任 雪,布买热木·尔肯,古丽扎尔·阿不力米提,位文慧

肿瘤及中枢神经系统性病变内部存在显著的空间异质性，传统影像学定性评估方式难以实现病灶微观特征的精准量化。影像组学与生境分析技术能够基于像素、体素水平挖掘病灶微观空间分布差异，无创表征组织微环境特征，现已成为影像学精准研究的新兴方向。本文综述了该技术在多种疾病诊疗中的应用价值，并探讨当前生境分析存在的技术瓶颈与临床转化难题，展望影像组学生境分析未来的优化方向与临床应用前景，以期为该技术的进一步完善和临床普及应用提供理论依据与研究参考。

影像组学；生境分析；空间异质性；肿瘤；中枢神经系统病变

[1] 戴婧洳,马琳莹,陈峰,等.生境成像在消化系统肿瘤中的研究进展[J].国际医学放射学杂志,2025,48(03):337-342. [2] Wu J,Xia Y,Wang X,et al.Radiomics++:Review of Habitat Imaging Analysis for Decoding Tumor Heterogeneity[J].Annual Review of Biomedical Engineering,2026:10-1146. [3] Li J,Qiu Z,Zhang C,et al.ITHscore:comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features[J].European Radiology,2023,33(2):893-903. [4] Kazerouni A S,Hormuth D A N,Davis T,et al.Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+Breast Cancer[J].Cancers,2022,14(7):1837. [5] Wu L X,Ding N,Ji Y D,et al.Habitat Analysis in Tumor Imaging:Avancing Precision Medicine Through Radiomic Subregion Segmentation[J].Cancer Management and Research,2025,17:731-741. [6] Zhao H,Su Y,Wang Y,et al.Using tumor habitat-derived radiomic analysis during pretreatment(18)F-FDG PET for predicting KRAS/ NRAS/BRAF mutations in colorectal cancer[J].Cancer Imaging,2024,24(1):26. [7] Wang S,Liu X,Wu Y,et al.Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer:a multi-center study[J].Frontiers in Oncology,2023,13:1252074. [8] Prior O,Macarro C,Navarro V,et al.Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer[J].Radiology:Artificial Intelligence,2024,6(2):e230118. [9] Fang M,Kan Y,Dong D,et al.Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer[J].Frontiers in Oncology,2020,10:563. [10] Sachpekidis C,Enqvist O,Ulén J,et al.Artificial intelligence-based,volumetric assessment of the bone marrow metabolic activity in[(18)F]FDG PET/CT predicts survival in multiple myeloma[J].European Journal of Nuclear Medicine and Molecular Imaging,2024, 51(8):2293-2307. [11] Beig N,Bera K,Prasanna P,et al.Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma[J].Clinical Cancer Research,2020,26(8):1866-1876. [12] Verma R,Correa R,Hill V B,et al.Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression- free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination:A Feasibility Study[J]. Radiology:Artificial Intelligence,2020,2(6):e190168. [13] Komori T.The 2021 WHO Classification of Tumors,5th edition,Central Nervous System Tumors:A Short Review[J].Brain and Nerve, 2022,74(6):803-809. [14] NiemeläS,Oksi J,Jero J,et al.Glioma grade and post-neurosurgical meningitis risk[J].Acta Neurochirurgica,2024,166(1):300. [15] Zhu Y,Wang J,Xue C,et al.Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI:A Multicenter Study[J].Academic Radiology,2025,32(2):963-975. [16] Wang Y,Lin L,Hu Z,et al.Global Habitat Analysis with Multi-graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients[J].IEEE Journal of Biomedical and Health Informatics,2025,PP:10-1109. [17] Zhu F,Chen W,Chen H,et al.A novel multimodal framework combining habitat radiomics,deep learning,and conventional radiomics for predicting MGMT gene promoter methylation in Glioma:Superior performance of integrated models[J].European Journal of Radiology,2025,192:112406. [18] Rizzo S,Botta F,Raimondi S,et al.Radiomics:the facts and the challenges of image analysis[J].European Radiology Experimental, 2018,2(1):36. [19] Tabassum M,Suman A A,Suero Molina E,et al.Radiomics and Machine Learning in Brain Tumors and Their Habitat:A Systematic Review[J].Cancers,2023,15(15):3845. [20] Ikotun A M,Ezugwu A E.Boosting k-means clustering with symbiotic organisms search for automatic clustering problems[J].PLoS One,2022,17(8):e272861. [21] Xie P,Huang Q,Zheng L,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].European Radiology,2025,35(3):1382-1393. [22] Huang H,Chen H,Zheng D,et al.Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation[J].Cancer Imaging,2024,24(1):44. [23] 陈斌,谢文波,付勋,等.基于改进局部密度的可扩展层次聚类算法[J].南京大学学报(自然科学),2024,60(03):370-382.

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