Extracellular Volume Fraction Combined With Pathological Features of α-SMA and FAP for Predicting the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma After Surgery and Evaluating the Efficacy of Chemotherapy

IF 3.1 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-10-02 DOI:10.1002/cam4.71281

Liqiang Liu, Sicheng Xu, Pengbiao Miao, Xing He, Yaorong Peng, Qixian Zhou, Junkai Ren, Zhenyu Zhou, Huilin Ye, Wenbin Li

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引用次数: 0

Abstract

Objectives

This study aimed to evaluate the extracellular matrix of pancreatic ductal adenocarcinoma (PDAC) using the extracellular volume fraction (fECV) derived from enhanced CT images, integrating fECV with α-SMA-positive cancer-associated fibroblasts (CAFs) and FAP-positive CAFs to investigate their relationship with clinicopathological characteristics and prognosis of patients with PDAC.

Methods

A retrospective analysis of 124 patients who underwent surgical resection for PDAC was conducted. Immunohistochemistry was applied to determine the expression of α-SMA and FAP. fECV was calculated by attenuation values of PDAC and aorta. The Kaplan–Meier method was used to plot the postoperative overall survival (OS) and disease-free survival (DFS) curves. A Cox proportional hazards regression model was used to develop a predictive model.

Results

High α-SMA (OS: p < 0.001; DFS: p = 0.065) and FAP (OS: p < 0.001; DFS: p < 0.001) expressions and low fECV (OS: p < 0.001; DFS: p < 0.001) predict poor prognosis. Patients with co-high expression of α-SMA and FAP had worse OS and DFS. Multivariable analysis identified α-SMA (OS: hazard ratio [HR], 2.34 [95% CI, 1.30–4.21], p = 0.005), FAP (OS: HR, 4.43 [95% CI, 2.72–7.19], p < 0.001), and fECV (OS: HR, 0.58 [95% CI, 0.37–0.90], p = 0.015) as independent predictors of prognosis. The predictive model established by combining fECV with α-SMA and FAP in this study cohort demonstrated the best predictive value.

Conclusions

The integration of fECV with α-SMA and FAP expressions offers a robust method for predicting clinical outcomes in PDAC patients, potentially guiding treatment strategies.

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细胞外体积分数结合α-SMA和FAP病理特征预测胰管腺癌患者术后预后及评价化疗效果

目的：本研究旨在利用增强CT图像获得的细胞外体积分数（fECV）评价胰腺导管腺癌（PDAC）的细胞外基质，将fECV与α- sma阳性的癌相关成纤维细胞（CAFs）和fap阳性的CAFs结合，探讨其与PDAC患者临床病理特征和预后的关系。方法：回顾性分析124例手术切除的PDAC患者。免疫组化法检测α-SMA和FAP的表达。通过PDAC和主动脉的衰减值计算fECV。Kaplan-Meier法绘制术后总生存期（OS）和无病生存期（DFS）曲线。采用Cox比例风险回归模型建立预测模型。结论：fECV与α-SMA和FAP表达的整合为预测PDAC患者的临床结果提供了一种可靠的方法，可能指导治疗策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.