Share:
Share this content in WeChat
X
Review
Research progress of magnetic resonance habitat imaging in common gynecological malignant tumors
ZHOU Heng  LU Ji  YANG Chunxiang  JIANG Tao  ZHANG Tianyi 

Cite this article as: ZHOU H, LU J, YANG C X, et al. Research progress of magnetic resonance habitat imaging in common gynecological malignant tumors[J]. Chin J Magn Reson Imaging, 2026, 17(9): 207-215. DOI:10.12015/issn.1674-8034.2026.09.027.


[Abstract] Cervical cancer, endometrial cancer, and ovarian cancer are the three most common malignancies of the female reproductive system. Their prominent intratumoral heterogeneity is the core reason for difficulties in preoperative precise staging, variations in treatment response, and inaccurate prognostic assessment in some tumors. Habitat imaging, as an extension of radiomics, employs voxel-level clustering of multiparametric magnetic resonance imaging to partition tumors into distinct functional subregions. This approach not only characterizes the spatial heterogeneity within tumors but also noninvasively maps the discrepancies between pathophysiological microenvironment features and molecular biological behaviors, thereby providing a novel imaging method to elucidate tumor biological behavior. This review systematically summarizes the research progress of magnetic resonance habitat imaging in the three major gynecological tumors. It first outlines the pathological basis of tumor heterogeneity and the technical principles of habitat imaging. It then focuses on summarizing the clinical application value of this technique in tumor molecular subtype identification, precise staging assessment, and treatment response prediction. Finally, it addresses common issues in current research: including methodological inconsistency, lack of biological validation, and insufficient evidence for clinical translation, and proposes future research directions. This review aims to offer a new imaging tool for precision diagnosis and treatment of gynecological tumors and to promote the translation of habitat imaging from research to clinical practice.
[Keywords] magnetic resonance imaging;habitat imaging;cervical cancer;endometrial cancer;ovarian cancer;tumor heterogeneity

ZHOU Heng1, 2   LU Ji1, 2*   YANG Chunxiang1, 2   JIANG Tao1, 2   ZHANG Tianyi1, 2  

1 The First College of Clinical Medical Science, China Three Gorges University, Yichang 443000, China

2 Department of Radiology, Yichang Central People's Hospital, Yichang 443000, China

Corresponding author: LU J, E-mail: 15926951408@163.com

Conflicts of interest   None.

Received  2026-04-14
Accepted  2026-08-24
DOI: 10.12015/issn.1674-8034.2026.09.027
Cite this article as: ZHOU H, LU J, YANG C X, et al. Research progress of magnetic resonance habitat imaging in common gynecological malignant tumors[J]. Chin J Magn Reson Imaging, 2026, 17(9): 207-215. DOI:10.12015/issn.1674-8034.2026.09.027.

