The application of artificial intelligence and radiomics in lung cancer

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

[1]  M. van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.

[2]  Amparo Alonso-Betanzos,et al.  Filter Methods for Feature Selection - A Comparative Study , 2007, IDEAL.

[3]  Kristin D. Brockway,et al.  What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. , 2012, International journal of radiation oncology, biology, physics.

[4]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  S. Korreman,et al.  Interobserver delineation variation in lung tumour stereotactic body radiotherapy. , 2012, The British journal of radiology.

[7]  D. Aberle,et al.  Results of initial low-dose computed tomographic screening for lung cancer. , 2013, The New England journal of medicine.

[8]  E. Fishman,et al.  CT scan parameters and radiation dose: practical advice for radiologists. , 2013, Journal of the American College of Radiology : JACR.

[9]  H. Kohrt,et al.  Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients , 2014, Nature.

[10]  Muhammet Baykara,et al.  Texture based feature extraction methods for content based medical image retrieval systems. , 2014, Bio-medical materials and engineering.

[11]  Basic CT parameters. , 2014, AJR. American journal of roentgenology.

[12]  B. Kramer,et al.  Overdiagnosis in low-dose computed tomography screening for lung cancer. , 2014, JAMA internal medicine.

[13]  A. Nicholson,et al.  Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. , 2015, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[14]  J. Austin,et al.  The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. , 2015, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[15]  E. Kazerooni,et al.  Performance of Lung-RADS in the National Lung Screening Trial , 2015, Annals of Internal Medicine.

[16]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[17]  J. Crowley,et al.  The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer , 2007, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[18]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[19]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[21]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[22]  P. Lambin,et al.  Machine Learning methods for Quantitative Radiomic Biomarkers , 2015, Scientific Reports.

[23]  Peter A Balter,et al.  Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer , 2016 .

[24]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Jinzhong Yang,et al.  Computational resources for radiomics , 2016 .

[26]  Sang Joon Park,et al.  Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability , 2016, PloS one.

[27]  D. Rubin,et al.  Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. , 2016, Radiology.

[28]  M. Atkins,et al.  Predictive biomarkers for checkpoint inhibitor-based immunotherapy. , 2016, The Lancet. Oncology.

[29]  R. Govindan,et al.  Clinical Implications of Genomic Discoveries in Lung Cancer. , 2016, The New England journal of medicine.

[30]  P. Lambin,et al.  Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology , 2016, Front. Oncol..

[31]  Yanqi Huang,et al.  Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. , 2016, Radiology.

[32]  T. Burki.  Predicting lung cancer prognosis using machine learning. , 2016, The Lancet. Oncology.

[33]  Yanqi Huang,et al.  Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[35]  Samuel H. Hawkins,et al.  Predicting Malignant Nodules from Screening CT Scans , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[36]  P. Lambin,et al.  Defining the biological basis of radiomic phenotypes in lung cancer , 2017, eLife.

[37]  I. Sohn,et al.  Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[38]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  H. Kauczor,et al.  Correlation Between SUVmax and CT Radiomic Analysis Using Lymph Node Density in PET/CT-Based Lymph Node Staging , 2017, The Journal of Nuclear Medicine.

[40]  Hao Chen,et al.  Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge , 2016, Medical Image Anal..

[41]  E. Kazerooni,et al.  Lung-RADS: Pushing the Limits. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[42]  John Quackenbush,et al.  Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. , 2017, Cancer research.

[43]  N. Paragios,et al.  Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[44]  K. Dreyer,et al.  When Machines Think: Radiology's Next Frontier. , 2017, Radiology.

[45]  E. Giovannetti,et al.  Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets , 2017, Cancer cell.

[46]  Guixia Kang,et al.  3D multi-view convolutional neural networks for lung nodule classification , 2017, PloS one.

[47]  Dennis Wollersheim,et al.  Pulmonary nodule classification with deep residual networks , 2017, International Journal of Computer Assisted Radiology and Surgery.

[48]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[49]  I. Mellman,et al.  Elements of cancer immunity and the cancer–immune set point , 2017, Nature.

[50]  Kentaro Inamura,et al.  Lung Cancer: Understanding Its Molecular Pathology and the 2015 WHO Classification , 2017, Front. Oncol..

