Development and prospects of MRI in pulmonary nodule evaluation

HU Hao; WANG Yubo; HE Zhengying; DING Zhiyong; YE Xiaodan; YANG Bin

doi:10.19732/j.cnki.2096-6210.2026.01.013

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Development and prospects of MRI in pulmonary nodule evaluation

Review | 更新时间：2026-03-25

- Development and prospects of MRI in pulmonary nodule evaluation
- Oncoradiology Vol. 35, Issue 1, Pages: 103-110(2026)
- 作者机构：
  
  1.昆明医科大学附属甘美医院医学影像中心，云南昆明650051
  2.昆明医科大学研究生院放射影像学专业，云南昆明650500
  3.迪庆藏族自治州人民医院影像中心，云南迪庆674400
  4.曲靖市妇幼保健院影像中心，云南曲靖655000
  5.复旦大学附属中山医院放射诊断科，上海200032
- 作者简介：
  
  YANG Bin　E-mail: yyangbinapple@163.com
- 基金信息：
  
  National Natural Science Foundation of China(82160348);Yunnan Province Major Special Plan(202302AA310018-D-8);Yunnan Province's "Xingdian Talent Support Program" Youth Talent Project(XDYC-QNRC-2022-0608);2024 Yunnan Province Medical Discipline Leader Training Program(D-202405);2023 Kunming Health Science and Technology Talent Training Project (Medical Technology Center)
- DOI：10.19732/j.cnki.2096-6210.2026.01.013
  CLC： R734.2;R445.2
- Received：14 August 2025，
  
  Revised：2025-10-30，
  
  Published：28 February 2026
- 稿件说明：
移动端阅览
胡　皓, 王宇博, 和正英, 等. 肺结节MRI发展历程与展望［J］. 肿瘤影像学, 2026, 35(1): 103-110.

HU H, WANG Y B, HE Z Y,Citation: et al. Development and prospects of MRI in pulmonary nodule evaluation［J］. Oncoradiology, 2026, 35(1): 103-110.
胡　皓, 王宇博, 和正英, 等. 肺结节MRI发展历程与展望［J］. 肿瘤影像学, 2026, 35(1): 103-110. DOI： 10.19732/j.cnki.2096-6210.2026.01.013.

HU H, WANG Y B, HE Z Y,Citation: et al. Development and prospects of MRI in pulmonary nodule evaluation［J］. Oncoradiology, 2026, 35(1): 103-110. DOI： 10.19732/j.cnki.2096-6210.2026.01.013.

摘要

肺癌是全球范围内导致癌症死亡的首要原因，由于早期症状隐匿，确诊时多已进展。低剂量或标准剂量计算机体层成像（computed tomography，CT）筛查虽提高了早期检出率，但电离辐射暴露问题促使磁共振成像（magnetic resonance imaging，MRI）作为无辐射成像手段受到关注。新型MRI序列在肺结节筛查中展现出优异的抗伪影、高分辨率与小结节识别能力。然而，这些序列在临床推广过程中仍面临标准化参数缺乏、设备依赖高、阅片经验不足等落地难点，限制了其真实世界应用。本文进一步聚焦于此类序列在肺结节早期筛查与定性诊断中的转化瓶颈，尝试构建“性能—适应性—解读”三位一体的评估框架。同时，文章还探讨MRI功能成像与人工智能（artificial intelligence，AI）诊断模型结合的可能性，指出当前AI模型在MRI数据处理上面临训练数据匮乏、序列异构性与特征迁移障碍等关键问题。综上，本文系统分析MRI在肺结节筛查与诊断中的技术进展，特别强调新序列落地难题及AI分析框架缺口，旨在为无辐射肺癌筛查路径提供有针对性的研究思路与实践启示。

