12-04-2025 | Artificial Intelligence | Review

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis

Authors: Zhiqiang Bai, Lumin Xu, Zujun Ding, Yi Cao, Zepeng Wang, Wenjie Yang, Wei Xu, Hang Li

Abstract

Objective

This meta-analysis aims to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) in the preoperative detection of lymph node metastasis (LNM) in patients with rectal cancer and to compare it with the diagnostic performance of radiologists.

Methods

A thorough literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to September 2024. The selected studies focused on the diagnostic performance of MRI-based AI in detecting rectal cancer LNM. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, each reported with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I² statistic. Furthermore, the modified quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was applied to assess the methodological quality of the selected studies.

Results

Seventeen studies were included in this meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) for MRI-based AI in detecting preoperative LNM in rectal cancer were 0.71 (95% CI: 0.66–0.74), 0.71 (95% CI: 0.67–0.75), and 0.77 (95% CI: 0.73–0.80), respectively. For radiologists, these values were 0.64 (95% CI: 0.49–0.77), 0.72 (95% CI: 0.62–0.80), and 0.74 (95% CI: 0.68–0.80). Both analyses showed no significant publication bias (p > 0.05).

Conclusions

MRI-based AI demonstrates diagnostic performance similar to that of radiologists. The high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results and assess their practical clinical value.

Key Points

Question How effective is MRI-based AI in detecting LNM in rectal cancer patients compared to traditional radiology methods?

Findings The diagnostic performance of MRI-based AI is comparable to radiologists, with pooled sensitivity and specificity both at 0.71, indicating moderate accuracy.

Clinical relevance Integrating MRI-based AI can enhance diagnostic efficiency in identifying LNM, especially in settings with limited access to skilled radiologists, but requires further validation.

Available only for authorised users

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492CrossRefPubMed

Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin. https://doi.org/10.3322/caac.21708.

Jin M, Frankel WL (2018) Lymph node metastasis in colorectal cancer. Surg Oncol Clin N Am 27:401–412. https://doi.org/10.1016/j.soc.2017.11.011CrossRefPubMed

Nagtegaal ID, Knijn N, Hugen N et al (2017) Tumor deposits in colorectal cancer: improving the value of modern staging-a systematic review and meta-analysis. J Clin Oncol 35:1119–1127. https://doi.org/10.1200/jco.2016.68.9091CrossRefPubMed

Yagi R, Shimada Y, Kameyama H et al (2016) Clinical significance of extramural tumor deposits in the lateral pelvic lymph node area in low rectal cancer: a retrospective study at two institutions. Ann Surg Oncol 23:552–558. https://doi.org/10.1245/s10434-016-5379-9CrossRefPubMedPubMedCentral

Benson AB, Venook AP, Al-Hawary MM et al (2022) Rectal cancer, version 2.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 20:1139–1167. https://doi.org/10.6004/jnccn.2022.0051CrossRefPubMed

Borgheresi A, De Muzio F, Agostini A et al (2022) Lymph nodes evaluation in rectal cancer: where do we stand and future perspective. J Clin Med 11:2599. https://doi.org/10.3390/jcm11092599CrossRefPubMedPubMedCentral

Zhuang Z, Zhang Y, Wei M, Yang X, Wang Z (2021) Magnetic resonance imaging evaluation of the accuracy of various lymph node staging criteria in rectal cancer: a systematic review and meta-analysis. Front Oncol 11:709070. https://doi.org/10.3389/fonc.2021.709070CrossRefPubMedPubMedCentral

Van Cutsem E, Verheul HM, Flamen P et al (2016) Imaging in colorectal cancer: progress and challenges for the clinicians. Cancers (Basel) 8:81. https://doi.org/10.3390/cancers8090081CrossRefPubMed

10.

Kaur H, Choi H, You YN et al (2012) MR imaging for preoperative evaluation of primary rectal cancer: practical considerations. Radiographics 32:389–409. https://doi.org/10.1148/rg.322115122CrossRefPubMed

11.

