Share:
Share this content in WeChat
X
• Review •
Research progress on spatial correlation between multiparametric magnetic resonance imaging and pathological features of the tumor microenvironment
YU Chang  CUI Yong 

Cite this article as: YU C, CUI Y. Research progress on spatial correlation between multiparametric magnetic resonance imaging and pathological features of the tumor microenvironment[J]. Chin J Magn Reson Imaging, 2026, 17(10): 225-234. DOI:10.12015/issn.1674-8034.2026.10.028.


[Abstract] Tumor microenvironment (TME) spatial heterogeneity can greatly affect how a tumor invades, spreads, resists treatment, and influences patient outcomes. Traditional biopsies are invasive and sampling bias often happens; this makes it difficult to check the TME fully and dynamically in the clinic. Multiparametric magnetic resonance imaging (mpMRI) brings together several sequences, such as diffusion, perfusion, oxygenation, elasticity, and metabolism; by doing so, it offers a non-invasive, quantitative, and overall look at TME spatial heterogeneity, so it becomes a key tool for studies that connect images with pathology in space. In this review, we cover recent progress on the links between mpMRI and core pathological features of the TME; these features include new blood vessel formation, low oxygen, stromal fibrosis, the immune microenvironment, cell growth, and tissue death. We explain the biological basis of mpMRI sequences and methods for matching images with tissue slices, summarize the number-based relationships between multi-parameter imaging measures and pathology indicators along with how they are used in patient care, and analyze main problems like not enough standardization, limited accuracy in matching, a lack of large-scale checking, and troubles in moving findings to real clinical practice; we also discuss forward-looking directions such as combining multiple data types, registration driven by AI, and integrated spatial omics, so as to offer a reference for making cancer diagnosis and treatment more precise with mpMRI.
[Keywords] multiparametric magnetic resonance imaging;tumor microenvironment;spatial heterogeneity;imaging-pathology spatial correlation;precision medicine

YU Chang1   CUI Yong1, 2*  

1 Department of Radiology, Affiliated Cancer Hospital of Inner Mongolia Medical University/Peking University Cancer Hospital (Inner Mongolia Campus), Inner Mongolia Cancer Center, Hohhot 010020, China

2 Department of Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing 100142, China

Corresponding author: CUI Y, E-mail: yong.cui@outlook.com

Conflicts of interest   None.

Received  2026-06-10
Accepted  2026-08-20
DOI: 10.12015/issn.1674-8034.2026.10.028
Cite this article as: YU C, CUI Y. Research progress on spatial correlation between multiparametric magnetic resonance imaging and pathological features of the tumor microenvironment[J]. Chin J Magn Reson Imaging, 2026, 17(10): 225-234. DOI:10.12015/issn.1674-8034.2026.10.028.

