全身免疫相关脾脏放射组学预测接受明确放化疗的局部晚期宫颈癌患者的无进展生存期。

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-11-15 DOI:10.1186/s12880-024-01492-1
Yi Li, Longxiang Guo, Peng Xie, Yuhui Liu, Yuanlin Li, Ao Liu, Minghuan Li
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引用次数: 0

摘要

目的:全身免疫对于驱动治疗诱导的抗肿瘤免疫反应至关重要,而脾脏可反映全身免疫的改变。本研究旨在评估对比增强 CT 脾脏放射组学对接受确定性化放疗(dCRT)的局部晚期宫颈癌(LACC)患者无进展生存期(PFS)的预测价值。此外,我们还研究了脾脏放射组学特征和脾脏体积变化在评估全身免疫力中的作用:这项回顾性研究纳入了 257 例接受 dCRT 的 LACC 患者。患者按 7:3 的比例随机分为训练组和验证组。从 dCRT 前后的 CT 图像中提取放射体征。通过最小绝对缩小和选择算子(LASSO)Cox 回归计算出特征,并以此计算出放射体征评分(Radscore)。根据治疗前后的测量结果确定脾脏体积变化的百分比。通过多变量 Cox 回归分析确定了 PFS 的独立预后因素。模型性能通过接收者操作特征曲线(ROC)和 C 指数进行评估。根据 ROC 曲线确定的 Radscore 临界值用于将患者分为高危和低危生存组。Wilcoxon 检验用于分析不同生存风险组之间和不同脾脏体积变化组之间血液学参数的差异。斯皮尔曼相关分析用于探讨脾脏体积变化与血液学指标之间的关系:独立预后因素包括FIGO分期、治疗前中性粒细胞与淋巴细胞比值(pre-NLR)、脾脏体积变化和Radscore。在训练组(AUC:0.923,C-index:0.884)和验证组(AUC:0.895,C-index:0.834)中,放射组学组合模型对PFS的预测效果最好。与低风险组相比,高风险组的 NLR 前(p = 0.0054)和 NLR 后(p = 0.038)均较高。此外,与脾脏体积减小组相比,脾脏体积增大组的 NLR 后(p = 0.0059)和治疗后血小板与淋巴细胞比值(p 结论:脾脏放射组学与临床研究相结合,可为脾脏疾病的诊断和治疗提供依据:脾脏放射组学与临床特征相结合可有效预测 LACC 患者 dCRT 后的 PFS。此外,脾脏放射组学特征和脾脏体积的变化可反映全身免疫的改变。
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Systemic immune-related spleen radiomics predict progression-free survival in patients with locally advanced cervical cancer underwent definitive chemoradiotherapy.

Purpose: Systemic immunity is essential for driving therapeutically induced antitumor immune responses, and the spleen may reflect alterations in systemic immunity. This study aimed to evaluate the predictive value of contrast-enhanced CT-based spleen radiomics for progression-free survival (PFS) in patients with locally advanced cervical cancer (LACC) who underwent definitive chemoradiotherapy (dCRT). Additionally, we investigated the role of spleen radiomics features and changes in spleen volume in assessing systemic immunity.

Methods: This retrospective study included 257 patients with LACC who underwent dCRT. The patients were randomly divided into training and validation groups in a 7:3 ratio. Radiomic features were extracted from CT images obtained before and after dCRT. Radiomic scores (Radscore) were calculated using features selected through least absolute shrinkage and selection operator (LASSO) Cox regression. The percentage change in spleen volume was determined from measurements taken before and after treatment. Independent prognostic factors for PFS were identified through multivariate Cox regression analyses. Model performance was evaluated with the receiver operating characteristic (ROC) curve and the C-index. The Radscore cut-off value, determined from the ROC curve, was used to stratify patients into high- and low-risk survival groups. The Wilcoxon test was used to analyze differences in hematological parameters between different survival risk groups and between different spleen volume change groups. Spearman correlation analysis was used to explore the relationship between spleen volume change and hematological parameters.

Results: Independent prognostic factors included FIGO stage, pre-treatment neutrophil-to-lymphocyte ratio (pre-NLR), spleen volume change, and Radscore. The radiomics-combined model demonstrated the best predictive performance for PFS in both the training group (AUC: 0.923, C-index: 0.884) and the validation group (AUC: 0.895, C-index: 0.834). Compared to the low-risk group, the high-risk group had higher pre-NLR (p = 0.0054) and post-NLR (p = 0.038). Additionally, compared to the decreased spleen volume group, the increased spleen volume group had lower post-NLR (p = 0.0059) and post-treatment platelet-to-lymphocyte ratio (p < 0.001).

Conclusion: Spleen radiomics combined with clinical features can effectively predict PFS in patients with LACC after dCRT. Furthermore, spleen radiomics features and changes in spleen volume can reflect alterations in systemic immunity.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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