Heijnen LA, Lambregts DM, Mondal D et al (2013) Diffusion-weighted MR imaging in primary rectal cancer staging demonstrates but does not characterise lymph nodes. Eur Radiol 23:3354–3360. https://doi.org/10.1007/s00330-013-2952-5CrossRefPubMed

12.

Burdan F, Sudol-Szopinska I, Staroslawska E et al (2015) Magnetic resonance imaging and endorectal ultrasound for diagnosis of rectal lesions. Eur J Med Res. https://doi.org/10.1186/s40001-014-0078-0

13.

Resch A, Langner C (2013) Lymph node staging in colorectal cancer: old controversies and recent advances. World J Gastroenterol. https://doi.org/10.3748/wjg.v19.i46.8515

14.

Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. https://doi.org/10.1186/s41747-018-0061-6.

15.

Yang Y, Xu Z, Cai Z et al (2024) Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: a dual-center study. J Cancer Res Clin Oncol. https://doi.org/10.1007/s00432-024-05986-x.

16.

Castiglioni I, Rundo L, Codari M et al (2021) AI applications to medical images: from machine learning to deep learning. Phys Med. https://doi.org/10.1016/j.ejmp.2021.02.006.

17.

Li C, Yin J (2021) Radiomics based on T2-weighted imaging and apparent diffusion coefficient images for preoperative evaluation of lymph node metastasis in rectal cancer patients. Front Oncol. https://doi.org/10.3389/fonc.2021.671354.

18.

Abbaspour E, Karimzadhagh S, Monsef A, Joukar F, Mansour-Ghanaei F, Hassanipour S (2024) Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis. Int J Surg. https://doi.org/10.1097/js9.0000000000001239.

19.

Li J, Zhou Y, Wang X, Zhou M, Chen X, Luan K (2021) An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02863-2.

20.

Su Y, Zhao H, Liu P et al (2022) A nomogram model based on MRI and radiomic features developed and validated for the evaluation of lymph node metastasis in patients with rectal cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-022-03672-5.

21.

McInnes MDF, Moher D, Thombs BD et al (2018) Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. https://doi.org/10.1001/jama.2017.19163.

22.

Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. https://doi.org/10.7326/0003-4819-155-8-201110180-00009.

23.

Wolff RF, Moons KGM, Riley RD et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. https://doi.org/10.7326/m18-1376.

24.

Dong X, Ren G, Chen Y et al (2023) Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer. Front Oncol. https://doi.org/10.3389/fonc.2023.1194120.

25.

Hao Y, Zheng J, Li W et al (2024) Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics. Eur Radiol. https://doi.org/10.1007/s00330-024-10958-3.

26.

Jia H, Jiang X, Zhang K et al (2022) A nomogram of combining IVIM-DWI and MRI radiomics from the primary lesion of rectal adenocarcinoma to assess nonenlarged lymph node metastasis preoperatively. J Magn Reson Imaging 56:658–667. https://doi.org/10.1002/jmri.28068CrossRefPubMed

27.

Kasai S, Shiomi A, Shimizu H et al (2024) Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study. BJS Open. https://doi.org/10.1093/bjsopen/zrae073

28.

Li H, Chen XL, Liu H, Lu T, Li ZL (2022) MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer. Front Oncol. https://doi.org/10.3389/fonc.2022.1087882

29.

Li Y, Zeng C, Du Y (2023) Use of a radiomics-clinical model based on magnetic diffusion-weighted imaging for preoperative prediction of lymph node metastasis in rectal cancer patients. Medicine (Baltimore). https://doi.org/10.1097/md.0000000000036004

30.

Liu X, Yang Q, Zhang C et al (2020) Multiregional-based magnetic resonance imaging radiomics combined with clinical data improves efficacy in predicting lymph node metastasis of rectal cancer. Front Oncol. https://doi.org/10.3389/fonc.2020.585767

31.