[1]
WANG C X, WANG J, MIAO L L, et al. Cancer metabolism: from the Warburg effect to precision therapy[J/OL]. Front Immunol, 2026, 17: 1793553 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/42199418/. DOI: 10.3389/fimmu.2026.1793553.
[2]
CHU X Y, TIAN Y, LV C. Decoding the spatiotemporal heterogeneity of tumor-associated macrophages[J/OL]. Mol Cancer, 2024, 23(1): 150 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/39068459/. DOI: 10.1186/s12943-024-02064-1.
[3]
REN Q P, ZHANG X, WU X W, et al. Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study[J/OL]. BMC Med, 2025, 23(1): 594 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/41162981/. DOI: 10.1186/s12916-025-04424-0.
[4]
ZABIHOLLAHY F, WU H H, SISK A E, et al. Deep learning-based anatomy-aware morph model for registration of prostate whole-mount histopathology to MRI[J/OL]. Radiol Imaging Cancer, 2025, 7(3): e240336 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/40445100/. DOI: 10.1148/rycan.240336.
[5]
HOFFMANN E, MASTHOFF M, KUNZ W G, et al. Multiparametric MRI for characterization of the tumour microenvironment[J]. Nat Rev Clin Oncol, 2024, 21(6): 428-448. DOI: 10.1038/s41571-024-00891-1.
[6]
MA W H, YANG L, ZHANG Y, et al. Heterogeneity assessment of breast cancer tumor microenvironment: multiparametric quantitative analysis with DCE-MRI and discovery of radiomics biomarkers[J/OL]. Breast Cancer Targets Ther, 2025, 17: 573-581 [2026-06-09]. https://www.dovepress.com/heterogeneity-assessment-of-breast-cancer-tumor-microenvironment-multi-peer-reviewed-fulltext-article-BCTT. DOI: 10.2147/BCTT.S530834.
[7]
LIU M M, BANE O, MU X, et al. Immuno-oncologic profiling of renal masses using multiparametric MRI: a pilot study[J/OL]. J Immunother Cancer, 2025, 13(12): e012833 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/41360428/. DOI: 10.1136/jitc-2025-012833.
[8]
LAI Q P, TENG X Z, ZHANG J, et al. Tumor imaging heterogeneity index-inspired insights into the unveiling tumor microenvironment of breast cancer[J/OL]. Int J Mol Sci, 2025, 26(23): 11624 [2026-06-09]. https://www.mdpi.com/1422-0067/26/23/11624. DOI: 10.3390/ijms262311624.
[9]
WU J J, XIA Y W, WANG X C, et al. Radiomics++: review of habitat imaging analysis for decoding tumor heterogeneity[J]. Annu Rev Biomed Eng, 2026, 28(1): 219-248. DOI: 10.1146/annurev-bioeng-031825-040442.
[10]
WATANABE H, TAKEUCHI M, HIGAKI A, et al. Histopathological basis of peritumoral enhancement in muscle-invasive bladder cancer[J/OL]. Magn Reson Med Sci, 2026, 25(3) [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/41850821/. DOI: 10.2463/mrms.mp.2025-0211.
[11]
ZHAO R, SHEN W, ZHAO W, et al. Integrating radiomics, pathomics, and biopsy-adapted immunoscore for predicting distant metastasis in locally advanced rectal cancer[J/OL]. ESMO Open, 2025, 10(3): 104102 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/39951928/. DOI: 10.1016/j.esmoop.2024.104102.
[12]
ALIC L, HAECK J C, BOL K, et al. Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: image registration approach[J/OL]. PLoS One, 2011, 6(8): e22835 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/21897840/. DOI: 10.1371/journal.pone.0022835.
[13]
ALYAMI W, KYME A, BOURNE R. Histological validation of MRI: a review of challenges in registration of imaging and whole-mount histopathology[J]. J Magn Reson Imaging, 2022, 55(1): 11-22. DOI: 10.1002/jmri.27409.
[14]
AGANJ I, FEIWEIER T, KIRSCH J E, et al. Spatial image gradient estimation from the diffusion MRI profile[J]. Magn Reson Med, 2026, 95(5): 2980-2991. DOI: 10.1002/mrm.70258.