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide

and its insidious early symptoms often result in diagnosis at an advanced stage. Although low-dose or standard-dose computed tomography (CT) screening has improved early detection rates

concerns regarding ionizing radiation exposure have increased interest in magnetic resonance imaging (MRI) as a radiation-free alternative. Recent advances in novel MRI sequences have demonstrated promising performance in lung nodule screening

offering reduced susceptibility to artifacts

high spatial resolution

and improved detection of small nodules. However

their clinical implementation remains challenging due to the lack of standardized imaging parameters

strong equipment dependence

and limited reader experience

which restrict their real-world applicability. This review focused on the translational bottlenecks of these novel sequences in early lung nodule screening and qualitative diagnosis

and proposed an integrated evaluation framework encompassing performance

adaptability

and interpretability. Furthermore

the potential integration of MRI functional imaging with artificial intelligence (AI)-based diagnostic models is discussed

highlighting current challenges such as limited training datasets

sequence heterogeneity

and barriers to feature transferability. Overall

this review systematically summarized the technological advances of MRI in lung nodule screening and diagnosis

emphasizing the implementation challenges of emerging sequences and gaps in AI analytical frameworks

with the aim of providing targeted research directions and practical insights for radiation-free lung cancer screening strategies.

关键词

Keywords

references

World Health Organization . Global cancer burden growing, amidst mounting need for services ［EB/OL］. ( 2024-02-01 ) ［ 2025-04-30 ］. https://www.who.int/news/item/01-02-2024-global-cancer-burden-growing-amidst-mounting-need-for-services https://www.who.int/news/item/01-02-2024-global-cancer-burden-growing-amidst-mounting-need-for-services .

ZHOU J L , XU Y , LIU J M , et al . Global burden of lung cancer in 2022 and projections to 2050: incidence and mortality estimates from GLOBOCAN ［J］. Cancer Epidemiol , 2024 , 93 : 102693 .

SIEGEL R L , MILLER K D , WAGLE N S , et al . Cancer statistics, 2023 ［J］. CA A Cancer J Clin , 2023 , 73 ( 1 ): 17 - 48 .

BHAMANI A , CREAMER A , VERGHESE P , et al . Low-dose CT for lung cancer screening in a high-risk population (SUMMIT): a prospective, longitudinal cohort study ［J］. Lancet Oncol , 2025 , 26 ( 5 ): 609 - 619 .

FUSS C . Low-dose CT of the chest: Is high-risk population screening for both lung cancer and cardiovascular disease possible? ［J］. Radiology , 2025 , 314 ( 2 ): e250215 .

CAVION C C , ALTMAYER S , FORTE G C , et al . Diagnostic performance of MRI for the detection of pulmonary nodules: a systematic review and meta-analysis ［J］. Radiol Cardiothorac Imaging , 2024 , 6 ( 2 ): e230241 .

FENG H , SHI G F , LIU H , et al . The application and value of 3 T magnetic resonance imaging in the display of pulmonary nodules ［J］. Front Oncol , 2022 , 12 : 844514 .

OHNO Y , OZAWA Y , NAGATA H , et al . Lung magnetic resonance imaging: technical advancements and clinical applications ［J］. Invest Radiol , 2024 , 59 ( 1 ): 38 - 52 .

LISZEWSKI M C , CIET P , WINANT A J , et al . Magnetic resonance imaging of pediatric lungs and airways: new paradigm for practical daily clinical use ［J］. J Thorac Imaging , 2024 , 39 ( 1 ): 57 - 66 .

YANG S Y , SHAN F , SHI Y X , et al . Sensitivity and specificity of magnetic resonance imaging in routine diagnosis of pulmonary lesions: a comparison with computed tomography ［J］. J Thorac Dis , 2022 , 14 ( 10 ): 3762 - 3772 .

BRUCKMANN N M , KIRCHNER J , MORAWITZ J , et al . Free-breathing 3D Stack of Stars GRE (StarVIBE) sequence for detecting pulmonary nodules in 18 F-FDG PET/MRI ［J］. EJNMMI Phys , 2022 , 9 ( 1 ): 11 .

RENZ D M , HERRMANN K H , KRAEMER M , et al . Ultrashort echo time MRI of the lung in children and adolescents: comparison with non-enhanced computed tomography and standard post-contrast T1w MRI sequences ［J］. Eur Radiol , 2022 , 32 ( 3 ): 1833 - 1842 .

