Spectral CT-derived extracellular volume fraction for pathological characterization and noninvasive T staging in colon cancer

IF 3.9 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
European Journal of Radiology Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI:10.1016/j.ejrad.2026.112722
Zhanrong Shen , Jiong Liu , Ruidan Yang , Yinfei Fan , Shuanglu Wang , Yue Shu , Jian Shu
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引用次数: 0

Abstract

Objectives

To investigate the association between the extracellular volume fraction (ECV) quantified by spectral CT and multiparameter pathological features (Ki-67, mismatch repair status, WHO Grade, Perineural/Lymphovascular Invasion, pT/pN Stage) in colon cancer, and to develop a noninvasive preoperative prediction model for any indicator showing a significant association.

Methods

This single-center retrospective study enrolled 158 patients with pathologically confirmed colon cancer. All patients underwent contrast-enhanced spectral CT and hematocrit testing within 1 week before surgery for ECV calculation. Univariate logistic regression was used to evaluate associations between ECV and each pathological feature. For pathological features showing significant associations, three predictive models were developed: a clinical model, an ECV-only model and a fusion model. Models were trained using elastic net-regularized logistic regression with stratified 3-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and calibration.

Results

Univariate analysis showed that spectral CT-derived ECV was significantly associated with pathological T (pT) stage (OR = 1.07, 95% CI: 1.01–1.13, P = 0.02), with higher ECV values in advanced pT3–T4 tumors. No statistically significant associations were observed with other pathological features (P > 0.05). For pT staging prediction, the fusion model achieved the highest discrimination performance (mean AUC = 0.815, 95% CI: 0.69–0.96) and best calibration (Brier score = 0.225), numerically outperforming the clinical model (AUC = 0.772) and the ECV-only model (AUC = 0.699). Decision curve analysis suggested that the fusion model provided higher net clinical benefit than single-modality approaches.

Conclusion

 Spectral CT–derived ECV is associated with invasion depth in colon cancer. A multimodal model integrating ECV with clinical indicators showed promising internal performance for preoperative pT risk estimation, providing proof-of-concept for future validation studies.
光谱ct衍生的细胞外体积分数用于结肠癌的病理特征和非侵入性T分期。
目的:探讨光谱CT量化的细胞外体积分数(ECV)与结肠癌多参数病理特征(Ki-67、错配修复状态、WHO分级、周围神经/淋巴血管浸润、pT/pN分期)之间的关系,并为任何显示显著相关性的指标建立无创术前预测模型。方法:本单中心回顾性研究纳入158例经病理证实的结肠癌患者。所有患者术前1周内均行造影增强频谱CT和红细胞压积检查,计算ECV。采用单因素logistic回归评估ECV与各病理特征之间的关系。对于表现出显著相关性的病理特征,我们开发了三种预测模型:临床模型、ecv模型和融合模型。模型使用弹性网络正则化逻辑回归进行训练,并进行分层三重交叉验证。使用受试者工作特征曲线下面积(AUC)、Brier评分和校准来评估模型性能。结果:单因素分析显示,光谱ct衍生的ECV与病理T (pT)分期显著相关(OR = 1.07, 95% CI: 1.01-1.13, P = 0.02),晚期pT3-T4肿瘤的ECV值较高。与其他病理特征无统计学意义(P < 0.05)。对于pT分期预测,融合模型具有最高的识别性能(平均AUC = 0.815, 95% CI: 0.69-0.96)和最佳校准(Brier评分= 0.225),在数值上优于临床模型(AUC = 0.772)和仅ecv模型(AUC = 0.699)。决策曲线分析表明,融合模式比单一模式提供更高的净临床效益。结论:频谱ct衍生的ECV与结肠癌的浸润深度有关。将ECV与临床指标相结合的多模态模型在术前pT风险评估中显示出良好的内部性能,为未来的验证研究提供了概念证明。
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
CiteScore
6.70
自引率
3.00%
发文量
398
审稿时长
42 days
期刊介绍: European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.
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