[15]
ZHENG K Y, ZHANG Y W, SHU H, et al. Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI[J/OL]. Nat Commun, 2026, 17: 2162 [2026-06-09]. https://www.nature.com/articles/s41467-026-69578-8. DOI: 10.1038/s41467-026-69578-8.
[16]
YANG Y P, LIU J, SHU J. Comparative analysis of apparent diffusion coefficient (ADC) metrics for the differential diagnosis of breast mass lesions[J/OL]. BMC Med Imag, 2025, 25(1): 117 [2026-06-09]. https://link.springer.com/article/10.1186/s12880-025-01654-9. DOI: 10.1186/s12880-025-01654-9.
[17]
STABINSKA J, THIEL T A, ZÖLLNER H J, et al. Investigation of diffusion time dependence of apparent diffusion coefficient and intravoxel incoherent motion parameters in the human kidney[J]. Magn Reson Med, 2025, 93(5): 2020-2028. DOI: 10.1002/mrm.30396.
[18]
YAN Z X, WANG M, ZHAO J, et al. Time-dependent diffusion MRI of soft tissue tumors: correlations with Ki-67 proliferation status[J/OL]. Quant Imaging Med Surg, 2026, 16(3): 209 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/41816047/. DOI: 10.21037/qims-2025-1815.
[19]
SHEN J, ZHANG L T, LI S K, et al. Multi-sequence MRI based radiomics nomogram for prediction expression of programmed death ligand 1 in thymic epithelial tumor[J/OL]. Front Immunol, 2025, 16: 1555530 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/40292290/. DOI: 10.3389/fimmu.2025.1555530.
[20]
BEN DOR N, SIGHINOLFI G, ROSETTI V, et al. Diffusion tensor imaging biomarkers to predict neurological outcomes in brain surgery: a systematic review[J/OL]. Life, 2026, 16(1): 115 [2026-06-09]. https://www.mdpi.com/2075-1729/16/1/115. DOI: 10.3390/life16010115.
[21]
TANG L, WU T H, HU R L, et al. Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI)[J]. Quant Imaging Med Surg, 2024, 14(4): 2774-2787. DOI: 10.21037/qims-23-1471.
[22]
SONG D D, CHEN B Y, LI Z X, et al. Role of dynamic contrast-enhanced and dynamic susceptibility contrast imaging in evaluating the biological features of glioma[J]. Quant Imaging Med Surg, 2025, 15(8): 7030-7045. DOI: 10.21037/qims-2024-2794.
[23]
ZHOU C Y, GUO Y H, MENG Z N, et al. Hemodynamic inflow-outflow profiling as an integrated DCE-MRI biomarker for diagnosis and risk stratification in breast cancer: a multicenter retrospective study[J/OL]. Breast Cancer Targets Ther, 2026, 18: 563533 [2026-06-09]. https://www.dovepress.com/hemodynamic-inflow-outflow-profiling-as-an-integrated-dce-mri-biomarke-peer-reviewed-fulltext-article-BCTT. DOI: 10.2147/BCTT.S563533.
[24]
PRAH M A, SCHMAINDA K M. Practical guidance to identify and troubleshoot suboptimal DSC-MRI results[J/OL]. Front Radiol, 2024, 4: 1307586 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/38445104/. DOI: 10.3389/fradi.2024.1307586.
[25]
HUANG W H, LI N, ZHU S, et al. Imaging hypoxia for head and neck cancer: current status, challenges, and prospects[J]. Theranostics, 2025, 15(16): 8012-8030. DOI: 10.7150/thno.112781.
[26]
YIN L, ZHANG J, PANG Y X, et al. Blood oxygenation level-dependent MRI for predicting lymphovascular space invasion in cervical squamous cell carcinoma treated with neoadjuvant chemotherapy: a prospective study[J]. Acad Radiol, 2026, 33(7): 2934-2945. DOI: 10.1016/j.acra.2026.03.021.
[27]
LE T T, IM G H, LEE C H, et al. Mapping cerebral perfusion in mice under various anesthesia levels using highly sensitive BOLD MRI with transient hypoxia[J/OL]. Sci Adv, 2024, 10(9): eadm7605 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/38416820/. DOI: 10.1126/sciadv.adm7605.
[28]
