• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于放射组学熵作为肿瘤异质性替代物的局限性:感兴趣区面积、采集方案和组织部位有实质性影响。

Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence.

机构信息

INSERM U1015, Equipe Labellisée Ligue Nationale Contre le Cancer, Gustave Roussy Cancer Campus, Villejuif, France.

Département de l'imagerie médicale, Gustave Roussy, Université Paris Saclay, F-94805, Villejuif, France.

出版信息

Sci Rep. 2017 Aug 11;7(1):7952. doi: 10.1038/s41598-017-08310-5.

DOI:10.1038/s41598-017-08310-5
PMID:28801575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5554130/
Abstract

Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman's Rho ranged between 0.41 and 0.59 (P = 0.0001, Bonferroni correction). Multivariate linear analysis revealed that entropy in SM#1 is significantly associated with (i) primary tumor type; (ii) entropy in SM#2 (same malignant process); (iii) ROI area size; (iv) metastasis site; and (v) entropy in the psoas muscle (reference tissue). Entropy was a logarithmic function of ROI area in normal control tissues (aorta, psoas) and in mathematical models (P < 0.01). We concluded that entropy is a tumor-specific metric only if confounding factors are corrected.

摘要

熵是一种很有前途的定量成像生物标志物,可用于描述癌症的成像表型。熵与肿瘤基因表达、肿瘤代谢、肿瘤分期、患者预后和治疗反应有关。我们的假设是,肿瘤特异性生物标志物(如熵)应该在同步转移的肿瘤之间存在相关性。因此,熵的大部分方差应该归因于恶性过程。我们对 112 名在 MOSCATO-01 临床试验中前瞻性入组的具有匹配/配对同步转移(SM#1 和 SM#2)的患者进行了分析。使用 TexRAD 软件从 CT 扫描的感兴趣区域(ROI)中提取了成像特征。我们发现,同步转移熵在 5 个空间尺度滤波器之间存在相关性:Spearman 相关系数范围为 0.41 至 0.59(P = 0.0001,Bonferroni 校正)。多元线性分析表明,SM#1 中的熵与(i)原发肿瘤类型;(ii)SM#2 中的熵(相同的恶性过程);(iii)ROI 面积大小;(iv)转移部位;以及(v)腰大肌(参考组织)中的熵显著相关。在正常对照组织(主动脉、腰大肌)和数学模型中,熵是 ROI 面积的对数函数(P < 0.01)。我们得出结论,只有在纠正了混杂因素的情况下,熵才是一种肿瘤特异性指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/01ff367b7561/41598_2017_8310_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/e6e716d133f1/41598_2017_8310_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/2e29cc359e02/41598_2017_8310_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/8d0421621b13/41598_2017_8310_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/5f395cc1bc92/41598_2017_8310_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/01ff367b7561/41598_2017_8310_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/e6e716d133f1/41598_2017_8310_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/2e29cc359e02/41598_2017_8310_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/8d0421621b13/41598_2017_8310_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/5f395cc1bc92/41598_2017_8310_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8297/5554130/01ff367b7561/41598_2017_8310_Fig5_HTML.jpg

