Radiomics-based tumor phenotype determination based on medical imaging and tumor microenvironment in a preclinical setting

被引:19
|
作者
Mueller, Johannes [1 ,2 ,3 ]
Leger, Stefan [1 ,2 ,4 ,5 ,6 ,7 ,8 ]
Zwanenburg, Alex [1 ,2 ,4 ,5 ,6 ,7 ,8 ]
Suckert, Theresa [1 ,2 ,9 ,10 ]
Luehr, Armin [1 ,2 ,11 ]
Beyreuther, Elke [1 ,2 ,12 ]
von Neubeck, Claere [1 ,2 ,13 ]
Krause, Mechthild [1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,14 ]
Loeck, Steffen [1 ,2 ,7 ,14 ]
Dietrich, Antje [1 ,2 ,9 ,10 ]
Buetof, Rebecca [1 ,2 ,4 ,5 ,6 ,7 ,8 ,14 ]
机构
[1] Tech Univ Dresden, Helmholtz Zentrum Dresden Rossendorf, OncoRay Natl Ctr Radiat Res Oncol, Fac Med, Dresden, Germany
[2] Tech Univ Dresden, Helmholtz Zentrum Dresden Rossendorf, Univ Hosp Carl Gustav Carus, Dresden, Germany
[3] Helmholtz Zentrum Dresden Rossendorf, Inst Radiooncol OncoRay, Dresden, Germany
[4] Natl Ctr Tumor Dis NCT, Partner Site Dresden, Dresden, Germany
[5] German Canc Res Ctr, Dresden, Germany
[6] Tech Univ Dresden, Fac Med, Fetscherstr 74, D-01307 Dresden, Germany
[7] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dresden, Germany
[8] Helmholtz Assoc Helmholtz Zentrum Dresden Rossend, Dresden, Germany
[9] German Canc Consortium DKTK, Partner Site Dresden, Dresden, Germany
[10] German Canc Res Ctr, Heidelberg, Germany
[11] TU Dortmund Univ, Dept Phys, Med Phys & Radiotherapy, Dortmund, Germany
[12] Helmholtz Zentrum Dresden Rossendorf, Inst Radiat Phys, Dresden, Germany
[13] Univ Duisburg Essen, Dept Particle Therapy, Univ Hosp Essen, Duisburg, Germany
[14] Tech Univ Dresden, Fac Med, Dept Radiotherapy & Radiat Oncol, Dresden, Germany
关键词
Head and neck; Preclinical; Radiomics; Tumor microenvironment; Hypoxia; PRIMARY RADIOCHEMOTHERAPY; PROSPECTIVE TRIAL; FDG-PET; HYPOXIA; CANCER; PARAMETERS; HEAD; MRI; RADIOTHERAPY; EXPRESSION;
D O I
10.1016/j.radonc.2022.02.020
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background and purpose: Radiomics analyses have been shown to predict clinical outcomes of radiotherapy based on medical imaging-derived biomarkers. However, the biological meaning attached to such image features often remains unclear, thus hindering the clinical translation of radiomics analysis. In this manuscript, we describe a preclinical radiomics trial, which attempts to establish correlations between the expression of histological tumor microenvironment (TME)- and magnetic resonance imaging (MRI)-derived image features. Materials & Methods: A total of 114 mice were transplanted with the radioresistant and radiosensitive head and neck squamous cell carcinoma cell lines SAS and UT-SCC-14, respectively. The models were irradiated with five fractions of protons or photons using different doses. Post-treatment T1-weighted MRI and histopathological evaluation of the TME was conducted to extract quantitative features pertaining to tissue hypoxia and vascularization. We performed radiomics analysis with leave-one-out cross validation to identify the features most strongly associated with the tumor's phenotype. Performance was assessed using the area under the curve (AUC(Valid)) and F1-score. Furthermore, we analyzed correlations between TME- and MRI features using the Spearman correlation coefficient rho. Results: TME and MRI-derived features showed good performance (AUC(Valid, TME) = 0.72, AUC(Valid, MRI) = 0.85, AUC(Valid, Combined) = 0.85) individual tumor phenotype prediction. We found correlation coefficients of rho = -0.46 between hypoxia-related TME features and texture-related MRI features. Tumor volume was a strong confounder for MRI feature expression. Conclusion: We demonstrated a preclinical radiomics implementation and notable correlations between MRI- and TME hypoxia-related features. Developing additional TME features may help to further unravel the underlying biology. (c) 2022 The Authors. Published by Elsevier B.V.
引用
收藏
页码:96 / 104
页数:9
相关论文
共 50 条
  • [1] Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
    Kang, Wendi
    Qiu, Xiang
    Luo, Yingen
    Luo, Jianwei
    Liu, Yang
    Xi, Junqing
    Li, Xiao
    Yang, Zhengqiang
    JOURNAL OF TRANSLATIONAL MEDICINE, 2023, 21 (01)
  • [2] Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis
    Wendi Kang
    Xiang Qiu
    Yingen Luo
    Jianwei Luo
    Yang Liu
    Junqing Xi
    Xiao Li
    Zhengqiang Yang
    Journal of Translational Medicine, 21
  • [3] Radiomics-based Malignancy Prediction of Parotid Gland Tumor
    Kamezawa, H.
    Arimura, H.
    Yasumatsu, R.
    Ninomiya, K.
    Haseai, S.
    INTERNATIONAL FORUM ON MEDICAL IMAGING IN ASIA 2019, 2019, 11050
  • [4] Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging
    Prasanna, Prateek
    Karnawat, Ayush
    Ismail, Marwa
    Madabhushi, Anant
    Tiwaria, Pallavi
    JOURNAL OF MEDICAL IMAGING, 2019, 6 (02)
  • [5] Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images
    Zossou, Vincent-Beni Sena
    Gnangnon, Freddy Houehanou Rodrigue
    Biaou, Olivier
    de Vathaire, Florent
    Allodji, Rodrigue S.
    Ezin, Eugene C.
    CANCERS, 2024, 16 (06)
  • [6] Reproducibility of radiomics for deciphering tumor phenotype with imaging
    Zhao, Binsheng
    Tan, Yongqiang
    Tsai, Wei-Yann
    Qi, Jing
    Xie, Chuanmiao
    Lu, Lin
    Schwartz, Lawrence H.
    SCIENTIFIC REPORTS, 2016, 6
  • [7] Reproducibility of radiomics for deciphering tumor phenotype with imaging
    Binsheng Zhao
    Yongqiang Tan
    Wei-Yann Tsai
    Jing Qi
    Chuanmiao Xie
    Lin Lu
    Lawrence H. Schwartz
    Scientific Reports, 6
  • [8] Tumor microenvironment based modulation of thyroid cancer phenotype
    Tuli, Neha Y.
    Cabin, Jonathan
    Suriano, Robert
    Bednarczyk, Robert
    Hanly, Elyse
    Geliebter, Jan
    Shin, Edward
    Tiwari, Raj K.
    CANCER RESEARCH, 2014, 74 (19)
  • [9] Preclinical Models and Imaging Modalities of the Tumor Microenvironment in Metastasis
    Sousa, Sofia
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2023, (193):
  • [10] Radiomics-based prediction of radiosensitivity from preclinical HNSCC histopathology images
    Michlikova, S.
    Meneghetti, A. Rabasco
    Loeck, S.
    Yakimovich, A.
    Rassamegevanon, T.
    von Neubeck, C.
    Dietrich, A.
    Krause, M.
    RADIOTHERAPY AND ONCOLOGY, 2023, 182 : S593 - S593