SONG X L, CHEN S J, DENG P, et al. Principles and clinical application advances of virtual magnetic resonance elastography based on DWI[J]. Chin J Magn Reson Imaging, 2026, 17(1): 208-215, 227. DOI: 10.12015/issn.1674-8034.2026.01.032.
[29]
ASEEL A, MCCARTHY P, MOHAMMED A. Brain magnetic resonance spectroscopy to differentiate recurrent neoplasm from radiation necrosis: a systematic review and meta-analysis[J]. J Neuroimaging, 2023, 33(2): 189-201. DOI: 10.1111/jon.13080.
[30]
XU N, LU Y, YANG X R, et al. Research on application of multiparametric MRI to predict FNCLCC grading and ki67 expression in soft tissue sarcoma biopsy pathology: Based on a CT-MRI fusion image registration method[J/OL]. Front Oncol, 2025, 15: 1606942 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/40860816/. DOI: 10.3389/fonc.2025.1606942.
[31]
LEJOLY M, VAN DEN BERGHE T, CREYTENS D, et al. Exact radiological-pathological correlation of chondrosarcomas using a patient-specific 3D mold[J]. Skeletal Radiol, 2025, 54(10): 2179-2190. DOI: 10.1007/s00256-025-04942-1.
[32]
HUANG M Q, SONG C Y, ZHOU X Q, et al. Tissue-matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma[J]. Int J Cancer, 2025, 156(8): 1634-1643. DOI: 10.1002/ijc.35281.
[33]
JUNG M, BOGNER B, DIALLO T D, et al. Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm[J]. Theranostics, 2023, 13(5): 1594-1606. DOI: 10.7150/thno.81938.
[34]
HU L S, D'ANGELO F, WEISKITTEL T M, et al. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures[J/OL]. Nat Commun, 2023, 14(1): 6066 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/37770427/. DOI: 10.1038/s41467-023-41559-1.
[35]
HECTORS S J, WAGNER M, BANE O, et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging[J/OL]. Sci Rep, 2017, 7: 2452 [2026-06-09]. https://www.nature.com/articles/s41598-017-02706-z. DOI: 10.1038/s41598-017-02706-z.
[36]
ZAMBOGLOU C, DRENDEL V, JILG C A, et al. Comparison of (68)Ga-HBED-CC PSMA-PET/CT and multiparametric MRI for gross tumour volume detection in patients with primary prostate cancer based on slice by slice comparison with histopathology[J]. Theranostics, 2017, 7(1): 228-237. DOI: 10.7150/thno.16638.
[37]
MIRANDA J, HEISELMAN J S, FIRAT C, et al. Deformable mapping of rectal cancer whole-mount histology with restaging MRI at voxel scale: a feasibility study[J/OL]. Radiol Imaging Cancer, 2024, 6(6): e240073 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/39452890/. DOI: 10.1148/rycan.240073.
[38]
JARDIM-PERASSI B V, HUANG S N, DOMINGUEZ-VIQUEIRA W, et al. Multiparametric MRI and coregistered histology identify tumor habitats in breast cancer mouse models[J]. Cancer Res, 2019, 79(15): 3952-3964. DOI: 10.1158/0008-5472.CAN-19-0213.
[39]
KANNAN P, KRETZSCHMAR W W, WINTER H, et al. Functional parameters derived from magnetic resonance imaging reflect vascular morphology in preclinical tumors and in human liver metastases[J]. Clin Cancer Res, 2018, 24(19): 4694-4704. DOI: 10.1158/1078-0432.CCR-18-0033.
[40]
SUN M N, WANG H, YANG Y Y, et al. Tumor habitats based on multiparametric MRI distinguish atypical glioblastoma from primary central nervous system lymphoma: imaging-pathologic correlation[J]. J Magn Reson Imag, 2026, 63(1): 142-154. DOI: 10.1002/jmri.70080.
[41]
SUN Y, WILLIAMS S, BYRNE D, et al. Association analysis between quantitative MRI features and hypoxia-related genetic profiles in prostate cancer: a pilot study[J/OL]. Br J Radiol, 2019, 92(1104): 20190373 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/31356111/. DOI: 10.1259/bjr.20190373.
[42]
