,221 patients from the Surveillance, Epidemiology, and End Results (SEER) database were split into training and internal validation cohorts, while an external validation cohort included 165 patients from our hospital. Multivariate Cox regression analysis revealed that age, sex, tumour size, LODDS score, and M stage were independent prognostic factors for CSS. The LODDS outperformed the N stage and positive lymph node (PLN) count in terms of predictive ability and is recognised as an independent prognostic factor for nomogram construction. In the training and internal and external validation sets, the 1-year AUCs of the columniogram were 0.732, 0.672, and 0.719, respectively. The 3-year AUCs were 0.705, 0.692, and 0.638, respectively. The 5-year AUCs were 0.726, 0.698, and 0.713, respectively, indicating good predictive power. The calibration curve revealed that the predicted survival rate was consistent with the actual survival rate in the three groups. The ROC and DCA demonstrated that the nomogram has more potential in predicting prognosis than the existing AJCC staging system. We constructed and validated a novel nomogram leveraging LODDS, which effectively estimates the CSS at 1, 3, and 5 years for individuals with gastric cancer.
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Project description:BackgroundInadequate number of lymph nodes examined was not uncommon. We aimed to assess the clinical role of inadequate number of lymph nodes examined in stage II colon cancer.MethodsThe cancer data used in our study were obtained from the SEER (Surveillance, Epidemiology and End Results) program. Using the chi-square test, all the variables obtained in our study were compared based on whether patients had enough (≥12) lymph nodes examined. Kaplan-Meier analysis was used for overall survival (OS) analysis, and log-rank test was applied to compare different N stages with the total number of lymph nodes examined. Multivariate analysis was carried out by creating a Cox proportional hazard model to assess the prognostic roles of different variables.ResultsIn total, 80,296 stage II/III colon cancer patients were recruited for our study. N0 stage with <8 lymph nodes examined would present with a worse prognosis compared to N1 stage (5-year OS rates, 51.6% vs. 57.1%, p < 0.001). Multivariate analyses indicated that OS of N0 stage with <8 lymph nodes examined was similar to that of N1 stage after adjusting for other recognized prognostic factors [hazard ratios (HRs) = 1.051, 95% confidence intervals (CIs) = 1.014-1.090, p = 0.018].ConclusionsN0 stage with less than eight lymph nodes examined in stage II colon cancer presented with no better OS compared to that of N1 stage. Stage II colon cancer with less than eight lymph nodes examined needed to be given greater emphasis in clinical practice.
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Project description:Background: Numerous studies have showed that internal mammary lymph node (IMLN) metastasis is an important adverse prognostic factor in patients with breast cancer (BC), however, there are no available prediction model for the preoperative diagnosis of IMLN metastasis. Methods: Data from 102 breast cancer patients treated with IMLN operation were used to establish and calibrate a nomogram for IMLN status based on multivariate logistic regression. Prediction performance of this model was further validated with a second set of 50 patients with BC. Discrimination of the predict model was assessed by the C-index, and calibration assessed by calibration plots. Moreover, we conducted the decision curve analysis (DCA) to evaluate the clinical value of the nomogram. Finally, the survival status of patients in different risk groups based on nomogram were also compared. Results: The final multivariate regression model included tumor location, lymph vascular invasion (LVI), and pathological axillary lymph node stage (pALN stage). A nomogram was developed as a graphical representation of the model and had good calibration and discrimination in both sets (with C-index of 0.86 and 0.83 for the training and validation set, respectively). Moreover, the DCA showed the clinical usefulness of our constructed nomogram. False negative (FN) in low risk group classified by nomogram (FN-LR-nomogram) did not significantly impact adjuvant treatment decision making, and more importantly, patients with FN-LR-nomogram had recurrence-free survival equivalent to patients with pathologically ture negative in low risk group classified by nomogram (TN-LR-nomogram). Conclusions: As a non-invasive prediction tool, our nomogram shows favorable predictive accuracy for IMLN metastasis in patients with BC and can serve as a basis to integrate future molecular markers for its clinical application.
| S-EPMC6857087 | biostudies-literature
Project description:BackgroundClinical guidelines recommend ≥12 examined lymph nodes (ELNs) for colon cancer staging, but more may be necessary for accuracy. This study utilized nodal staging scores (NSS) to identity the optimal number of ELNs based on tumor location and T stage, and to assess its prognostic impact.MethodsData from 80,792 patients in the Surveillance, Epidemiology, and End Results (SEER) database (2004-2014) and 2,300 patients from the West China Hospital (WCH) cohort (2008-2014) with stage I-III resected colon cancer were analyzed. Optimal ELNs was estimated using a β-binomial distribution model, stratified by tumor location (left-sided, LS; right-sided, RS) and T stage. The primary outcome was overall survival (OS). The association between sufficient nodal staging and OS in node-negative patients was validated by multivariate Cox models.ResultsThe mean number of ELN was 18.75 in the SEER and 14.58 in the WCH database. There were 57.8% and 48.8% patients who had RS colon cancer in the SEER and WCH database. Fewer T3-4 tumors were observed in the SEER cohort compared to the WCH cohort (68.4% vs. 87.2%). Sufficient nodal staging required ≥24 ELNs for T3 tumors, ≥34 nodes for T4 LS tumors, and ≥40 nodes for T4 RS tumors. For T3 lesions, examining 20-29 ELNs were more likely to have node-positive disease [odd ratio (OR) 1.07; 95% confidence interval (CI): 1.01-1.12] compared to patients with 12-15 ELNs. In the T3N0 group, ELN ≥24 was independently associated with better OS in the SEER database [hazard ratio (HR) 0.72; 95% CI: 0.68-0.75], which was validated in the WCH cohort (HR 0.54; 95% CI: 0.38-0.76).ConclusionsOptimal ELNs for adequate colon cancer staging is related to both T stage and tumor location. We recommend that ≥24 lymph nodes be examined for T3 tumors, ≥34 for LS T4 tumors and ≥40 for RS T4 tumors for sufficient staging.
