多模态CT在结直肠癌淋巴结转移评估中的研究进展

王晗; 郝粉娥

doi:10.19732/j.cnki.2096-6210.2026.02.012

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多模态CT在结直肠癌淋巴结转移评估中的研究进展

综述 | 更新时间：2026-05-07

- 多模态CT在结直肠癌淋巴结转移评估中的研究进展
- Advances in multimodal CT for assessing lymph node metastasis in colorectal cancer
- 肿瘤影像学 2026年35卷第2期页码：319-325
- 作者机构：
  
  1.内蒙古医科大学附属医院影像诊断科，内蒙古呼和浩特 010050
  2.内蒙古医科大学第一临床医学院，内蒙古呼和浩特 010059
- 作者简介：
- 基金信息：
  
  内蒙古自治区医师协会临床医学研究和临床新技术推广项目(YSXH2024KYF042);内蒙古医科大学创新创业项目(S20241013201;202410132014)
- DOI：10.19732/j.cnki.2096-6210.2026.02.012
  中图分类号： R735.3;R445.3
- 收稿：2025-09-01，
  
  修回：2025-11-16，
  
  纸质出版：2026-04-28
- 稿件说明：
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王　晗, 郝粉娥. 多模态CT在结直肠癌淋巴结转移评估中的研究进展［J］. 肿瘤影像学, 2026, 35(2): 319-325.

WANG H, HAO　F E.Citation: Advances in multimodal CT for assessing lymph node metastasis in colorectal cancer［J］. Oncoradiology, 2026, 35(2): 319-325.
王　晗, 郝粉娥. 多模态CT在结直肠癌淋巴结转移评估中的研究进展［J］. 肿瘤影像学, 2026, 35(2): 319-325. DOI： 10.19732/j.cnki.2096-6210.2026.02.012.

WANG H, HAO　F E.Citation: Advances in multimodal CT for assessing lymph node metastasis in colorectal cancer［J］. Oncoradiology, 2026, 35(2): 319-325. DOI： 10.19732/j.cnki.2096-6210.2026.02.012.

摘要

结直肠癌（colorectal cancer，CRC）发病率近年来逐步升高。评估CRC患者淋巴结转移（lymph node metastasis，LNM）是疾病分期的关键，直接影响治疗选择和患者预后评估。随着医学影像学技术的发展，计算机体层成像（computed tomography，CT）作为临床常规检查手段，在LNM评估中至关重要。从传统形态学评估逐步向定量化、功能化及智能化方向演变。常规CT通过淋巴结短径、形态、边界及强化模式等特征评估转移，灵敏度和特异度受限。双能量CT（dual-energy CT，DECT）等功能成像技术的出现，为淋巴结内部成分变化、血供特征及微观结构异质性的无创评估提供了新思路。近些年研究已证实碘浓度（iodine concentration，IC）、标准化碘浓度（normalized iodine concentration，NIC）、有效原子序数（effective atomic number，

eff

）和能谱曲线斜率等参数，可以有效地提高术前LNM的预测准确度。人工智能（artificial Intelligence，AI）的应用进一步丰富了诊断参数，推动淋巴结研究发展。新兴的AI技术在淋巴结自动检测、特征自动提取和精准预测方面表现出良好潜力。本文旨在综述CT技术及AI在CRC患者LNM评估中的优势与不足，为临床诊疗与后续研究提供参考指导，旨在实现更高效、精准的淋巴结评估和个体化治疗决策。

Abstract

The incidence of colorectal cancer (CRC) has been gradually increasing in recent years. Assessing lymph node metastasis (LNM) in CRC patients is critical for disease staging

directly influencing treatment selection and prognosis evaluation. With advances in medical imaging technology

computed tomography (CT) has become a routine clinical examination method

playing a vital role in LNM assessment. The approach has evolved from traditional morphological assessment toward quantification

functional analysis

and intelligent evaluation. Conventional CT assesses metastasis based on features such as lymph node short axis diameter

morphology

margins

and enhancement patterns

but its sensitivity and specificity remain limited. The emergence of functional imaging techniques like dual-energy CT (DECT) offers new avenues for non-invasive evaluation of internal lymph node composition c

hanges

blood supply characteristics

and microstructural heterogeneity. Recent studies have confirmed that parameters such as iodine concentration (IC)

normalized iodine concentration (NIC)

effective atomic number (

eff

)

and energy spectrum curve slope can significantly enhance the accuracy of preoperative LNM prediction. The application of artificial intelligence (AI) has further enriched diagnostic parameters and propelled lymph node research. Emerging AI technologies demonstrate promising potential in automated lymph node detection

feature extraction

and precise prediction. This review aimed to summarize the advantages and limitations of CT imaging techniques and AI in assessing LNM in CRC patients

providing reference guidance for clinical practice and future research to achieve more efficient

accurate lymph node evaluation and personalized treatment decisions.

关键词

Keywords

references

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