泛免疫炎症价值评分在局部晚期直肠癌中的重要性

IF 2.7 4区 医学 Q3 IMMUNOLOGY
Mahmut Uçar, Mukaddes Yılmaz, Eda Erdiş, Birsen Yücel
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引用次数: 0

摘要

目的在本研究中,我们旨在观察局部晚期直肠癌患者诊断时计算的泛免疫-炎症值(pan-immune-inflammation value, PIV)评分对预后的意义,以及其对治疗反应、生存和预后的影响。材料与方法本回顾性单中心观察性研究旨在分析接受新辅助治疗的非转移性(II-III期)直肠癌患者,分为PIV-L (n = 67,50%)和PIV-H (n = 67,50%)两组。中位PIV评分用于确定截止值。采用生存分析。采用单因素和多因素Cox回归分析确定预后因素。结果术前临床淋巴结状况(p = 0.011)、肝转移情况(p = 0.028)、癌胚抗原(CEA;p = 0.013),癌抗原19.9 (CA19.9;P = 0.040)水平;病理完全缓解(p = 0.035);肿瘤回归评分(p = 0.030);术后淋巴结状况(p = 0.019);肿瘤沉积(p = 0.035);出芽率组间差异有统计学意义(p = 0.043)。PIV-L组和PIV-H组的5年和10年总生存率分别为77%对69%和62%对38% (p = 0.032)。在单因素分析中,PIV评分是OS的预后指标(HR: 1.85, 95% CI: 1.04-3.31, p = 0.035),而在多因素分析中,结果不显著(HR: 1.76, 95% CI: 0.98-3.01 p = 0.056)。PIV-L组和PIV-H组的5年和10年无病生存率(DFS)分别为67%对54%和56%对39%,PIV-H组的DFS较PIV-H组低,差异有统计学意义(p = 0.048)。对于DFS,单因素分析发现PIV评分为无统计学意义的预后因素(HR: 0.052, 95% CI: 0.99 ~ 2.86, p = 0.052),多因素分析认为PIV评分为独立预后因素(HR: 1.87, 95% CI: 1.08 ~ 3.26, p = 0.026)。结论预处理PIV评分越高,DFS的临床病理特征越差,治疗反应越差,生存率越低,预后越差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Importance of Pan-Immune-Inflammation Value Score in Locally Advanced Rectal Cancer

The Importance of Pan-Immune-Inflammation Value Score in Locally Advanced Rectal Cancer

Aim

In this study, we aimed to observe the prognostic significance of the pan-immune-inflammation value (PIV) score calculated at the time of diagnosis in patients with locally advanced rectal cancer, as well as its effect on treatment response, survival, and prognosis.

Material and Method

This retrospective, single-center observational study was designed to analyze patients with nonmetastatic (stages II–III) rectal cancer who received neoadjuvant treatment, categorized into two groups: PIV-L (n = 67, 50%) and PIV-H (n = 67, 50%). The median PIV score was used for cutoff determination. Survival analysis was applied. Univariate and multivariate Cox regression analyses were used to determine prognostic factors.

Results

Preoperative clinical lymph node status (p = 0.011), liver metastasis (p = 0.028), carcinoembryonic antigen (CEA; p = 0.013), and cancer antigen 19.9 (CA19.9; p = 0.040) levels; pathological complete response (p = 0.035); tumor regression score (p = 0.030); postoperative lymph node status (p = 0.019); tumor deposits (p = 0.035); and budding (p = 0.043) were statistically different between the groups. The 5- and 10-year overall survival (OS) rates were 77% versus 69% and 62% versus 38% in the PIV-L and PIV-H groups, respectively (p = 0.032). While the PIV score was prognostic for OS in univariate analysis (HR: 1.85, 95% CI: 1.04–3.31, p = 0.035), a result of insignificance was obtained in multivariate analysis (HR: 1.76, 95% CI: 0.98–3.01 p = 0.056). The 5- and 10-year disease-free survival (DFS) rates were 67% versus 54% and 56% versus 39% in the PIV-L and PIV-H groups, respectively, with the PIV-H group showing a statistically significantly lower rate (p = 0.048). For DFS, the PIV score was found to be a statistically insignificant prognostic factor in univariate analysis (HR: 0.052, 95% CI: 0.99–2.86, p = 0.052) and recognized as an independent prognostic factor in multivariate analysis (HR: 1.87, 95% CI: 1.08–3.26, p = 0.026).

Conclusion

A higher pretreatment PIV score was associated with poorer clinicopathological features, a worse treatment response, lower survival rates, and a poor prognosis for DFS.

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来源期刊
Immunity, Inflammation and Disease
Immunity, Inflammation and Disease Medicine-Immunology and Allergy
CiteScore
3.60
自引率
0.00%
发文量
146
审稿时长
8 weeks
期刊介绍: Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology
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