Meng X, Xia W, Xie P et al (2019) Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol. https://doi.org/10.1007/s00330-018-5763-x

32.

Song G, Li P, Wu R et al (2022) Development and validation of a high-resolution T2WI-based radiomic signature for the diagnosis of lymph node status within the mesorectum in rectal cancer. Front Oncol. https://doi.org/10.3389/fonc.2022.945559

33.

Sun Y, Lu Z, Yang H et al (2024) Prediction of lateral lymph node metastasis in rectal cancer patients based on MRI using clinical, deep transfer learning, radiomic, and fusion models. Front Oncol. https://doi.org/10.3389/fonc.2024.1433190

34.

Wei Q, Yuan W, Jia Z et al (2023) Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. Abdom Radiol (NY). https://doi.org/10.1007/s00261-022-03731-x

35.

Yang YS, Feng F, Qiu YJ, Zheng GH, Ge YQ, Wang YT (2021) High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02733-x

36.

Zhao W, Xu H, Zhao R et al (2024) MRI-based radiomics model for preoperative prediction of lateral pelvic lymph node metastasis in locally advanced rectal cancer. Acad Radiol. https://doi.org/10.1016/j.acra.2023.07.016

37.

Zhuang Z, Zhang Y, Yang X, Deng X, Wang Z (2024) T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-024-04209-8

38.

Esteva A, Robicquet A, Ramsundar B et al (2019) A guide to deep learning in healthcare. Nat Med. https://doi.org/10.1038/s41591-018-0316-z

39.

Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal. https://doi.org/10.1016/j.media.2017.07.005

40.

Liu Y, Chen PC, Krause J, Peng L (2019) How to read articles that use machine learning: users’ guides to the medical literature. JAMA. https://doi.org/10.1001/jama.2019.16489

41.

Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17:195. https://doi.org/10.1186/s12916-019-1426-2CrossRefPubMedPubMedCentral

42.

Hoshino N, Murakami K, Hida K, Sakamoto T, Sakai Y (2019) Diagnostic accuracy of magnetic resonance imaging and computed tomography for lateral lymph node metastasis in rectal cancer: a systematic review and meta-analysis. Int J Clin Oncol 24:46–52. https://doi.org/10.1007/s10147-018-1349-5CrossRefPubMed

43.

Meng Y, Ai Q, Hu Y et al (2024) Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer. Abdom Radiol (NY) 49:1805–1815. https://doi.org/10.1007/s00261-024-04204-zCrossRefPubMed

44.

Wang PP, Deng CL, Wu B (2021) Magnetic resonance imaging-based artificial intelligence model in rectal cancer. World J Gastroenterol 27:2122–2130. https://doi.org/10.3748/wjg.v27.i18.2122CrossRefPubMedPubMedCentral

45.

Hamabe A, Takemasa I, Ishii M et al (2024) The potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial. J Gastroenterol 59:896–904. https://doi.org/10.1007/s00535-024-02133-8CrossRefPubMedPubMedCentral

46.

Yin Z, Yao C, Zhang L, Qi S (2023) Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med 10:1128084. https://doi.org/10.3389/fmed.2023.1128084CrossRef

47.

Steyerberg EW, Harrell FE Jr (2016) Prediction models need appropriate internal, internal-external, and external validation, Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69:245–247. https://doi.org/10.1016/j.jclinepi.2015.04.005CrossRefPubMed

Title: Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis
Authors: Zhiqiang Bai
Lumin Xu
Zujun Ding
Yi Cao
Zepeng Wang
Wenjie Yang
Wei Xu
Hang Li
Publication date: 12-04-2025
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Rectal Cancer
Rectal Cancer
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Metastasis
Published in: European Radiology
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-025-11519-y

Navigating neuroimaging in Alzheimer disease care

Springer Medicine

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis

Abstract

Objective

Methods

Results

Conclusions

Key Points

Navigating neuroimaging in Alzheimer disease care

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

Key Points

Please log in to get access to this content