&lt;p&gt;Nomogram to Predict Preoperative Occult Peritoneal Metastasis of Gastrointestinal Stromal Tumors (GIST) Based on Imaging and Inflammatory Indexes&lt;/p&gt;

Xu, Shao-Jun; Lin, Guo; Ling, Hongjian; Guo, Ren-Jie; Chen, Jie; Y, Liao; Lin, Tao; Zhou, Yongjian

doi:10.2147/cmar.s275422

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Currently, the known factors associated with prognosis are tumor size, site, nuclear split phase count, tumor rupture, positive cut margins, KIT 11 exon deletion mutations, and other adverse biological behaviors ( 11 – 15 ). The Fletcher classification criteria and the Miettinen-Lasotar classification criteria (also known as AFIP risk assessment) have been used clinically as risk assessment for GISTs, the former classifying four grades of very low, low, medium and high risk with only two indicators of tumor size and nuclear split phase count, the latter adding tumor primary site to tumor size and nuclear split phase count parameter indexes and introduced a comprehensive scoring method to classify them ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

et al. 2022

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BackgroundThe increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.MethodsWe downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.ResultsA total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.ConclusionBased on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

et al. 2022

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“…A total of 394 patients were included in this retrospective analysis. Patients who received radical treatment between January 2010 and December 2014 were assigned into the training cohort (n=280), while patients from January 2015 to June 2016 were assigned into the validation cohort (n=114) for validation (12,13). The TNM staging system 8 th edition was retrospectively used for all of the patients in our study.…”

Section: Study Populationmentioning

confidence: 99%

A nomogram to predict the recurrence-free survival and analyze the utility of chemotherapy in stage IB non-small cell lung cancer

Zhang¹,

Xie²,

Cai³

et al. 2022

Transl Lung Cancer Res

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Background: Large part of patients of stage IB non-small cell lung cancer (IB NSCLC) may suffer recurrence after surgery. This study is to determine risk factors and establish a nomogram for postoperative recurrence and to provide a reference for adjuvant chemotherapy selection in patients with stage IB NSCLC.Methods: A total of 394 patients with postoperative stage IB NSCLC who visited Fujian Medical University Union Hospital between January 2010 and June 2016 were selected. Patients were divided into training and validation cohorts based on the time of diagnosis. Independent risk factors were identified using a Cox proportional hazards regression model. A nomogram was created to predict recurrence-free survival (RFS) and was validated with an independent cohort. The predictive ability of the nomogram was evaluated using the concordance index (C-index) and calibration curve. RFS between the high-and low-risk groups was determined using Kaplan-Meier curves, and subgroup analysis of chemotherapy was performed.Results: Visceral pleura invasion, micropapillary structures, tumor size, preoperative serum carcinoembryonic antigen (CEA) level, preoperative serum cytokeratin-19 fragments (Cyfra21-1) level, and postoperative histology were identified as independent risk factors for stage IB NSCLC recurrence. Discrimination of the nomogram showed good prognostic accuracy and clinical applicability, with a C-index of 0.827 and 0.866 in the training and validation cohorts, respectively. The difference in RFS between the high-and low-risk groups in both cohorts was significant (P<0.05). Finally, a significant difference was observed on whether high-risk group should accept postoperative chemotherapy (P<0.05).Conclusions: This nomogram can predict postoperative recurrence probability in patients with stage IB NSCLC, and can select patients with risk factors who need adjuvant chemotherapy.

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A New Online Dynamic Nomogram: Construction and Validation of a Predictive Model for Distant Metastasis Risk and Prognosis in Patients with Gastrointestinal Stromal Tumors

Ding

et al. 2023

Journal of Gastrointestinal Surgery

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<p>Nomogram to Predict Preoperative Occult Peritoneal Metastasis of Gastrointestinal Stromal Tumors (GIST) Based on Imaging and Inflammatory Indexes</p>

Cited by 4 publications

References 41 publications

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

A nomogram to predict the recurrence-free survival and analyze the utility of chemotherapy in stage IB non-small cell lung cancer

A New Online Dynamic Nomogram: Construction and Validation of a Predictive Model for Distant Metastasis Risk and Prognosis in Patients with Gastrointestinal Stromal Tumors

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