[1]
HOARE B S, MIKES B A, KHAN Y S. Anatomy, Abdomen and Pelvis: Female Internal Genitals[M/OL]. Treasure Island (FL): StatPearls Publishing, 2026 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/32119488/.
[2]
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.
[3]
SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17-48. DOI: 10.3322/caac.21763.
[4]
KASIUS J C, PIJNENBORG J M A, LINDEMANN K, et al. Risk stratification of endometrial cancer patients: FIGO stage, biomarkers and molecular classification[J/OL]. Cancers, 2021, 13(22): 5848 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/34831000/. DOI: 10.3390/cancers13225848.
[5]
BODALAL Z, TREBESCHI S, NGUYEN-KIM T D L, et al. Radiogenomics: bridging imaging and genomics[J]. Abdom Radiol (NY), 2019, 44(6): 1960-1984. DOI: 10.1007/s00261-019-02028-w.
[6]
CAI Z P, XU Z Y, CHEN Y F, et al. Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study[J]. Int J Surg, 2024, 110(7): 4310-4319. DOI: 10.1097/JS9.0000000000001335.
[7]
WANG X Y, WEI M X, CHEN Y, et al. Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer[J/OL]. Insights Imaging, 2024, 15(1): 281 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/39576435/. DOI: 10.1186/s13244-024-01855-w.
[8]
SCHIAVONE M L, SCARPITTA R, RAVERA F, et al. Liquid biopsy in breast cancer: Redefining precision medicine[J/OL]. J Liq Biopsy, 2025, 9: 100312 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/40740670/. DOI: 10.1016/j.jlb.2025.100312.
[9]
YANG Y, HAN Y, ZHAO S J, et al. Spatial heterogeneity of edema region uncovers survival-relevant habitat of Glioblastoma[J/OL]. Eur J Radiol, 2022, 154: 110423 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/35777079/. DOI: 10.1016/j.ejrad.2022.110423.
[10]
LI M J, DING N, YIN S N, et al. Tumour habitat-based radiomics analysis enhances the ability to predict prostate cancer aggressiveness with biparametric MRI-derived features[J/OL]. Front Oncol, 2025, 15: 1504132 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/40165905/. DOI: 10.3389/fonc.2025.1504132.
[11]
YUAN L, ZHANG J L, MA L N, et al. Prediction of zonal heterogeneity in prostate cancer using multi-parametric magnetic resonance habitat imaging[J]. Chin J Magn Reson Imaging, 2025, 16(11): 142-148. DOI: 10.12015/issn.1674-8034.2025.11.021.
[12]
LEE D H, PARK J E, KIM N, et al. Tumor habitat analysis using longitudinal physiological MRI to predict tumor recurrence after stereotactic radiosurgery for brain metastasis[J]. Korean J Radiol, 2023, 24(3): 235-246. DOI: 10.3348/kjr.2022.0492.
[13]
WANG S X, LIU X W, WU Y, et al. Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study[J/OL]. Front Oncol, 2023, 13: 1252074 [2026-03-28]. https://pubmed.ncbi.nlm.nih.gov/37954078/. DOI: 10.3389/fonc.2023.1252074.
[14]
WANG Q Z, ZHANG Y, WANG T Y, et al. MRI-based habitat analysis for the prediction of progression-free survival in primary spinal tumors[J/OL]. Radiology, 2025, 317(1): e242993 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41147907/. DOI: 10.1148/radiol.242993.
[15]
WU L X, DING N, JI Y D, et al. Habitat analysis in tumor imaging: advancing precision medicine through radiomic subregion segmentation[J]. Cancer Manag Res, 2025, 17: 731-741. DOI: 10.2147/CMAR.S511796.
[16]
JIAO K J, YANG B, CHEN W, et al. Prediction of habitat subregions of the glioblastoma microenvironment based on multimodal MRI radiomics for MGMT promoter methylation expression[J]. Chin J Magn Reson Imaging, 2023, 14(11): 25-30, 76. DOI: 10.12015/issn.1674-8034.2023.11.005.
[17]
BAI X, WANG H Y. Mapping tumor heterogeneity: landscape and challenges in habitat imaging[J]. Chin J Med Imaging, 2025, 33(9): 897-899. DOI: 10.3969/j.issn.1005-5185.2025.09.001.
[18]
WANG X R, XIE Z H, WANG X Q, et al. Preoperative prediction of IDH genotypes and prognosis in adult-type diffuse gliomas: intratumor heterogeneity habitat analysis using dynamic contrast-enhanced MRI and diffusion-weighted imaging[J/OL]. Cancer Imaging, 2025, 25(1): 11 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/39923105/. DOI: 10.1186/s40644-025-00829-5.
[19]
