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24.08.2022 | Imaging Informatics and Artificial Intelligence

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

verfasst von: Jing Yan, Qiuchang Sun, Xiangliang Tan, Chaofeng Liang, Hongmin Bai, Wenchao Duan, Tianhao Mu, Yang Guo, Yuning Qiu, Weiwei Wang, Qiaoli Yao, Dongling Pei, Yuanshen Zhao, Danni Liu, Jingxian Duan, Shifu Chen, Chen Sun, Wenqing Wang, Zhen Liu, Xuanke Hong, Xiangxiang Wang, Yu Guo, Yikai Xu, Xianzhi Liu, Jingliang Cheng, Zhi-Cheng Li, Zhenyu Zhang

Erschienen in: European Radiology | Ausgabe 2/2023

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Abstract

Objectives

To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

Methods

The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS.

Results

The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01).

Conclusions

Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient’s prognosis and guiding individualized treatment.

Key Points

• MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics.

• DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation.

• The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

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Ostrom QT, Patil N, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS (2020) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013-2017. Neuro Oncol 22:iv1-iv96

Stupp R, Mason WP, van den Bent MJ et al (2005) European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRef

Park AK, Kim P, Ballester LY, Esquenazi Y, Zhao Z (2019) Subtype-specific signaling pathways and genomic aberrations associated with prognosis of glioblastoma. Neuro Oncol 21:59–70CrossRef

Sottoriva A, Spiteri I, Piccirillo SG et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A 110:4009–4014

The Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061–1068CrossRef

Verhaak RG, Hoadley KA, Purdom E et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17:98–110CrossRef

Mazurowski MA (2015) Radiogenomics: what it is and why it is important. J Am Coll Radiol 12:862–866CrossRef

Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248CrossRef

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef

10.

Bismeijer T, van der Velden BH, Canisius S et al (2020) Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression. Radiology 296:277–287CrossRef

11.

Fan M, Xia P, Clarke R, Wang Y, Li L (2020) Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat Commun 11:4861CrossRef

12.

Yan J, Zhang S, Li KK et al (2020) Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma. EBioMedicine 61:103093CrossRef

13.

Beig N, Bera K, Prasanna P et al (2020) Radiogenomic-based survival risk stratification of tumor habitat on Gd-T1w MRI is associated with biological processes in glioblastoma. Clin Cancer Res 26:1866–1876CrossRef

14.

Beig N, Singh S, Bera K et al (2021) Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in Glioblastoma. Neuro Oncol 23:251–263CrossRef

15.

Sun Q, Chen Y, Liang C et al (2021) Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma. Radiology 14:203281

16.

Park JE, Kim HS, Park SY et al (2020) Prediction of core signaling pathway by using diffusion-and perfusion-based MRI radiomics and next-generation sequencing in isocitrate dehydrogenase wild-type glioblastoma. Radiology 294:388–397CrossRef

17.

Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2019) Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 290:290–297CrossRef

18.

Sun Q, Lin X, Zhao Y et al (2020) Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don’t forget the peritumoral region. Front Oncol 10:53

19.

Yan J, Zhao Y, Chen Y et al (2021) Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities. EBioMedicine 72:103583CrossRef

20.

Tomaszewski MR, Gillies RJ (2021) The biological meaning of radiomic features. Radiology 5:202553

21.

Itakura H, Achrol AS, Mitchell LA, et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 7:303ra138

22.

Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157

23.

Kickingereder P, Burth S, Wick A et al (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889

24.

Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857

25.

Bae S, Choi YS, Ahn SS, et al (2018) Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology 289:797-806

26.

Lao J, Chen Y, Li ZC et al (2017) A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 7:10353

27.

Han W, Qin L, Bay C et al (2020) Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. AJNR Am J Neuroradiol 41:40–48

28.

Nie D, Lu J, Zhang H et al (2019) Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep 9:1103

29.

Tang Z, Xu Y, Jin L et al (2020) Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients. IEEE Trans Med Imaging 39:2100–2109

30.

Yoon HG, Cheon W, Jeong SW et al (2020) Multi-parametric deep learning model for prediction of overall survival after postoperative concurrent chemoradiotherapy in glioblastoma patients. Cancers (Basel) 12:2284

31.

Parsons DW, Jones S, Zhang X et al (2008) An integrated genomic analysis of human glioblastoma multiforme. Science 321:1807–1812

32.

Kotliarova S, Fine HA (2012) SnapShot: glioblastoma multiforme. Cancer Cell 21:710–710

33.

Pearson JRD, Regad T (2017) Targeting cellular pathways in glioblastoma multiforme. Signal Transduct Target Ther 2:17040

34.

Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M et al (2020) A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro Oncol 22:402–411

35.

Li ZC, Bai H, Sun Q, et al (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650

36.

Hong EK, Choi SH, Shin DJ et al (2018) Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma. Eur Radiol 28:4350–4361

37.

Chen CH, Chen PY, Lin YY et al (2019) Suppression of tumor growth via IGFBP3 depletion as a potential treatment in glioma. J Neurosurg 132:168–179

38.

Si D, Yin F, Peng J, Zhang G (2020) High expression of CD44 predicts a poor prognosis in glioblastomas. Cancer Manag Res 12:769–775

39.

Shin CH, Robinson JP, Sonnen JA et al (2017) HBEGF promotes gliomagenesis in the context of Ink4a/Arf and Pten loss. Oncogene 36:4610–4618

40.

Kulkarni S, Goel-Bhattacharya S, Sengupta S, Cochran BH (2018) A large-scale RNAi screen identifies SGK1 as a key survival kinase for GBM stem cells. Mol Cancer Res 16:103–114

41.

Chen J, Hou C, Zheng Z, Lin H, Lv G, Zhou D (2019) Identification of secreted phosphoprotein 1 (SPP1) as a prognostic factor in lower-grade gliomas. World Neurosurg 130:e775–e785

42.

Demuth T, Reavie LB, Rennert JL et al (2007) MAP-ing glioma invasion: mitogen-activated protein kinase kinase 3 and p38 drive glioma invasion and progression and predict patient survival. Mol Cancer Ther 6:1212–1222

Titel: Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study
verfasst von: Jing Yan
Qiuchang Sun
Xiangliang Tan
Chaofeng Liang
Hongmin Bai
Wenchao Duan
Tianhao Mu
Yang Guo
Yuning Qiu
Weiwei Wang
Qiaoli Yao
Dongling Pei
Yuanshen Zhao
Danni Liu
Jingxian Duan
Shifu Chen
Chen Sun
Wenqing Wang
Zhen Liu
Xuanke Hong
Xiangxiang Wang
Yu Guo
Yikai Xu
Xianzhi Liu
Jingliang Cheng
Zhi-Cheng Li
Zhenyu Zhang
Publikationsdatum: 24.08.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 2/2023
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-09066-x

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Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study