OHNO Y , TAKENAKA D , YOSHIKAWA T , et al . Efficacy of ultrashort echo time pulmonary MRI for lung nodule detection and lung-RADS classification ［J］. Radiology , 2022 , 302 ( 3 ): 697 - 706 .

SANCHEZ F , TYRRELL P N , CHEUNG P , et al . Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo ［J］. Cancer Imaging , 2023 , 23 ( 1 ): 17 .

WANG F N , LIN X , LIN C , et al . Ability of three-dimensional 3-Tesla ultrashort echo time magnetic resonance imaging to display the morphological characteristics of pulmonary nodules: a sensitivity analysis ［J］. Quant Imaging Med Surg , 2023 , 13 ( 3 ): 1792 - 1801 .

JIANG Y H , PU D D , ZHANG X Y , et al . Comparison of diagnostic performance for pulmonary nodule detection between free-breathing spiral ultrashort echo time and free-breathing radial volumetric interpolated breath-hold examination ［J］. BMC Med Imaging , 2025 , 25 ( 1 ): 15 .

LIU Q Y , FENG Z C , LIU W V , et al . Assessment of solid pulmonary nodules or masses using zero echo time MR lung imaging: a prospective head-to-head comparison with CT ［J］. Front Oncol , 2022 , 12 : 812014 .

CHANG C Y , LEE T H , LIU R S , et al . Fractionated deep-inspiration breath-hold ZTE Compared with Free-breathing four-dimensional ZTE for detecting pulmonary nodules in oncological patients underwent PET/MRI ［J］. Sci Rep , 2021 , 11 : 17636 .

ZOU Q , WAN Q , LIU J Q , et al . Image quality and detection efficacy of zero echo time magnetic resonance imaging on Lung-RADS 2 pulmonary ground-glass nodules in comparison to thin-slice fat-saturated T2-weighted imaging ［J］. J Thorac Dis , 2024 , 16 ( 8 ): 5167 - 5179 .

DANG S , HAN D , DUAN H , et al . The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma ［J］. Clin Radiol , 2024 , 79 ( 4 ): 279 - 286 .

GUAN H X , PAN Y Y , WANG Y J , et al . Comparison of various parameters of DWI in distinguishing solitary pulmonary nodules ［J］. Curr Med Sci , 2018 , 38 ( 5 ): 920 - 924 .

KOO C W , LU A M , TAKAHASHI E A , et al . Can MRI contribute to pulmonary nodule analysis? ［J］. J Magn Reson Imaging , 2019 , 49 ( 7 ): e256 - e264 .

USUDA K , ISHIKAWA M , IWAI S , et al . Pulmonary nodule and mass: superiority of MRI of diffusion-weighted imaging and T2-weighted imaging to FDG-PET/CT ［J］. Cancers , 2021 , 13 ( 20 ): 5166 .

ZHOU J X , WEN Y , DING R L , et al . Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions ［J］. Cancer Imaging , 2024 , 24 ( 1 ): 14 .

KUMAR N , SHARMA M , AGGARWAL N , et al . Role of various DW MRI and DCE MRI parameters as predictors of malignancy in solid pulmonary lesions ［J］. Can Assoc Radiol J , 2021 , 72 ( 3 ): 525 - 532 .

FENG H , SHI G F , LIU H , et al . Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules ［J］. Magn Reson Imaging , 2021 , 75 : 100 - 106 .

胡俊蛟，刘梅桃，赵　伟，等 . T1WI star-VIBE增强联合

TWIST-VIBE动态增强MRI对肺结节诊断的应用价值［J］. 中南大学学报（医学版）， 2023 ， 48 （ 4 ）： 581 - 593 .

HU J J ， LIU M T ， ZHAO W ， et al . Value for combination of T1WI star-VIBE with TWIST-VIBE dynamic contrast-enhanced MRI in distinguishing lung nodules ［J］. J Cent South Univ （Med Sci）， 2023 ， 48 （ 4 ）： 581 - 593 .

FANG W , FU W D , ZHOU W M , et al . Discrimination between benign and malignant lung lesions using volumetric quantitative dynamic contrast-enhanced MRI ［J］. Curr Med Imag Former Curr Med Imag Rev , 2023 , 20 : e270723219206 .