ZHANG X L, CHEN X Y, FU Y, et al. Study on heterogeneity of vascularity and cellularity via multiparametric MRI habitat imaging in breast cancer[J/OL]. BMC Med Imaging, 2025, 25(1): 159 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/40361010/. DOI: 10.1186/s12880-025-01698-x.
[20]
XU S J, YING Y S, HU Q L, et al. Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy[J/OL]. Cancer Imaging, 2025, 25(1): 108 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/40883826/. DOI: 10.1186/s40644-025-00929-2.
[21]
LI S L, DAI Y M, CHEN J Y, et al. MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges[J/OL]. Cancer Imaging, 2024, 24: 107 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/39148139/. DOI: 10.1186/s40644-024-00758-9.
[22]
GUO J T, FAN Z C, LI D, et al. Parameter-driven habitat imaging based on intravoxel incoherent motion MRI for preoperative prediction of muscle invasion in bladder cancer[J/OL]. Cancer Imaging, 2025, 25(1): 125 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41188984/. DOI: 10.1186/s40644-025-00948-z.
[23]
LI C R, TAN J, LI H Y, et al. The value of multiparametric MRI-based habitat imaging for differentiating uterine sarcomas from atypical leiomyomas: a multicentre study[J]. Abdom Radiol, 2025, 50(2): 995-1008. DOI: 10.1007/s00261-024-04539-7.
[24]
XIE P Y, HUANG Q T, ZHENG L T, et al. Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study[J]. Eur Radiol, 2025, 35(3): 1382-1393. DOI: 10.1007/s00330-024-11172-x.
[25]
YANG J, JI J N, NI X, et al. Explorations of predictors for parametrial invasion and how it affects treatment strategy for bulky cervical cancer[J/OL]. Front Oncol, 2025, 15: 1660495 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41098705/. DOI: 10.3389/fonc.2025.1660495.
[26]
YANG C S, LI M, YI X, et al. Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer[J/OL]. Front Oncol, 2025, 15: 1604749 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/40823078/. DOI: 10.3389/fonc.2025.1604749.
[27]
XIAO M L, FU L, WEI Y, et al. Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma[J]. Eur Radiol, 2024, 34(2): 852-862. DOI: 10.1007/s00330-023-10042-2.
[28]
YANG C S, LI M, YANG C F, et al. Radiomics based on habitat analysis in predicting parametrial invasion of early stage cervical cancer[J/OL]. Front Oncol, 2026, 16: 1694347 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41675288/. DOI: 10.3389/fonc.2026.1694347.
[29]
CUI Y M, LI Y, NA J, et al. Integration of radiomics, habitat imaging, and deep learning for MRI-based prediction of parametrial invasion in cervical cancer: A dual-center study[J/OL]. Magn Reson Imaging, 2026, 125: 110542 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41173217/. DOI: 10.1016/j.mri.2025.110542.
[30]
LIU L X, PAN L Y, ZOU C Y, et al. Reliability study of MRI in detecting lymph node metastasis of cervical cancer: a multi-center retrospective study result analysis[J]. Quant Imaging Med Surg, 2025, 15(10): 9559-9570. DOI: 10.21037/qims-24-2204.
[31]
WANG M, CAO Y, ZHANG W W, et al. Prediction model of lymph node metastasis in cervical cancer based on MRI habitat radiomics[J/OL]. Cancers, 2025, 18(1): 152 [2026-01-26]. https://pubmed.ncbi.nlm.nih.gov/41514660/. DOI: 10.3390/cancers18010152.
[32]
VENKAT V, SAKALECHA A K, DUDEKULA A, et al. Diagnostic utility of diffusion-weighted MRI and apparent diffusion coefficient values in differentiating metastatic from non-metastatic lymph nodes in cervical carcinoma[J/OL]. Cureus, 2025 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/40621327/. DOI: 10.7759/cureus.85371.
[33]
HOU W L, MA Y R, SUN S L, et al. Predictive factors for postoperative recurrence in early cervical cancer patients: a meta-analysis[J/OL]. Front Surg, 2025, 12: 1588558 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/40611924/. DOI: 10.3389/fsurg.2025.1588558.
[34]
BAI F, WU G Z, QU X Y, et al. Optimal adjuvant radiotherapy strategy for cervical cancer: a multi-center database cohort study[J/OL]. Int J Gynecol Cancer, 2026, 36(1): 102791 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/41308331/. DOI: 10.1016/j.ijgc.2025.102791.
[35]
PETOUSIS S, ALMPERIS A, MARGIOULA-SIARKOU C, et al. Adjuvant treatment for surgically-treated cervical cancer patients: A comprehensive review[J]. Cancers, 2025, 17(22): 3710 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/41301072/. DOI: 10.3390/cancers17223710.
