<p>Construction of a nomogram for the prediction of prognosis in patients with resectable gastric cancer undergoing fewer than sixteen lymph node biopsies</p>

Abstract

BackgroundLymph node metastases evaluation is important for assessing gastric cancer prognosis. In patients not undergoing adequate lymph node biopsy, lymph node stage migration occurs with the use of the existing staging system. This study established a prediction model to improve prognostication in patients undergoing fewer than 16 lymph nodes biopsy.Patients and methodsIn total, 3036 eligible patients from the Surveillance, Epidemiology, and End Results Program database were evaluated. They were randomized into development and validation sets in a 1:1 ratio (n=1520 and 1516, respectively). To avoid model overfitting and loss of important factors, prognostic factors related to overall survival (OS) were screened according to the Akaike information criterion. The nomogram was assessed using discrimination and consistency tests in the development and validation sets; the concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves were also evaluated. Comparison with the 7th American Joint Committee on Cancer (AJCC) staging system was based on Kaplan–Meier curves, ROC, risk stratification, and decision curve analysis (DCA).ResultsAge, race, degree of differentiation, invasion depth, chemotherapy, radiotherapy, and lymph node ratio were independent prognostic factors in OS. C-indices of the development and validation sets were 0.759 (95% CI: 0.741–0.777) and 0.742 (95% CI: 0.713–0.771), respectively; calibration curves were approximately 45° diagonal, indicating good predictive ability of the nomogram. In contrast to the 7th AJCC staging system, the Kaplan–Meier curves and risk stratification of the nomogram had better discrimination ability, the ROC curves of the nomogram achieved more predictive accuracy, and the DCA indicated that the nomogram conferred higher net benefit.ConclusionOur constructed nomogram predicts the prognosis of patients with resectable gastric cancer undergoing biopsy of fewer than 16 lymph nodes more precisely and has better clinical applicability than the 7th AJCC staging system.

<p>Prognostic Factors and Local Treatment Modalities of Small-Cell Carcinoma of the Cervix: An Analysis According to the International Federation of Gynecology and Obstetrics Stage</p>

“…A nomogram was developed from the results of the Cox regression analysis, and it was used to predict the 3-and 5-year overall survival by representing the sum of points for each variable. 17 The internal validation of the model was performed using a 1000 bootstrap resample. The C-index, which denotes the proportion of pairs, measured on a scale of 0.5 (no better than chance) to 1 (perfect discrimination), was calculated to verify the nomogram's predictive accuracy, with the responder having a higher predicted probability of response than the non-responder.…”

LncRNA HOTAIR promotes the growth and metastasis of gastric cancer by sponging miR-1277-5p and upregulating COL5A1

“…The Stomach Adenocarcinoma (STAD) dataset from TCGA was processed by deleting blank values for further analysis, and the thresholds used to divide patients by age, tumor grade, TNM stage, T stage, N stage, M stage, and percentage of positive lymph nodes were in accordance with other previous studies [23][24][25][26][27][28][29]. Based on the results of the χ 2 test and LASSO regression, some important variables were selected, and Cox regression analysis was used to construct a nomogram using the rms package in R software.…”

The prognostic performance of the log odds of positive lymph nodes in patients with esophageal squamous cell carcinoma: A population study of the US SEER database and a Chinese single‐institution cohort

“…Previous studies have suggested that a C-index ranging from 0.50-0.70 indicates a low degree of distinction, a moderate degree of distinction between the interval 0.70 and 0.90, and a high degree of distinction higher than 0.90. 22 Stratification analyses visualized by tree diagrams were used to evaluate the performance of the LODDS classification in distinguishing heterogeneous patients within various groups according to the results of the multivariate analyses.…”