Detection and Analysis of Lung Cancer Using Radiomic Approach

Lung cancer is the most prevailing form of cancer and claims more lives each year in comparison of colon, prostate, and breast cancers combined. The prominent types of lung cancers are explained here along with their database details. In this work, the radiomic approach is proposed for the process of detection and analysis of lung cancer due to its impressive prognostic power. This becomes possible with the advent of computer aided detection system that not only provides a cost effective technique but also provides a noninvasive provision. The main aspect of this approach relies on its ease of implementation over the different types of databases irrespective to the category of cancer. The precise detection and prediction is possible via radiomic approach, which becomes state-of-the-art technique for lung cancer analysis. In future, the radiomic approach would be employed along with the deep learning models in conjunction with the computer-aided detection system for better diagnosis purposes.

[1]  H. Hricak,et al.  Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores , 2015, European Radiology.

[2]  Raj Kumar Sagar,et al.  Detection of Lung Cancer Using Content Based Medical Image Retrieval , 2015, 2015 Fifth International Conference on Advanced Computing & Communication Technologies.

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

[4]  Li Cao,et al.  A detection approach for solitary pulmonary nodules based on CT images , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[5]  C. Begley,et al.  Drug development: Raise standards for preclinical cancer research , 2012, Nature.

[6]  Shweta Gupta,et al.  Variational Level Set Formulation and Filtering Techniques on CT Images , 2012 .

[7]  Hiroshi Fujita,et al.  Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter , 2012, International Journal of Computer Assisted Radiology and Surgery.

[8]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[9]  G. Parker,et al.  Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome , 2014, Clinical Cancer Research.

[10]  Kemal Tuncali,et al.  Abdominal masses sampled at PET/CT-guided percutaneous biopsy: initial experience with registration of prior PET/CT images. , 2010, Radiology.

[11]  Robert J. Gillies,et al.  TU-CD-BRB-02: BEST IN PHYSICS (JOINT IMAGING-THERAPY): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics , 2015 .

[12]  K. Gunavathi,et al.  Efficient and reliable lung nodule detection using a neural network based computer aided diagnosis system , 2012, 2012 International Conference on Emerging Trends in Electrical Engineering and Energy Management (ICETEEEM).

[13]  M. Kuo,et al.  Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.

[14]  Dazhe Zhao,et al.  A method of pulmonary nodule detection utilizing multiple support vector machines , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[15]  Rajesh Mehra,et al.  Breast cancer histology images classification: Training from scratch or transfer learning? , 2018, ICT Express.

[16]  Daisuke Yamamoto,et al.  Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images , 2009, Algorithms.

[17]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.

[18]  Rajesh Mehra,et al.  Automatic Magnification Independent Classification of Breast Cancer Tissue in Histological Images Using Deep Convolutional Neural Network , 2018, Communications in Computer and Information Science.

[19]  Thomas Krause,et al.  PET/CT-guided biopsies of metabolically active bone lesions: applications and clinical impact , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[20]  Sarah E Bohndiek,et al.  Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment , 2014, Magnetic resonance in medicine.

[21]  Robert J. Gillies,et al.  Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma , 2015, PloS one.

[22]  Steinar Lundgren,et al.  Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer , 2014, NMR in biomedicine.

[23]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[24]  Jamshid Dehmeshki,et al.  Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images , 2009, IEEE Transactions on Biomedical Engineering.

[25]  Omar S. Al-Kadi,et al.  Biomedical texture analysis : fundamentals, tools and challenges , 2017 .

[26]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[27]  S. S. Singh,et al.  Lung Cancer Detection on CT Images by Using Image Processing , 2012, 2012 International Conference on Computing Sciences.

[28]  Ricardo A. M. Valentim,et al.  Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects , 2014, BioMedical Engineering OnLine.

[29]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[30]  Weiqi Zhou,et al.  Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice , 2015, PloS one.