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子宫颈癌中MRI影像组学分析与肿瘤微环境的关联

Associations between MRI radiomics analysis and tumor-micro milieu in uterine cervical cancer.

作者信息

Meyer Hans-Jonas, Leonhardi Jakob, Höhn Anne-Kathrin, Kabbani Noura, Zimmermann Silke, Borggrefe Jan, Surov Alexey

机构信息

Department of Diagnostic and Interventional Radiology, University Hospital Leipzig, Liebigstraße 20, 04103, Leipzig, Germany.

Institute of Pathology, University Hospital of Leipzig, Leipzig, Germany.

出版信息

J Cancer Res Clin Oncol. 2025 Jul 21;151(7):219. doi: 10.1007/s00432-025-06253-3.


DOI:10.1007/s00432-025-06253-3
PMID:40691330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12279667/
Abstract

PURPOSE: The complex interactions of the tumor micromilieu could be reflected by magnetic resonance imaging (MRI) when analyzed with the radiomics approach. For several tumor entities, it has been shown that radiomics derived from MRI can reflect important characteristics of the tumors. The present study investigated the association radiomics derived from MRI images and histopathological features in uterine cervical cancer. METHODS: The MRI before any treatment was used to extract the radiomics features of T1- and T2-weighted images. The biopsy specimens were stained for Ki 67, e-cadherin, vimentin, programmed-death ligand 1, and tumor-infiltrating lymphocytes (TIL, all CD45 positive cells). Tumor-stroma ratio (TSR) was calculated on routine H&E specimen. Spearman's correlation analysis and discrimination analyses were performed as statistical analyses. RESULTS: The patient sample was comprised of 89 female patients with a mean age of 49.3 years ± 14.6 (range 27-77 years) with squamous cell cervical carcinoma. "Kurtosis" derived from T1-weighted images after contrast media application correlated with the Ki-67 index (r = 0.28, p = 0.02). "WavEnHL_s-4" derived from T2-weighted images and "S(1.0)Contrast" derived from T1-weighted images after contrast media application showed correlations with TSR (r = - 0.24, p = 0.04, each). Several associations were identified between the radiomics features with immune scores defined by programmed-death ligand 1, the highest correlation showed Teta1 derived from T2-weighted images with the combined positive score (r = - 0.38, p < 0.01). There were several associations with vimentin expression, the highest showed "Variance" derived from T1-weighted images after contrast media application (r = 0.46, p < 0.01). CONCLUSIONS: Radiomics features derived from MRI can reflect tumor characteristics of UCC. Especially immune-related features were reflected by the MRI texture features. Proliferation potential, composition of the extracellular matrix and tumor-stroma ratio were also significantly associated with radiomics features. These presented results need to be evaluated in an independent cohort to test their stability.

摘要

目的:当采用放射组学方法进行分析时,肿瘤微环境的复杂相互作用可通过磁共振成像(MRI)反映出来。对于几种肿瘤实体,已表明源自MRI的放射组学能够反映肿瘤的重要特征。本研究调查了源自MRI图像的放射组学与子宫颈癌组织病理学特征之间的关联。 方法:使用任何治疗前的MRI提取T1加权和T2加权图像的放射组学特征。活检标本进行Ki 67、E-钙黏蛋白、波形蛋白、程序性死亡配体1和肿瘤浸润淋巴细胞(TIL,所有CD45阳性细胞)染色。在常规苏木精-伊红(H&E)标本上计算肿瘤-基质比(TSR)。进行Spearman相关性分析和判别分析作为统计分析。 结果:患者样本包括89例平均年龄为49.3岁±14.6岁(范围27 - 77岁)的鳞状细胞宫颈癌女性患者。造影剂应用后源自T1加权图像的“峰度”与Ki-67指数相关(r = 0.28,p = 0.02)。源自T2加权图像的“WavEnHL_s-4”和造影剂应用后源自T1加权图像的“S(1.0)对比度”与TSR相关(r均为 - 0.24,p均为0.04)。在放射组学特征与由程序性死亡配体1定义的免疫评分之间发现了几种关联,最高相关性显示源自T2加权图像的Teta1与联合阳性评分相关(r = - 0.38,p < 0.01)。与波形蛋白表达存在几种关联,最高相关性显示造影剂应用后源自T1加权图像的“方差”(r = 0.46,p < 0.01)。 结论:源自MRI的放射组学特征能够反映子宫颈癌的肿瘤特征。特别是免疫相关特征通过MRI纹理特征得以体现。增殖潜能、细胞外基质组成和肿瘤-基质比也与放射组学特征显著相关。这些呈现的结果需要在独立队列中进行评估以检验其稳定性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/1aecdd5e32bc/432_2025_6253_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/abfb7ade34ec/432_2025_6253_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/e590a6277011/432_2025_6253_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/09b9b4d907f4/432_2025_6253_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/1aecdd5e32bc/432_2025_6253_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/abfb7ade34ec/432_2025_6253_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/e590a6277011/432_2025_6253_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/09b9b4d907f4/432_2025_6253_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18f9/12279667/1aecdd5e32bc/432_2025_6253_Fig4_HTML.jpg

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本文引用的文献

[1]
Radiomics predicting immunohistochemical markers in primary hepatic carcinoma: Current status and challenges.

Heliyon. 2024-11-20

[2]
Multimodal MRI-based deep-radiomics model predicts response in cervical cancer treated with neoadjuvant chemoradiotherapy.

Sci Rep. 2024-8-17

[3]
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.

CA Cancer J Clin. 2024

[4]
Linking tumor immune infiltrate and systemic immune mediators to treatment response and prognosis in advanced cervical cancer.

Sci Rep. 2023-12-19

[5]
Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.

BMC Med Imaging. 2023-10-11

[6]
Radiomics systematic review in cervical cancer: gynecological oncologists' perspective.

Int J Gynecol Cancer. 2023-10-2

[7]
MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone.

Magn Reson Imaging. 2022-9

[8]
Classifying early stages of cervical cancer with MRI-based radiomics.

Magn Reson Imaging. 2022-6

[9]
MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study.

Acta Radiol. 2023-1

[10]
An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer.

Br J Radiol. 2022-1-1

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