[51]  Joshua T. Cohen,et al.  Risk-Targeted Lung Cancer Screening , 2018, Annals of Internal Medicine.

[52]  L. Paz-Ares,et al.  Mechanisms of acquired resistance to first- and second-generation EGFR tyrosine kinase inhibitors , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[53]  J. Mechalakos,et al.  Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer , 2018, Medical physics.

[54]  A. Tafreshi,et al.  Pembrolizumab plus Chemotherapy for Squamous Non–Small‐Cell Lung Cancer , 2018, The New England journal of medicine.

[55]  Massimo Bellomi,et al.  Radiomics: the facts and the challenges of image analysis , 2018, European Radiology Experimental.

[56]  W. Stead.  Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. , 2018, JAMA.

[57]  Elise M. Blanchet,et al.  Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation. , 2018, Radiology.

[58]  D. Dong,et al.  Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer , 2017, Translational oncology.

[59]  D. Dong,et al.  A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy , 2018, Clinical Cancer Research.

[60]  A. Ng,et al.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists , 2018, PLoS medicine.

[61]  J. Brodersen,et al.  Estimation of Overdiagnosis of Lung Cancer in Low-Dose Computed Tomography Screening: A Secondary Analysis of the Danish Lung Cancer Screening Trial. , 2018, JAMA internal medicine.

[62]  J. Canales‐Vázquez,et al.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. , 2018, Radiology.

[63]  L. Schwartz,et al.  Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. , 2019, Academic radiology.

[64]  S. Novello,et al.  Pembrolizumab plus Chemotherapy in Metastatic Non–Small‐Cell Lung Cancer , 2018, The New England journal of medicine.

[65]  Guido Gerig,et al.  User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP , 2018, Neuroinformatics.

[66]  Wenbing Lv,et al.  Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer , 2018, EBioMedicine.

[67]  Geoffrey G. Zhang,et al.  Voxel size and gray level normalization of CT radiomic features in lung cancer , 2018, Scientific Reports.

[68]  Suane Pires P. da Silva,et al.  Lung Nodule Classification via Deep Transfer Learning in CT Lung Images , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[69]  R. Shukla,et al.  Nonlinear Heart Rate Variability based artificial intelligence in lung cancer prediction , 2018 .

[70]  D. Ettinger,et al.  Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. , 2018, Journal of the National Comprehensive Cancer Network : JNCCN.

[71]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[72]  C. Simone,et al.  Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. , 2018, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[73]  Caramella Caroline,et al.  Prognostic value of histogram analysis in advanced non-small cell lung cancer: a radiomic study , 2017, Oncotarget.

[74]  L. Cozzi,et al.  Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions , 2018, European Journal of Nuclear Medicine and Molecular Imaging.

[75]  Michael Wainberg,et al.  Deep learning in biomedicine , 2018, Nature Biotechnology.

[76]  J. Shih,et al.  Management of acquired resistance to EGFR TKI–targeted therapy in advanced non-small cell lung cancer , 2018, Molecular Cancer.

[77]  Isaac S Kohane,et al.  Artificial Intelligence in Healthcare , 2019, Artificial Intelligence and Machine Learning for Business for Non-Engineers.

[78]  L. Schwartz,et al.  CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study , 2018, Scientific Reports.

[79]  R. Gillies,et al.  Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study , 2018, PLoS medicine.

[80]  A. Mansfield,et al.  First‐Line Atezolizumab plus Chemotherapy in Extensive‐Stage Small‐Cell Lung Cancer , 2018, The New England journal of medicine.

[81]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[82]  Ehsan Kazemi,et al.  Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images , 2017, bioRxiv.

[83]  V. Rodriguez-Galiano,et al.  Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods. , 2018, The Science of the total environment.

[84]  N. Paragios,et al.  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. , 2018, The Lancet. Oncology.

[85]  Samuel H. Hawkins,et al.  Predicting malignant nodules by fusing deep features with classical radiomics features , 2018, Journal of medical imaging.

[86]  S. Park,et al.  Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement , 2019, European Radiology.

