scholarly journals Can preoperative modified systemic inflammation score (mSIS) be used to predict malignancy in persistent nondiagnostic thyroid nodules?

2021 ◽  
Vol 221 (1) ◽  
pp. 117-121
Author(s):  
Hakan Ataş ◽  
Birol Korukluoğlu ◽  
Buket Altun Özdemir ◽  
Neşe Yakşi ◽  
Barış Saylam ◽  
...  

2018 ◽  
Vol 158 (6) ◽  
pp. 1042-1050 ◽  
Author(s):  
Yousif I. Eltohami ◽  
Huang-Kai Kao ◽  
William Wei-Kai Lao ◽  
Yenlin Huang ◽  
Mohamed Abdelrahman ◽  
...  

Objectives This study aimed to investigate the potential prognostic role of the oral cancer systemic inflammation score (SIS) based on serum albumin levels and the lymphocyte-to-monocyte ratio in patients with oral squamous cell carcinoma (OSCC) after treatment. Study Design A retrospective cohort study. Setting Tertiary care center. Subjects and Methods The study involved 613 patients who were treated for OSCC between September 2005 and December 2014. The association of the oral cancer SIS with various clinicopathological features was investigated. A nomogram based on different clinicopathological features and SIS was established to predict prognosis. Results Higher SIS was significantly associated with older age ( P = .0013), advanced tumor status ( P < .0001), tumor depth ( P < .0001), advanced overall pathologic stage ( P < .0001), and extranodal extension ( P = .0045), as well as the presence of perineural invasion ( P = .0341). Higher SIS, older age, overall stage, and extranodal extension were demonstrated to be independent prognostic indicators for shorter overall survival ( P < .0001). A nomogram comprising SIS, TNM stage, and the degree of cell differentiation, as well as perineural invasion and extranodal extension, was developed to predict the prognosis of these patients. The c-index of the nomogram model based on TNM staging only was 0.688 and could be increased to 0.752 if SIS and several other clinicopathological parameters were incorporated. Conclusions Higher SIS is associated with many poor prognosticators, and the nomogram that was established and based on the incorporation of SIS might strengthen the prediction of prognosis in patients with OSCC.


2021 ◽  
Vol 8 ◽  
Author(s):  
Jianping Xiong ◽  
Wenzhe Kang ◽  
Fuhai Ma ◽  
Hao Liu ◽  
Shuai Ma ◽  
...  

Background: The modified systemic inflammation score (mSIS), which is calculated by a composite score of the lymphocyte-to-monocyte ratio and the albumin content in serum, is identified as the new score to predict the prognosis for various cancers. However, its significance for patients with adenocarcinoma of esophagogastric junction (AEJ), who receive surgery, remains unclear.Methods: This study retrospectively analyzed 317 patients with AEJ receiving surgery between September 2010 and December 2016. The associations between the mSIS and the clinicopathological features, overall survival (OS), as well as relapse-free survival (RFS), were assessed. In addition, the time-dependent receiver operating characteristic (t-ROC) curve analysis was performed for comparing the value of those scoring systems in predicting patient prognosis.Results: Of the 317 cases, 119 were rated as mSIS 0, 123 as mSIS 1, and 75 as mSIS 2. Besides, mSIS was significantly related to age and tumor size. On multivariate analysis, mSIS was identified as a predictor to independently predict OS (p &lt; 0.001) along with RFS (p &lt; 0.001), and a significantly strong correlation was observed at the advanced pTNM stages based on the mSIS system. In the subgroup analysis of adjuvant chemotherapy and surgery alone, mSIS was still the predictor for independently predicting patient OS (p &lt; 0.001) together with RFS (p &lt; 0.001) for the two groups. T-ROC analysis showed that mSIS was more accurate than controlling nutritional status score in predicting OS and RFS.Conclusions: The mSIS can serve as an easy, useful scoring system to independently predict the preoperative survival for AEJ cases undergoing surgery.


2018 ◽  
Vol 22 (2) ◽  
pp. 403-412 ◽  
Author(s):  
Jian-Xian Lin ◽  
Jun-Peng Lin ◽  
Jian-Wei Xie ◽  
Jia-bin Wang ◽  
Jun Lu ◽  
...  

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