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Progress in the application of MRI-based radiomics in esophageal cancer
TANG Yuqing  PANG Caifeng  ZHOU Ling  LI Rui 

Cite this article as: TANG Y Q, PANG C F, ZHOU L, et al. Progress in the application of MRI-based radiomics in esophageal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(9): 197-202. DOI:10.12015/issn.1674-8034.2025.09.030.


[Abstract] Esophageal cancer (EC) is one of the the most common malignant tumors of the digestive tract in China. Early diagnosis, staging, and prognosis assessment are of significant importance for improving survival rates. MRI radiomics, by extracting a large number of deep features from MRI images, provides a novel perspective for the diagnosis and treatment of esophageal cancer. In recent years, research on radiomics in esophageal cancer has primarily focused on computed tomography (CT) and positron emission tomography/computed tomography (PET/CT), while studies specifically targeting MRI radiomics are relatively scarce. Corresponding systematic reviews remain limited, with notable deficiencies particularly in areas such as multimodal integration, standardization of multi-center data, and clinical translation. This article systematically reviews the advances in the application of MRI radiomics in esophageal cancer, primarily covering tumor staging, treatment response evaluation, and survival prediction. Relevant studies have found that MRI radiomics demonstrates excellent performance in predicting lymph node metastasis and evaluating treatment efficacy. However, its efficacy in T-staging prediction still falls below that of CT and PET, mainly limited by spatial resolution, insufficient sample size, and heterogeneity of multi-center data.
[Keywords] esophageal cancer；magnetic resonance imaging；radiomics；clinical staging；chemotherapy；radiotherapy；prognostic assessment

TANG Yuqing1   PANG Caifeng1   ZHOU Ling2   LI Rui1*  

1 Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637007, China

2 School of Medical Imaging, North Sichuan Medical College, Nanchong 637007, China

Corresponding author: LI R, E-mail: ddtwg_nsmc@163.com

Conflicts of interest   None.