相似文献

1
Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence.基于放射组学熵作为肿瘤异质性替代物的局限性:感兴趣区面积、采集方案和组织部位有实质性影响。
Sci Rep. 2017 Aug 11;7(1):7952. doi: 10.1038/s41598-017-08310-5.
2
A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.一种基于放射组学的方法来评估肿瘤浸润 CD8 细胞与抗 PD-1 或抗 PD-L1 免疫治疗反应的关系:一项影像学生物标志物、回顾性多队列研究。
Lancet Oncol. 2018 Sep;19(9):1180-1191. doi: 10.1016/S1470-2045(18)30413-3. Epub 2018 Aug 14.
3
Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer.双能 CT 碘图纹理分析对可切除性肺癌患者的预后价值。
Eur Radiol. 2019 Feb;29(2):915-923. doi: 10.1007/s00330-018-5639-0. Epub 2018 Jul 27.
4
Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging.基于医学影像的结直肠癌 KRAS 基因突变状态预测模型分析。
Comput Math Methods Med. 2021 Dec 22;2021:3953442. doi: 10.1155/2021/3953442. eCollection 2021.
5
CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.大型原发性肾细胞癌的CT纹理分析:治疗前肿瘤异质性与组织学结果及临床结局相关
AJR Am J Roentgenol. 2016 Jul;207(1):96-105. doi: 10.2214/AJR.15.15451. Epub 2016 May 4.
6
CT perfusion of renal cell carcinoma: impact of volume coverage on quantitative analysis.CT 灌注成像在肾细胞癌中的应用:容积覆盖范围对定量分析的影响。
Invest Radiol. 2012 Jan;47(1):33-40. doi: 10.1097/RLI.0b013e31822598c3.
7
Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers.肿瘤放射组学特征可补充临床-放射学因素,预测局部晚期喉咽癌的长期局部控制和免于喉切除术的生存。
Br J Radiol. 2020 May 1;93(1109):20190857. doi: 10.1259/bjr.20190857. Epub 2020 Feb 26.
8
Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis.扩散张量成像的纹理分析:鉴别胶质母细胞瘤与单发脑转移瘤。
Acta Radiol. 2019 Mar;60(3):356-366. doi: 10.1177/0284185118780889. Epub 2018 Jun 3.
9
Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan.基于 594 项指标的肺癌 CT 扫描定量成像决策支持(QIDS)工具一致性评估和放射组学分析。
Cancer Control. 2021 Jan-Dec;28:1073274820985786. doi: 10.1177/1073274820985786.
10
CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer.CT纹理分析有助于鉴别疑似肺癌患者纵隔内恶性和良性淋巴结。
Acta Radiol. 2016 Jun;57(6):669-76. doi: 10.1177/0284185115598808. Epub 2015 Aug 12.

引用本文的文献

1
Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy.预定义和数据驱动的CT影像组学可预测接受立体定向体部放疗的肺转移患者的无复发生存期和总生存期。
PLoS One. 2024 Dec 31;19(12):e0311910. doi: 10.1371/journal.pone.0311910. eCollection 2024.
2
Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters.肺癌 CT 扫描图像多参数重建的标注测试-重测数据集。
Sci Data. 2024 Nov 20;11(1):1259. doi: 10.1038/s41597-024-04085-3.
3
Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.

