Dataset Information

Pre-operative lymph node status of gastric cancer evaluated by multidetector computed tomography.

ABSTRACT: The purpose of the present study was to perform a meta-analysis to evaluate the diagnostic value of Multidetector computed tomography (MDCT) in the pre-operative lymph node (N) staging in gastric cancer (GC) patients. The Medline, Embase and Web of Knowledge were searched for studies assessing the diagnostic value of MDCT in the pre-operative evaluation of TNM staging in GC patients. We pooled the sensitivity, specificity, positive and negative Likelihood ratio (LR+ and LR-), Diagnostic Odds Ratio (DOR) and constructed summary receiver operating characteristic curves (ROC). A total of 30 studies including 6637 GC patients were analyzed. The pooled estimates of sensitivity, specificity, LR+, LR- and DOR of MDCT in the detection of pre-operative N staging in GC patients were 0.67 (95% CI: 0.66-0.69 ), 0.84 (95% CI: 0.83-0.85), 3.25 (95% CI: 2.69-3.93), 0.36 (95% CI: 0.28-0.46) and 10.31 (95% CI: 7.66-13.88), respectively. The results of a summary ROC showed that the AUC and Q* were 0.8338 and 0.7661, respectively. As a control, the AUC and Q* of endoscopic ultrasonography were 0.8063 and 0.7414, respectively. Currently, it is necessary to recommend the routine clinical application of MDCT in the preoperative evaluation of lymph node status in GC patients.

SUBMITTER: Wang M

PROVIDER: S-EPMC4694323 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Pre-operative lymph node status of gastric cancer evaluated by multidetector computed tomography.

Wang Min M Ye Yanwei Y Yang Qing Q Li Jingjing J Han Chao C Wang Wei W Zhao Chunlin C Wen Jianguo J

International journal of clinical and experimental medicine 20151015 10

The purpose of the present study was to perform a meta-analysis to evaluate the diagnostic value of Multidetector computed tomography (MDCT) in the pre-operative lymph node (N) staging in gastric cancer (GC) patients. The Medline, Embase and Web of Knowledge were searched for studies assessing the diagnostic value of MDCT in the pre-operative evaluation of TNM staging in GC patients. We pooled the sensitivity, specificity, positive and negative Likelihood ratio (LR+ and LR-), Diagnostic Odds Rat ...[more]

PMID: 26770423

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Project description:BackgroundLymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery.MethodsRetrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n = 128) and a validation cohort (n = 64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared.ResultsSeven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates.ConclusionThe radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Project description:The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.

Dataset Information

Pre-operative lymph node status of gastric cancer evaluated by multidetector computed tomography.

Publications

Pre-operative lymph node status of gastric cancer evaluated by multidetector computed tomography.

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