scholarly journals Prognostic value of chemotherapy in addition to concurrent chemoradiotherapy in T3-4N0-1 nasopharyngeal carcinoma: a propensity score matching study

Oncotarget ◽  
2017 ◽  
Vol 8 (44) ◽  
pp. 76807-76815 ◽  
Author(s):  
Li-Rong Wu ◽  
Hong-Liang Yu ◽  
Ning Jiang ◽  
Xue-Song Jiang ◽  
Dan Zong ◽  
...  
2018 ◽  
Vol Volume 10 ◽  
pp. 2785-2797 ◽  
Author(s):  
Ronald Wihal Oei ◽  
Lulu Ye ◽  
Fangfang Kong ◽  
Chengrun Du ◽  
Ruiping Zhai ◽  
...  

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Feng Wang ◽  
Tingting Tao ◽  
Heng Yu ◽  
Yingying Xu ◽  
Zhi Yang ◽  
...  

Abstract Background Immunoinflammatory and nutritional markers, such as the peripheral blood neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and Onodera’s prognostic nutritional index (OPNI), have gained considerable attention and have been preliminarily revealed as prognostic markers of gastrointestinal stromal tumors (GISTs). Methods In this study, we first investigated the prognostic value of OPNI in GISTs treated with or without TKIs based on the propensity score matching (PSM) method. All of the patients had received surgical resection for primary GIST, and data from 2010 to 2018 were initially and retrospectively identified from our gastrointestinal center. Recurrence-free survival (RFS) was calculated by the Kaplan–Meier method and compared by the log-rank test. Results The patients were divided into groups treated and not treated with TKIs, and we used the propensity score matching method to homogenize their baseline data. Multivariate Cox proportional hazard regression models were applied to identify associations with outcome variables. A total of 563 GISTs were initially chosen, and 280 of them were included for analysis under the inclusion criteria. After PSM, there were 200 patients included. Multivariate analyses identified OPNI as an independent prognostic marker that was associated with primary site, tumor size, mitotic index, tumor rupture, necrosis, and modified NIH risk classification. Low OPNI (< 42.6; HR 0.409; P < 0.001) was associated with worse RFS. Conclusions Preoperative OPNI is a novel and useful prognostic marker for GISTs both treated and not treated with TKIs. Higher NLR and PLR have negative effects on RFS.


Author(s):  
Jie-bin Xie ◽  
Yue-shan Pang ◽  
Xun Li ◽  
Xiao-ting Wu

Abstract Background Current studies on the number of removed lymph nodes (LNs) and their prognostic value in small-bowel neuroendocrine tumors (SBNETs) are limited. This study aimed to clarify the prognostic value of removed LNs for SBNETs. Methods SBNET patients without distant metastasis from 2004 to 2017 in the SEER database were included. The optimal cutoff values of examined LNs (ELNs) and negative LNs (NLNs) were calculated by the X-tile software. Propensity score matching (PSM) was done to match patients 1:1 on clinicopathological characteristics between the two groups. The Kaplan-Meier method with log-rank test and multivariable Cox proportional-hazards regression model were used to evaluate the prognostic effect of removed LNs. Results The cutoff values of 14 for ELNs and 9 for NLNs could well distinguish patients with different prognoses. After 1:1 PSM, the differences in clinicopathological characteristics between the two groups were significantly reduced (all P > 0.05). Removal of more than one LN significantly improved the prognosis of the patients (P < 0.001). The number of lymphatic metastasis in the sufficiently radical resection group (SRR, 3.74 ± 3.278, ELN > 14 and NLN > 9) was significantly more than that in the insufficiently radical resection group (ISRR, 2.72 ± 3.19, ELN < 14 or NLN < 9). The 10-year overall survival (OS) of the SRR was significantly better than that of the ISRR (HR = 1.65, P = 0.001, 95% CI: 1.24–2.19). Conclusion Both ELNs and NLNs can well predict the OS of patients. Systematic removal of more than 14 LNs and more than 9 NLNs can increase the OS of SBNET patients.


Oral Oncology ◽  
2020 ◽  
Vol 103 ◽  
pp. 104589
Author(s):  
Hui Chang ◽  
Ya-lan Tao ◽  
Wei-jun Ye ◽  
Wei-wei Xiao ◽  
Yun-fei Xia ◽  
...  

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