本文引用的文献

1
F-FDG PET and CT Scans Detect New Imaging Patterns of Response and Progression in Patients with Hodgkin Lymphoma Treated by Anti-Programmed Death 1 Immune Checkpoint Inhibitor.氟代脱氧葡萄糖正电子发射断层扫描和计算机断层扫描检测抗程序性死亡 1 免疫检查点抑制剂治疗的霍奇金淋巴瘤患者新的反应和进展成像模式。
J Nucl Med. 2018 Jan;59(1):15-24. doi: 10.2967/jnumed.117.193011. Epub 2017 Jun 8.
2
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.计算医学成像(影像组学)在肿瘤学中的应用:承诺与挑战。
Ann Oncol. 2017 Jun 1;28(6):1191-1206. doi: 10.1093/annonc/mdx034.
3
Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1.
基于脂肪抑制T2序列的临床影像组学列线图用于鉴别管腔型和非管腔型乳腺癌。
Front Oncol. 2024 Oct 25;14:1451414. doi: 10.3389/fonc.2024.1451414. eCollection 2024.
4
How Does Cancer Occur? How Should It Be Treated? Treatment from the Perspective of Alkalization Therapy Based on Science-Based Medicine.癌症是如何发生的?应该如何治疗?基于循证医学的碱化疗法视角下的治疗。
Biomedicines. 2024 Sep 26;12(10):2197. doi: 10.3390/biomedicines12102197.
5
Development and Validation of Radiomics-Based Models for Predicting the Parametrial Invasion in Stage IB1 to IIA2 Cervical Cancer.基于影像组学的模型用于预测IB1至IIA2期宫颈癌宫旁浸润的开发与验证
Int J Gen Med. 2024 Sep 3;17:3813-3824. doi: 10.2147/IJGM.S478842. eCollection 2024.
6
Neuroimaging characterization of multiple sclerosis lesions in pediatric patients: an exploratory radiomics approach.小儿多发性硬化症病变的神经影像学特征:一种探索性的放射组学方法。
Front Neurosci. 2024 Feb 2;18:1294574. doi: 10.3389/fnins.2024.1294574. eCollection 2024.
7
Radiogenomics: Contemporary Applications in the Management of Rectal Cancer.放射基因组学:在直肠癌管理中的当代应用
Cancers (Basel). 2023 Dec 12;15(24):5816. doi: 10.3390/cancers15245816.
8
Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer.采用三维和二维分割的放射组学分析预测胰腺癌的生存结果。
World J Radiol. 2023 Nov 28;15(11):304-314. doi: 10.4329/wjr.v15.i11.304.
9
Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.原发性肝癌患者术后肺部感染预测模型的建立与验证
World J Gastrointest Oncol. 2023 Jul 15;15(7):1241-1252. doi: 10.4251/wjgo.v15.i7.1241.
10
Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.深度学习图像重建算法对肝肿瘤患者CT影像组学特征的影响
Front Oncol. 2023 Apr 5;13:1167745. doi: 10.3389/fonc.2023.1167745. eCollection 2023.
抗 PD-1/PD-L1 治疗的癌症患者中出现的一种新的疾病进展模式:超进展性疾病
Clin Cancer Res. 2017 Apr 15;23(8):1920-1928. doi: 10.1158/1078-0432.CCR-16-1741. Epub 2016 Nov 8.
4
Abscopal effect in a Hodgkin lymphoma patient treated by an anti-programmed death 1 antibody.一名接受抗程序性死亡1抗体治疗的霍奇金淋巴瘤患者的远隔效应。
Eur J Cancer. 2016 Oct;66:91-4. doi: 10.1016/j.ejca.2016.06.017. Epub 2016 Aug 18.
5
Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy.快速客观的 CT 扫描预后评分可识别出接受抗 PD-1/-L1 治疗后具有长期临床获益的转移性患者。
Eur J Cancer. 2016 Sep;65:33-42. doi: 10.1016/j.ejca.2016.05.031. Epub 2016 Jul 21.
6
Brain tumor segmentation with Deep Neural Networks.基于深度神经网络的脑肿瘤分割。
Med Image Anal. 2017 Jan;35:18-31. doi: 10.1016/j.media.2016.05.004. Epub 2016 May 19.
7
RECIST 1.1 - Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group.RECIST 1.1 - 标准化与疾病特异性调整:RECIST 工作组的观点
Eur J Cancer. 2016 Jul;62:138-45. doi: 10.1016/j.ejca.2016.03.082. Epub 2016 May 26.
8
RECIST 1.1-Update and clarification: From the RECIST committee.RECIST 1.1更新与说明:来自RECIST委员会。
Eur J Cancer. 2016 Jul;62:132-7. doi: 10.1016/j.ejca.2016.03.081. Epub 2016 May 14.
9
Reproducibility of radiomics for deciphering tumor phenotype with imaging.用于通过成像解读肿瘤表型的放射组学的可重复性。
Sci Rep. 2016 Mar 24;6:23428. doi: 10.1038/srep23428.
10
Evaluation of Immune-Related Response Criteria and RECIST v1.1 in Patients With Advanced Melanoma Treated With Pembrolizumab.帕博利珠单抗治疗晚期黑色素瘤患者的免疫相关反应标准与RECIST v1.1评估
J Clin Oncol. 2016 May 1;34(13):1510-7. doi: 10.1200/JCO.2015.64.0391. Epub 2016 Mar 7.