[87]  Su Jin Lee,et al.  A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. , 2019, Clinical Nuclear Medicine.

[88]  Shan Jiang,et al.  Classification of benign and malignant lung nodules from CT images based on hybrid features , 2019, Physics in medicine and biology.

[89]  Peng Liu,et al.  Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy , 2019, Experimental and therapeutic medicine.

[90]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[91]  The Lancet.  Lung cancer: some progress, but still a lot more to do. , 2019 .

[92]  Sang Min Lee,et al.  Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. , 2019, Radiology.

[93]  Sang Min Lee,et al.  Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer , 2019, Korean journal of radiology.

[94]  E. S. Durmaz,et al.  Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility. , 2019, AJR. American journal of roentgenology.

[95]  Lu Wang,et al.  Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks , 2019, International Journal of Computer Assisted Radiology and Surgery.

[96]  E. Choi,et al.  Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study. , 2019, Academic radiology.

[97]  T. Chan,et al.  The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy , 2019, Nature Reviews Cancer.

[98]  Jie Tian,et al.  The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges , 2019, Theranostics.

[99]  G. Collins,et al.  Artificial Intelligence Algorithms for Medical Prediction Should Be Nonproprietary and Readily Available. , 2019, JAMA internal medicine.

[100]  Seung-Min Park,et al.  Emerging ultrafast nucleic acid amplification technologies for next-generation molecular diagnostics. , 2019, Biosensors & bioelectronics.

[101]  V. Valentini,et al.  CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. , 2020, Academic radiology.

[102]  Ping Yi,et al.  Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence , 2019, BioMedical Engineering OnLine.

[103]  Wei-min Li,et al.  Implementation planning for lung cancer screening in China , 2019, Precision clinical medicine.

[104]  N. Kriegeskorte,et al.  Neural network models and deep learning , 2019, Current Biology.

[105]  Cesare Furlanello,et al.  Integrating deep and radiomics features in cancer bioimaging , 2019 .

[106]  Sai Ho Ling,et al.  Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure’ , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[107]  G. Corrao,et al.  Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. , 2019 .

[108]  Jon Kleinberg,et al.  Transfusion: Understanding Transfer Learning for Medical Imaging , 2019, NeurIPS.

[109]  H. Hatabu,et al.  Imaging of Precision Therapy for Lung Cancer: Current State of the Art. , 2019, Radiology.

[110]  Jie Xu,et al.  The practical implementation of artificial intelligence technologies in medicine , 2019, Nature Medicine.

[111]  Andreas M. Kaplan,et al.  Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence , 2019, Business Horizons.

[112]  Zhitao Xiao,et al.  Lung segmentation method with dilated convolution based on VGG-16 network , 2019, Computer assisted surgery.

[113]  D. Dong,et al.  Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning , 2019, European Respiratory Journal.

[114]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[115]  Jianyong Wang,et al.  MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks , 2020, Medical Image Anal..

[116]  D. Dong,et al.  Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker , 2020, Journal for ImmunoTherapy of Cancer.

[117]  Bernd Bischl,et al.  Benchmark for filter methods for feature selection in high-dimensional classification data , 2020, Comput. Stat. Data Anal..

[118]  Quanzheng Li,et al.  Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans. , 2020, European journal of radiology.

[119]  Zhang Yi,et al.  DeepLN: A framework for automatic lung nodule detection using multi-resolution CT screening images , 2020, Knowl. Based Syst..

[120]  Shudong Wang,et al.  Classification of Pathological Types of Lung Cancer from CT Images by Deep Residual Neural Networks with Transfer Learning Strategy , 2020, Open medicine.

[121]  A. Jemal,et al.  Cancer statistics, 2020 , 2020, CA: a cancer journal for clinicians.

[122]  Hassan Maleki,et al.  Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches , 2019, ArXiv.

[123]  T. Kadir,et al.  External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules , 2020, Thorax.

[124]  Ke-qiang Xu,et al.  Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma , 2020, Frontiers in Oncology.

[125]  M. Deevband,et al.  Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning , 2019, La radiologia medica.

[126]  Georg Langs,et al.  Introduction to Radiomics , 2020, The Journal of Nuclear Medicine.