Received  2025-03-24
Accepted  2025-08-25
DOI: 10.12015/issn.1674-8034.2025.09.030
Cite this article as: TANG Y Q, PANG C F, ZHOU L, et al. Progress in the application of MRI-based radiomics in esophageal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(9): 197-202. DOI:10.12015/issn.1674-8034.2025.09.030.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
WATERS J K, REZNIK S I. Update on management of squamous cell esophageal cancer[J]. Curr Oncol Rep, 2022, 24(3): 375-385. DOI: 10.1007/s11912-021-01153-4.
[3]
SHAHEEN N J, FALK G W, IYER P G, et al. Diagnosis and management of barrett's esophagus: an updated ACG guideline[J]. Am J Gastroenterol, 2022, 117(4): 559-587. DOI: 10.14309/ajg.0000000000001680.
[4]
XIAO N P, PEI Z D, LU W H, et al. Editorial: Application of radiomics in understanding tumor biological behaviors and treatment response[J/OL]. Front Oncol, 2023, 13: 1257447 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/37664068/. DOI: 10.3389/fonc.2023.1257447.
[5]
ZHANG W H, ZHUANG D M, WEI W Z, et al. The 100 most-cited radiomics articles in cancer research: a bibliometric analysis[J/OL]. Clin Imaging, 2025, 121: 110442 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/40086035/. DOI: 10.1016/j.clinimag.2025.110442.
[6]
ZHU Y, ZHANG Z Z, BAI G J, et al. Delta/pre-radiomics based on enhanced CT predicts complete response in locally advanced esophageal squamous cell carcinoma[J]. Am J Transl Res, 2025, 17(1): 162-177. DOI: 10.62347/WQYO9624.
[7]
LI H J, LIANG S M, CUI M X, et al. A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics[J/OL]. BMC Cancer, 2025, 25(1): 298 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39972430/. DOI: 10.1186/s12885-025-13697-w.
[8]
QI W X, LI S Y, XIAO J F, et al. A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors[J/OL]. Front Immunol, 2024, 15: 1351750 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/38352868/. DOI: 10.3389/fimmu.2024.1351750.
[9]
HINZPETER R, MIRSHAHVALAD S ALI, KULANTHAIVELU R, et al. Gastro-esophageal cancer: can radiomic parameters from baseline 18F-FDG-PET/CT predict the development of distant metastatic disease [J/OL]. Diagnostics (Basel), 2024, 14(11): 1205 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/38893731/. DOI: 10.3390/diagnostics14111205.
[10]
YU L Y, HAN S, LIU Z X. Research progress on the application of PET/CT radiomics in esophageal cancer[J]. Chin J New Clin Med, 2024, 17(6): 709-712. DOI: 10.3969/j.issn.1674-3806.2024.06.23.
[11]
WU C C, CHEN D S, QIANG J, et al. Application of imaging in diagnosis and treatment of esophageal cancer[J]. J Esophageal Dis, 2022, 4(2): 124-127, 160. DOI: 10.15926/j.cnki.issn2096-7381.2022.02.011.
[12]
LEE S L, YADAV P, STAREKOVA J, et al. Diagnostic performance of MRI for esophageal carcinoma: a systematic review and meta-analysis[J]. Radiology, 2021, 299(3): 583-594. DOI: 10.1148/radiol.2021202857.
[13]
PELLAT A, DOHAN A, SOYER P, et al. The role of magnetic resonance imaging in the management of esophageal cancer[J/OL]. Cancers (Basel), 2022, 14(5): 1141 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/35267447/. DOI: 10.3390/cancers14051141.
[14]
SAH B R, OWCZARCZYK K, SIDDIQUE M, et al. Radiomics in esophageal and gastric cancer[J]. Abdom Radiol (NY), 2019, 44(6): 2048-2058. DOI: 10.1007/s00261-018-1724-8.
[15]
Abdominal Group of the Radiology Branch of the Chinese Medical Association, ZHAO X M, QU J R. Expert consensus on MRI examination and structured report of esophageal cancer[J]. Chin J Radiol, 2023, 57(8): 836-843. DOI: 10.3760/cma.j.cn112149-20220823-00692.
[16]
XU Y H, LU P, GAO M C, et al. Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer[J]. World J Radiol, 2023, 15(7): 216-225. DOI: 10.4329/wjr.v15.i7.216.
[17]
HU S L, LI Y L, FAN X P. Predictive value of simulated CT radiomics combined with ipsilateral lung dosimetry parameters for radiation pneumonitis in patients with esophageal cancer: a machine learning-based retrospective study[J]. Int J Gen Med, 2024, 17: 4127-4140. DOI: 10.2147/IJGM.S475302.
[18]
LI C Y, ZHANG J, NING B D, et al. Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy[J/OL]. Radiat Oncol, 2024, 19(1): 72 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/38851718/. DOI: 10.1186/s13014-024-02462-1.
[19]
PUTTANAWARUT C, SIRIRUTBUNKAJORN N, TAWONG N, et al. Radiomic and dosiomic features for the prediction of radiation pneumonitis across esophageal cancer and lung cancer[J/OL]. Front Oncol, 2022, 12: 768152 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/35251959/. DOI: 10.3389/fonc.2022.768152.
[20]
VERMERSCH M, EMSEN B, MONNET A, et al. Chest PET/MRI in solid cancers: comparing the diagnostic performance of a free-breathing 3D-T1-GRE stack-of-stars volume interpolated breath-hold examination (StarVIBE) acquisition with that of a 3D-T1-GRE volume interpolated breath-hold examination (VIBE) for chest staging during whole-body PET/MRI[J]. J Magn Reson Imaging, 2022, 55(6): 1683-1693. DOI: 10.1002/jmri.27981.
[21]
WANG H Y, QIANG J, CHEN D S, et al. Advances in magnetic resonance imaging studies of esophageal cancer[J]. J Esophageal Dis, 2022, 4(2): 128-132. DOI: 10.15926/j.cnki.issn2096-7381.2022.02.012.
[22]
O'SHEA R J, ROOKYARD C, WITHEY S, et al. Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT[J/OL]. Insights Imaging, 2022, 13(1): 104 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/35715706/. DOI: 10.1186/s13244-022-01245-0.
[23]
CHEN S H. Preoperative evaluation of T stage of esophageal squamous cell carcinoma based on radiomics of enhanced MRI[D]. Chongqing: Chongqing Medical University, 2020. DOI: 10.27674/d.cnki.gcyku.2020.001145.
[24]
LIU S. Artificial Intelligence-based T staging of Esophageal Cancer by Magnetic Resonance Imaging[D]. Shanghai: East China Normal University, 2021. DOI: 10.27149/d.cnki.ghdsu.2021.000853.
[25]
QU J R, SHEN C, QIN J J, et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer[J]. Eur Radiol, 2019, 29(2): 906-914. DOI: 10.1007/s00330-018-5583-z.