BAIS C , MUELLER B , BRADY M F , et al . Tumor microvessel

density as a potential predictive marker for bevacizumab benefit: GOG-0218 biomarker analyses ［J］. J Natl Cancer Inst , 2017 , 109 ( 11 ): djx066 . Doi: 10.1093/jnci/djx066 http://dx.doi.org/10.1093/jnci/djx066 .

FU L , PAN X Y , DING H M , et al . The diagnostic value of vascular architecture in solid solitary pulmonary nodules quantified by dynamic contrast enhanced MRI ［J］. J Thorac Dis , 2025 , 17 ( 1 ): 19 - 30 .

YANG S Y , SHAN F , YAN Q Q , et al . A pilot study of native T1-mapping for focal pulmonary lesions in 3.0 T magnetic resonance imaging: size estimation and differential diagnosis ［J］. J Thorac Dis , 2020 , 12 ( 5 ): 2517 - 2528 .

YAN Q Q , YI Y Q , SHEN J , et al . Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses ［J］. Cancer Cell Int , 2021 , 21 ( 1 ): 539 .

MENG N , SONG C , SUN J , et al . Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18 F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions ［J］. Cancer Imaging , 2024 , 24 ( 1 ): 33 .

FANG T , MENG N , FENG P Y , et al . A comparative study of amide proton transfer weighted imaging and intravoxel incoherent motion MRI techniques versus 18 F-FDG PET to distinguish solitary pulmonary lesions and their subtypes ［J］. J Magn Reson Imaging , 2022 , 55 ( 5 ): 1376 - 1390 .

CHEN Y R , HAN Q J , HUANG Z W , et al . Value of IVIM in differential diagnoses between benign and malignant solitary lung nodules and masses: a meta-analysis ［J］. Front Surg , 2022 , 9 : 817443 .

GAO P , LIU Y Y , SHI C Z , et al . Performing IVIM-DWI using the multifunctional nanosystem for the evaluation of the antitumor microcirculation changes ［J］. MAGMA , 2020 , 33 ( 4 ): 517 - 526 .

SONG M H , YUE Y L , JIN Y F , et al . Intravoxel incoherent motion and ADC measurements for differentiating benign from malignant thyroid nodules: utilizing the most repeatable region of interest delineation at 3.0 T ［J］. Cancer Imaging , 2020 , 20 ( 1 ): 9 .

LI J X , WU B L , HUANG Z , et al . Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions ［J］. Front Oncol , 2023 , 12 : 1082454 .

XIANG L , YANG H , QIN Y , et al . Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions ［J］. Front Oncol , 2023 , 12 : 1075072 .

YANG B , GAO Y Q , LU J , et al . Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules ［J］. Front Oncol , 2023 , 13 : 1212608 .

YANG S Y , WANG Y D , SHI Y X , et al . Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation ［J］. Magn Reson Imaging , 2022 , 85 : 80 - 86 .

MORALES M A , MANNING W J , NEZAFAT R . Present and future innovations in AI and cardiac MRI ［J］. Radiology , 2024 , 310 ( 1 ): e231269 .

JUNG M , RAGHU V K , REISERT M , et al . Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population ［J］. EBioMedicine , 2024 , 110 : 105467 .

MOON J W , YANG E , KIM J H , et al . Predicting non-small-cell lung cancer survival after curative surgery via deep learning of diffusion MRI ［J］. Diagnostics , 2023 , 13 ( 15 ): 2555 .

ZHANG X L , DONG X L , BIN SARIPAN M I , et al . Deep learning PET/CT-based radiomics integrates clinical data: a feasibility study to distinguish between tuberculosis nodules and lung cancer ［J］. Thorac Cancer , 2023 , 14 ( 19 ): 1802 - 1811 .

BAE K , LEE J , JUNG Y , et al . Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection ［J］. Clin Radiol , 2024 , 79 ( 11 ): e1296 - e1303 .

MAIRHÖRMANN B , CASTELBLANCO A , HÄFNER F , et al . Automated MRI lung segmentation and 3D morphologic features for quantification of neonatal lung disease ［J］. Radiol Artif Intell , 2023 , 5 ( 6 ): e220239 .

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