RASMUSSEN J H, OLIN A, LELKAITIS G, et al. Does multiparametric imaging with (18)F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma [J]. Br J Cancer, 2020, 123(1): 46-53. DOI: 10.1038/s41416-020-0876-9.
[43]
HAN S H, ACKERSTAFF E, STOYANOVA R, et al. Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results[J]. NMR Biomed, 2013, 26(5): 519-532. DOI: 10.1002/nbm.2888.
[44]
SAVIC L J, SCHOBERT I T, PETERS D, et al. Molecular imaging of extracellular tumor pH to reveal effects of locoregional therapy on liver cancer microenvironment[J]. Clin Cancer Res, 2020, 26(2): 428-438. DOI: 10.1158/1078-0432.CCR-19-1702.
[45]
MCGRATH D M, LEE J, FOLTZ W D, et al. MR elastography to measure the effects of cancer and pathology fixation on prostate biomechanics, and comparison with T (1), T (2) and ADC[J]. Phys Med Biol, 2017, 62(3): 1126-1148. DOI: 10.1088/1361-6560/aa52f4.
[46]
CHEN J, WU Z R, ZHANG Z, et al. Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma[J]. Eur Radiol, 2024, 34(11): 6980-6991. DOI: 10.1007/s00330-024-10743-2.
[47]
GALATI F, RIZZO V, MOFFA G, et al. Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes [J/OL]. Eur Radiol Exp, 2022, 6(1): 39 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/35934721/. DOI: 10.1186/s41747-022-00289-7.
[48]
PESSOA E C, COUTO H L, KAMYIA CARVALHO PESSOA C P, et al. Breast MRI biomarkers of tumor biology: integrating imaging with pathology to guide clinical care[J/OL]. Eur J Radiol, 2026, 200: 112863 [2026-06-09]. https://www.ejradiology.com/article/S0720-048X(26)00211-1/fulltext. DOI: 10.1016/j.ejrad.2026.112863.
[49]
YUAN L, LI Y J, HUAN Y, et al. Application advances of tumor habitat imaging[J]. Int J Med Radiol, 2023, 46(6): 721-724. DOI: 10.19300/j.2023.Z20836.
[50]
WANG Y Q, ZHANG Y, LIU Z Y, et al. Multiparametric MRI-based habitat analysis integrating deep learning and radiomics for predicting preoperative Ki-67 expression level in breast cancer[J/OL]. BMC Med Imaging, 2026, 26(1): 80 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/41545974/. DOI: 10.1186/s12880-026-02151-3.
[51]
YOSHIMITSU K, KIYOSHIMA K, IRIE H, et al. Usefulness of apparent diffusion coefficient map in diagnosing prostate carcinoma: correlation with stepwise histopathology[J]. J Magn Reson Imaging, 2008, 27(1): 132-139. DOI: 10.1002/jmri.21181.
[52]
GAW N, HAWKINS-DAARUD A, HU L S, et al. Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI[J/OL]. Sci Rep, 2019, 9: 10063 [2026-06-09]. https://www.nature.com/articles/s41598-019-46296-4. DOI: 10.1038/s41598-019-46296-4.
[53]
XU X, MA M J, YE K L, et al. Magnetic resonance imaging-based approaches for detecting the efficacy of combining therapy following VEGFR-2 and PD-1 blockade in a colon cancer model[J/OL]. J Transl Med, 2024, 22(1): 198 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/38395884/. DOI: 10.1186/s12967-024-04975-5.
[54]
WU Z J, DONG C, BIAN T T, et al. Comparative study of diffusion-weighted imaging and pathology of VX2 malignant bone tumor in rabbits[J]. Radiol Pract, 2021, 36(1): 37-41. DOI: 10.13609/j.cnki.1000-0313.2021.01.008.
[55]
GILAD A A, ISRAELY T, DAFNI H, et al. Functional and molecular mapping of uncoupling between vascular permeability and loss of vascular maturation in ovarian carcinoma xenografts: The role of stroma cells in tumor angiogenesis[J]. Int J Cancer, 2005, 117(2): 202-211. DOI: 10.1002/ijc.21179.
[56]
WANG Y, LI J L, MA H, et al. Evaluation of tumor necrosis rate of osteosarcoma after neoadjuvant chemotherapy using quantitative parameters of dynamic contrast-enhanced magnetic resonance Ima-ging based on MRI-pathological correlation[J]. Chin J Clin Oncol, 2022, 49(19): 988-993. DOI: 10.12354/j.issn.1000-8179.2022.20220243.