[36]
ROMANOVA M, KLAT J. Prognostic significance of L1CAM in cervical cancer: A narrative review[J/OL]. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub, 2026 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/41496634/. DOI: 10.5507/bp.2025.035.
[37]
WANG Y, MA L N, LV Y H, et al. Noninvasive quantitative visualization of cervical cancer risk stratification using multiparametric MRI-based habitat imaging[J]. J Clin Radiol, 2026, 45(3): 498-506. DOI: 10.3969/j.issn.1001-9324.2026.03.018.
[38]
FENG Y, SUN Z J, LI Y Q, et al. A machine learning-based framework for prognostic prediction and tumor microenvironment characterization of locally advanced cervical cancer with concurrent chemoradiotherapy[J/OL]. npj Precis Oncol, 2026, 10: 31 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/41387521/. DOI: 10.1038/s41698-025-01234-8.
[39]
WANG S X, LIU X W, WANG Z G, et al. Mr-based radiomics analysis of intra-tumor heterogeneity for early treatment response prediction in locally advanced cervical cancer treated with concurrent chemoradiotherapy[J/OL]. Eur J Radiol, 2026, 194: 112547 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/41270704/. DOI: 10.1016/j.ejrad.2025.112547.
[40]
CORR B R, ERICKSON B K, BARBER E L, et al. Advances in the management of endometrial cancer[J/OL]. Bmj, 2025: e080978 [2026-02-21]. https://pubmed.ncbi.nlm.nih.gov/40044230/. DOI: 10.1136/bmj-2024-080978.
[41]
WANG T P, YU F, YANG B Y, et al. Value of magnetic resonance apparent diffusion coefficient values in predicting molecular typing of endometrial carcinoma: a preliminary study in a single tertiary center[J]. Fudan Univ J Med Sci, 2022, 49(6): 968-973. DOI: 10.3969/j.issn.1672-8467.2022.06.018.
[42]
NOUGARET S, HORTA M, SALA E, et al. Endometrial cancer MRI staging: updated guidelines of the European society of urogenital radiology[J]. Eur Radiol, 2019, 29(2): 792-805. DOI: 10.1007/s00330-018-5515-y.
[43]
MORETTI N R, KAMITANI H Z, DE SOUZA WAGNER P H, et al. Impact of TP53 somatic mutations on prognosis in endometrial cancer: a systematic review and meta-analysis[J]. Clin Transl Oncol, 2026, 28(4): 1324-1339. DOI: 10.1007/s12094-025-04076-9.
[44]
ZHOU J, YU X, CUI Y Y, et al. Prediction of molecular subtypes of endometrial cancer patients on the basis of intratumoral and peritumoral radiomic features from multiparametric MR images[J/OL]. Eur J Radiol, 2025, 187: 112110 [2026-01-16]. https://pubmed.ncbi.nlm.nih.gov/40262460/. DOI: 10.1016/j.ejrad.2025.112110.
[45]
JIN W T, ZHANG H, NING Y, et al. Development and validation of an explainable MRI-based habitat radiomics model for predicting p53-abnormal endometrial cancer: A multicentre feasibility study[J]. J Imaging Inform Med, 2026, 39(2): 1547-1557. DOI: 10.1007/s10278-025-01631-2.
[46]
JIN W T, WANG T P, CHEN X J, et al. MRI-based habitat radiomics analysis for identifying molecular subtypes of endometrial cancer: a feasible study from two institutions[J]. Fudan Univ J Med Sci, 2024, 51(6): 890-899. DOI: 10.3969/j.issn.1672-8467.2024.06.003.
[47]
CONCIN N, MATIAS-GUIU X, CIBULA D, et al. ESGO-ESTRO-ESP guidelines for the management of patients with endometrial carcinoma: update 2025[J/OL]. Lancet Oncol, 2025, 26(8): e423-e435 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40744042/. DOI: 10.1016/S1470-2045(25)00167-6.
[48]
YANG Y, YE Z J, ZHAO Y F, et al. Comparing the efficacy of different methods in assessing cervical stromal invasion in endometrial carcinoma: a retrospective study of 2, 020 patients[J/OL]. Front Oncol, 2025, 15: 1548436 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40008004/. DOI: 10.3389/fonc.2025.1548436.
[49]
ZHENG J J, LIN X Y, LI M. Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: a systematic review and meta-analysis[J/OL]. Front Oncol, 2025, 15: 1673060 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/41561765/. DOI: 10.3389/fonc.2025.1673060.
[50]
WANG X H, DENG C, KONG R Z, et al. Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma[J]. Acad Radiol, 2025, 32(3): 1476-1487. DOI: 10.1016/j.acra.2024.09.039.
[51]
