A Novel Radiogenomics Framework for Genomic and Image Feature Correlation using Deep Learning

Precision medicine still remains to be a prevalent treatment strategy which has been continuously pushed forward by the upcoming targeted therapies. To improve the precision and quantitative level, researches in radiomics and radiogenomics have devoted much of their endeavors to transform digital standard of medical images to mineable high-dimensional data by way of extracting mathematically quantitative features. However, most of the prior efforts could not effectively combine multi-source medical data sets together to generate satisfactory results and then visualize diagnoses by unifying low level features from images and other sources. In this paper, we design a novel and meaningful framework in order to map the features between medical images and gene expression profiles and quantity their correlations. To ameliorate, we take full advantage of deep learning methods, and characterize the lung cancer clinically at both genome and image levels. Our newly-devised protocol could give a strong association between gene and tumor growth statues, furthermore, it could provide cogent visual results for clinical research directly. The research presented in this paper could provide more comprehensive characterizations of tumor phenotypes, statues, and outcomes. As a result, it may be noted that, all of our prior efforts could contribute to the bigdata analysis for biomarker signatures, images, and “Omics”.

[1]  Alan D. Lopez,et al.  Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study , 2017, JAMA oncology.

[2]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[3]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[6]  Jie Tian,et al.  Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study , 2018, Gut.

[7]  Hao Chen,et al.  Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[8]  Christian Wachinger,et al.  DeepNAT: Deep convolutional neural network for segmenting neuroanatomy , 2017, NeuroImage.

[9]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[10]  Yoshua Bengio,et al.  Generative Adversarial Networks , 2014, ArXiv.

[11]  Su Ruan,et al.  Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.

[12]  Ian Goodfellow,et al.  Generative adversarial networks , 2020, Commun. ACM.

[13]  Jong Chul Ye,et al.  Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis , 2016, ArXiv.

[14]  Isaac Shiri,et al.  Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[15]  Jong Chul Ye,et al.  Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT , 2017, IEEE Transactions on Medical Imaging.

[16]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[17]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[18]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[19]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[20]  Issam El Naqa,et al.  Radiomics in precision medicine for lung cancer. , 2017, Translational lung cancer research.

[21]  Youbao Tang,et al.  CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation , 2018, MICCAI.

[22]  Tae-Sun Choi,et al.  Deciphering unclassified tumors of non-small-cell lung cancer through radiomics , 2017, Comput. Biol. Medicine.

[23]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[24]  Qianjin Feng,et al.  Pattern Classification for Gastrointestinal Stromal Tumors by Integration of Radiomics and Deep Convolutional Features , 2019, IEEE Journal of Biomedical and Health Informatics.

[25]  Prateek Prasanna,et al.  Radiomics and radiogenomics in lung cancer: A review for the clinician. , 2018, Lung cancer.

[26]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[27]  Mert R. Sabuncu,et al.  Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[28]  Hayit Greenspan,et al.  Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results , 2017, SASHIMI@MICCAI.

[29]  Eric P. Xing,et al.  SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays , 2017, DLMIA/ML-CDS@MICCAI.

[30]  Henggui Zhang,et al.  VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination , 2018, MICCAI.

[31]  M. Giger,et al.  Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. , 2013, Annual review of biomedical engineering.

[32]  Guang Yang,et al.  Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks , 2017, MIUA.

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  U. Rajendra Acharya,et al.  Towards precision medicine: from quantitative imaging to radiomics , 2018, Journal of Zhejiang University-SCIENCE B.

[35]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[36]  Lovedeep Gondara,et al.  Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).