[57]
BERRY L R, BARCK K H, GO M A, et al. Quantification of viable tumor microvascular characteristics by multispectral analysis[J]. Magn Reson Med, 2008, 60(1): 64-72. DOI: 10.1002/mrm.21470.
[58]
BARAJAS R F, PHILLIPS J J, PARVATANENI R, et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging[J]. Neuro Oncol, 2012, 14(7): 942-954. DOI: 10.1093/neuonc/nos128.
[59]
BEKAERT L, VALABLE S, LECHAPT-ZALCMAN E, et al. 18F]-FMISO PET study of hypoxia in gliomas before surgery: correlation with molecular markers of hypoxia and angiogenesis[J]. Eur J Nucl Med Mol Imaging, 2017, 44(8): 1383-1392. DOI: 10.1007/s00259-017-3677-5.
[60]
BAI L, MA M J, YUAN W X, et al. MRI monitored Mn-THPPmPEG12-guided phototherapy elicits abscopal immunity and synergizes with PD-1 blockade in triple-negative breast cancer in mice model[J/OL]. Int J Nanomedicine, 2026, 21: 608363 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/42438592/. DOI: 10.2147/IJN.S608363.
[61]
LIANG J X, AMPUERO J, CASTELL J, et al. Clinical application of Magnetic resonance elastography in hepatocellular carcinoma: from diagnosis to prognosis[J/OL]. Ann Hepatol, 2023, 28(2): 100889 [2026-06-09]. https://www.sciencedirect.com/science/article/pii/S1665268122002319via%3Dihub. DOI: 10.1016/j.aohep.2022.100889.
[62]
CHENG W Q, QI X, YANG H K, et al. Research progresses in multi-parameter MRI habitat imaging of breast cancer[J]. Chin J Med Imag Technol, 2024, 40(11): 1798-1801. DOI: 10.13929/j.issn.1003-3289.2024.11.034.
[63]
CHANG T G, PARK S, SCHÄFFER A A, et al. Hallmarks of artificial intelligence contributions to precision oncology[J]. Nat Cancer, 2025, 6(3): 417-431. DOI: 10.1038/s43018-025-00917-2.
[64]
ZHANG T F, ZHAO L, CUI T T, et al. Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review[J/OL]. J Transl Med, 2025, 23(1): 681 [2026-06-09]. https://link.springer.com/article/10.1186/s12967-025-06641-w. DOI: 10.1186/s12967-025-06641-w.
[65]
ENNINFUL A, ZHANG Z J, KLYMYSHYN D, et al. Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus[J/OL]. Nat Meth, 2026: 1-13 [2026-06-09]. https://www.nature.com/articles/s41592-025-02948-0. DOI: 10.1038/s41592-025-02948-0.
[66]
SHEN Y Q, SHEN Y, WANG M L, et al. A spatial imaging-transcriptomics paradigm for deciphering the molecular basis of microscopic MRI in the normal brain and Alzheimer's disease[J/OL]. Cell Rep, 2025, 44(8): 116073 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/40742809/. DOI: 10.1016/j.celrep.2025.116073.
[67]
PRELAJ A, MISKOVIC V, ZANITTI M, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review[J]. Ann Oncol, 2024, 35(1): 29-65. DOI: 10.1016/j.annonc.2023.10.125.
[68]
SHAO W, VESAL S, SOERENSEN S J C, et al. RAPHIA: a deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate[J/OL]. Comput Biol Med, 2024, 173: 108318 [2026-06-09]. https://pubmed.ncbi.nlm.nih.gov/38522253/. DOI: 10.1016/j.compbiomed.2024.108318.
[69]
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.
[70]
LI L, SHIRADKAR R, GOTTLIEB N, et al. Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology[J]. Med Phys, 2024, 51(4): 2549-2562. DOI: 10.1002/mp.16753.

PREV Research progress of morphometry and quantitative MRI techniques in knee post-traumatic osteoarthritis
NEXT Research progress on spatial correlation between multiparametric magnetic resonance imaging and pathological features of the tumor microenvironment
  



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