[26]
YANG R H, LIAO Y T, FAN W X, et al. Predictive value of MRI radiomics models for lymph node metastasis in patients with esophageal cancer[J]. Int J Med Radiol, 2022, 45(6): 626-631. DOI: 10.19300/j.2022.L19528.
[27]
CHEN Y L, LI R, CHEN T W, et al. Whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced MRI in resectable oesophageal squamous cell carcinoma can predict T-stage and regional lymph node metastasis[J]. Eur J Radiol, 2019, 112: 112-120. DOI: 10.1016/j.ejrad.2019.01.012.
[28]
YANG R H, LIAO Y T, FAN W X, et al. The value of multimodal MRI radiomics in predicting preoperative staging of esophageal cancinoma[J]. J Pract Radiol, 2022, 38(8): 1252-1255. DOI: 10.3969/j.issn.1002-1671.2022.08.008.
[29]
BAIOCCO S, SAH B R, MALLIA A, et al. Exploratory radiomic features from integrated 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer[J]. Eur J Nucl Med Mol Imaging, 2019, 46(7): 1478-1484. DOI: 10.1007/s00259-019-04306-7.
[30]
SUN N N, GE X L, LIU X S, et al. Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma[J]. Radiol Med, 2020, 125(2): 165-176. DOI: 10.1007/s11547-019-01081-1.
[31]
HIRATA A, HAYANO K, OHIRA G, et al. Volumetric histogram analysis of apparent diffusion coefficient for predicting pathological complete response and survival in esophageal cancer patients treated with chemoradiotherapy[J]. Am J Surg, 2020, 219(6): 1024-1029. DOI: 10.1016/j.amjsurg.2019.07.040.
[32]
QU J R, MA L, LU Y N, et al. DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer[J/OL]. Discov Oncol, 2022, 13(1): 3 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/35201487/. DOI: 10.1007/s12672-022-00464-7.
[33]
LU S, WANG C L, LIU Y, et al. The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma[J]. Eur Radiol, 2024, 34(1): 485-494. DOI: 10.1007/s00330-023-10040-4.
[34]
LIU Y S, WANG Y, HU X Y, et al. Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma[J/OL]. Insights Imaging, 2024, 15(1): 277 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39546168/. DOI: 10.1186/s13244-024-01851-0.
[35]
CHU F N, LIU Y, LIU Q P, et al. Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma[J]. Eur Radiol, 2022, 32(9): 5930-5942. DOI: 10.1007/s00330-022-08776-6.
[36]
CHU F N. Radiomics models based on CT and MRI to predict survival in patients with esophageal squamous cell carcinoma[D]. Zhengzhou: Zhengzhou University, 2023.
[37]
LI Y, YANG L, GU X L, et al. Computed tomography radiomics identification of T1-2 and T3-4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional [J]. Abdom Radiol (NY), 2024, 49(1): 288-300. DOI: 10.1007/s00261-023-04070-1.
[38]
LEI X Y, CAO Z, WU Y B, et al. Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics[J/OL]. Insights Imaging, 2023, 14(1): 174 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/37840068/. DOI: 10.1186/s13244-023-01528-0.
[39]
RAMOS-FRESNEDO A, PHILLIPS A L, CANTRELL M C, et al. Cancer recurrence and survival among patients who underwent neoadjuvant treatment and surgery for esophageal cancer: a single-institution 10-year experience[J/OL]. Surgery, 2025, 179: 108901 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39490255/. DOI: 10.1016/j.surg.2024.07.092.
[40]
CHEN L, OUYANG Y, LIU S, et al. Radiomics analysis of lymph nodes with esophageal squamous cell carcinoma based on deep learning[J/OL]. J Oncol, 2022, 2022: 8534262 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/36147442/. DOI: 10.1155/2022/8534262.
[41]
WU Y P, WU L, OU J, et al. Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes[J]. Abdom Radiol, 2025, 50(3): 1123-1132. DOI: 10.1007/s00261-024-04585-1.
[42]
JANNATDOUST P, VALIZADEH P, PAHLEVAN-FALLAHY M T, et al. Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer[J/OL]. Clin Imaging, 2024, 113: 110225 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/38905878/. DOI: 10.1016/j.clinimag.2024.110225.
[43]
CHEN T W, CAO J M. Update of AJCC guidelines for esophageal and esophagogastric junction cancer[J]. Med J West China, 2021, 33(4): 473-477. DOI: 10.3969/j.issn.1672-3511.2021.04.002.
[44]
YANG H, WANG F, HALLEMEIER C L, et al. Oesophageal cancer[J]. Lancet, 2024, 404(10466): 1991-2005. DOI: 10.1016/s0140-6736(24)02226-8.
[45]
PAPE M, VISSERS P A J, BEEREPOOT L V, et al. A population-based study in resected esophageal or gastroesophageal junction cancer aligned with CheckMate 577[J/OL]. Ther Adv Med Oncol, 2022, 14: 17588359221075495 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/35237351/. DOI: 10.1177/17588359221075495.
[46]
KAO Y S, HSU Y. A meta-analysis for using radiomics to predict complete pathological response in esophageal cancer patients receiving neoadjuvant chemoradiation[J]. In Vivo, 2021, 35(3): 1857-1863. DOI: 10.21873/invivo.12448.
[47]
LU Z H, LI Y S, HU W X, et al. Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma[J/OL]. Front Oncol, 2024, 14: 1347650 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39544301/. DOI: 10.3389/fonc.2024.1347650.
[48]
ZHENG Y T, MEI P Y, WANG M L, et al. CT-based delta-radiomics for predicting pathological response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma: a multicenter study[J/OL]. BMC Med Imaging, 2024, 24(1): 329 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39627736/. DOI: 10.1186/s12880-024-01503-1.
[49]
ZHAO B, WANG Y Q, ZHU H T, et al. Integrating tumour and lymph node radiomics features for predicting disease-free survival in locally advanced esophageal squamous cell cancer after neoadjuvant chemotherapy and complete resection[J/OL]. Eur J Surg Oncol, 2025, 51(3): 109547 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39729864/. DOI: 10.1016/j.ejso.2024.109547.
[50]
GONG J, LU J, ZHANG W, et al. A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy: a multicenter study[J/OL]. J Transl Med, 2024, 22(1): 1108 [2025-08-05]. https://pubmed.ncbi.nlm.nih.gov/39639328/. DOI: 10.1186/s12967-024-05897-y.

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