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Project description:BackgroundEndobronchial ultrasound (EBUS) sonographic features help identify benign/malignant lymph nodes while conducting transbronchial needle aspiration (TBNA). This study aims to identify risk factors for malignancy based on EBUS sonographic features and to estimate the risk of malignancy in lymph nodes by constructing a nomogram.Methods1082 lymph nodes from 625 patients were randomly enrolled in training (n = 760) and validation (n = 322) sets. The subgroup of EBUS-TBNA postoperative negative lymph nodes (n = 317) was randomly enrolled in a training (n = 224) set and a validation (n = 93) set. Logistic regression analysis was used to identify the EBUS features of malignant lymph nodes. A nomogram was formulated using the EBUS features in the training set and later validated in the validation set.ResultsMultivariate analysis revealed that long-axis, short-axis, echogenicity, fusion, and central hilar structure (CHS) were the independent predictors of malignant lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 5 EBUS features nomogram showed good discrimination, with area under the curve values of 0.880 (sensitivity = 0.829 and specificity = 0.807) and 0.905 (sensitivity = 0.819 and specificity = 0.857). Subgroup multivariate analysis revealed that long-axis, echogenicity, and CHS were the independent predictors of malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 3 EBUS features nomogram showed good discrimination, with the area under the curve values of 0.890 (sensitivity = 0.882 and specificity = 0.786) and 0.834 (sensitivity = 0.930 and specificity = 0.636).ConclusionsOur novel scoring system based on two nomograms can be utilized to predict malignant lymph nodes.
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Project description: Not available
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Project description:BackgroundThis propensity score matching (PSM) analysis assessed the influence of examined lymph nodes (ELNs) count on stage migration and survival in node-negative (pN0) gastric cancer (GC) patients.MethodsWe performed a retrospective analysis of 7,620 GC patients who underwent curative gastric resection in three Chinese medical centers. PSM was used to reduce the confounding effects between the pN0 GC patients with ELNs <16 or ≥16. Survival differences among various subgroups of GC patients were analyzed to assess the impact of the ELNs count on the stage migration in accordance with the overall survival (OS) of pN0 GC patients.ResultsAfter matching, the backgrounds of pN0 GC patients in the ELNs <16 (n=825) and ELNs ≥16 (n=826) groups were well-balanced. Survival analyses revealed that the ELNs count was positively correlated with the OS (P=0.001). Multiple Cox analysis indicated that the ELNs count was an independent predictor of the OS in pN0 GC patients. Stage migrations were mainly detected in subgroups of pN0 GC patients with specific pTNM stages, as follows: (I) pT2N0M0 with ELNs <16 vs. pT3N0M0 with ELNs ≥16; (II) pT3N0M0 with ELNs <16 vs. pT3N1M0 with ELNs ≥16; and (III) pT4aN0M0 with ELNs <16 vs. pT4aN1M0 with ELNs ≥16.ConclusionsWe show that stage migration can be detected in pN0 GC patients, and that it could be gradually reduced or prevented by increasing the ELNs count.
| S-EPMC7475395 | biostudies-literature
Project description:The current study was conducted to develop a multifactorial statistical model to predict the specific head and neck (H&N) tumor site origin in cases of squamous cell carcinoma confined to the cervical lymph nodes ("unknown primaries").The Surveillance, Epidemiology, and End Results (SEER) database was analyzed for patients with an H&N tumor site who were diagnosed between 2004 and 2011. The SEER patients were identified according to their H&N primary tumor site and clinically positive cervical lymph node levels at the time of presentation. The SEER patient data set was randomly divided into 2 data sets for the purposes of internal split-sample validation. The effects of cervical lymph node levels, age, race, and sex on H&N primary tumor site were examined using univariate and multivariate analyses. Multivariate logistic regression models and an associated set of nomograms were developed based on relevant factors to provide probabilities of tumor site origin.Analysis of the SEER database identified 20,011 patients with H&N disease with both site-level and lymph node-level data. Sex, race, age, and lymph node levels were associated with primary H&N tumor site (nasopharynx, hypopharynx, oropharynx, and larynx) in the multivariate models. Internal validation techniques affirmed the accuracy of these models on separate data.The incorporation of epidemiologic and lymph node data into a predictive model has the potential to provide valuable guidance to clinicians in the treatment of patients with squamous cell carcinoma confined to the cervical lymph nodes.
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