LIU D, HUANG J Y, ZHANG Y F, et al. Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma[J/OL]. BMC Med Imaging, 2024, 24(1): 252 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/39304802/. DOI: 10.1186/s12880-024-01430-1.
[52]
WANG W, LANG J L, XUE K M, et al. Evaluate lymphovascular space invasion in endometrial cancer using diffusion-weighted imaging-based habitat imaging[J/OL]. Sci Rep, 2026, 16: 1626 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/41381749/. DOI: 10.1038/s41598-025-31101-2.
[53]
HONG M K, DING D C. Early diagnosis of ovarian cancer: A comprehensive review of the advances, challenges, and future directions[J/OL]. Diagnostics (Basel), 2025, 15(4): 406 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40002556/. DOI: 10.3390/diagnostics15040406.
[54]
EFTEKHARI MOGHADAM A R, JALILIAN M, ABSALAN F, et al. Diffusion-weighted imaging-based differentiating between benign and malignant ovarian lesions[J/OL]. Adv Biomed Res, 2025, 14(1): [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40519573/. DOI: 10.4103/abr.abr_138_24.
[55]
PUNZÓN-JIMÉNEZ P, LAGO V, DOMINGO S, et al. Molecular management of high-grade serous ovarian carcinoma[J/OL]. Int J Mol Sci, 2022, 23(22): 13777 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/36430255/. DOI: 10.3390/ijms232213777.
[56]
RAI L, RAVAGGI A, BIGNOTTI E, et al. Oxford classic-defined EMT risk stratification of high-grade serous ovarian cancer for guiding treatment decisions[J]. Clin Cancer Res, 2026, 32(1): 188-202. DOI: 10.1158/1078-0432.ccr-24-4250.
[57]
HOTTON J, GAILLARD T, PAOLETTI X, et al. Survival impact of residual disease and timing of surgery in the management of advanced-stage low grade serous ovarian cancer: A systematic review and meta-analysis[J]. Gynecol Oncol, 2025, 203: 16-25. DOI: 10.1016/j.ygyno.2025.10.006.
[58]
PARIZA G, MAVRODIN C, POTORAC A, et al. A narrative review of clinical and molecular criteria for the selection of poor candidates for optimal cytoreduction in epithelial ovarian cancer[J/OL]. Life (Basel), 2025, 15(8): 1318 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40868967/. DOI: 10.3390/life15081318.
[59]
BI Q, MIAO K, LIU Y, et al. mpMRI-based habitat analysis for predicting prognoses in patients with high-grade serous ovarian cancer: a multicenter study[J]. Abdom Radiol, 2025, 50(12): 6039-6051. DOI: 10.1007/s00261-025-05004-9.
[60]
KILIM O, OLAR A, BIRICZ A, et al. Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction[J/OL]. medRxiv, 2024: 2024.06.01.24308293 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/38883738/. DOI: 10.1101/2024.06.01.24308293.
[61]
DION L, CARTON I, JAILLARD S, et al. The landscape and therapeutic implications of molecular profiles in epithelial ovarian cancer[J/OL]. J Clin Med, 2020, 9(7): 2239 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/32679669/. DOI: 10.3390/jcm9072239.
[62]
HE M G, SINGH R, WANG M D, et al. CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study[J/OL]. Cancer Imaging, 2025, 25(1): 85 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40611334/. DOI: 10.1186/s40644-025-00906-9.
[63]
BI Q, AI C H, MENG Q Y, et al. A multi-modal model integrating MRI habitat and clinicopathology to predict platinum sensitivity in patients with high-grade serous ovarian cancer: a diagnostic study[J]. Int J Surg, 2025, 111(7): 4222-4233. DOI: 10.1097/js9.0000000000002524.
[64]
BI Q, MIAO K, XU N, et al. Habitat radiomics based on MRI for predicting platinum resistance in patients with high-grade serous ovarian carcinoma: A multicenter study[J]. Acad Radiol, 2024, 31(6): 2367-2380. DOI: 10.1016/j.acra.2023.11.038.
[65]
KANDEMIR H, SÖZEN H, KARTAL M G, et al. An assessment of the effectiveness of preoperative İmaging modalities (MRI, CT, and 18F-FDG PET/CT) in determining the extent of disease spread in epithelial ovarian-tubal-peritoneal cancer (EOC)[J/OL]. Medicina (Kaunas), 2025, 61(2): 199 [2026-03-10]. https://pubmed.ncbi.nlm.nih.gov/40005316/. DOI: 10.3390/medicina61020199.

PREV Research progress of magnetic resonance proton density fat fraction in extra-hepatic fat quantification
NEXT Advances in deep learning-based body composition analysis with magnetic resonance imaging
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn