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Radiomics Of Pulmonary Nodules And Lung Cancer.

R. Wilson, A. Devaraj
Published 2017 · Medicine

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The large number of indeterminate pulmonary nodules encountered incidentally or during CT-based lung screening provides considerable diagnostic and management challenges. Conventional nodule evaluation relies on visually identifiable discriminators such as size and speculation. These visible nodule features are however small in number and subject to considerable interpretation variability. With the development of novel targeted therapies for lung cancer the diagnosis and characterization of early stage lung tumours has never been more important. Radiomics is a developing field aimed at deriving automated quantitative imaging features from medical images that can predict nodule and tumour behavior non-invasively. In contrast to conventional visual image features radiomics can extract substantially greater numbers of nodule features with much better reproducibility. This paper summarizes the basic process of radiomics and outlines why radiomic feature analysis may be particularly well suited to the evaluation of lung nodules. We review the current evidence for its clinical application with regards to pulmonary nodule management, considering promising applications such as predicting malignancy, histological subtyping, gene expression and post-treatment prognosis. Radiomics has the potential to transform the management of pulmonary nodules offering early diagnosis and personalized medicine using a method that is in cost-effective and non-invasive.
This paper references
10.1148/radiol.2015151169
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
10.1593/TLO.13844
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.
Y. Balagurunathan (2014)
10.1148/radiol.12111607
Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results.
O. Gevaert (2012)
10.1371/journal.pone.0100244
Noninvasive Image Texture Analysis Differentiates K-ras Mutation from Pan-Wildtype NSCLC and Is Prognostic
G. Weiss (2014)
10.3389/fonc.2016.00071
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Weimiao Wu (2016)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.1136/thoraxjnl-2015-207168
British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE
M. Callister (2015)
10.1097/JTO.0b013e3182843721
Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)—A Pilot Study
F. Maldonado (2013)
10.1148/RADIOL.2233011026
Volumetric growth rate of stage I lung cancer prior to treatment: serial CT scanning.
H. Winer-Muram (2002)
10.1259/BJR.73.876.11205667
Growth rate of small lung cancers detected on mass CT screening.
M. Hasegawa (2000)
10.1148/RADIOL.2312030167
Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable?
M. Revel (2004)
Usefulness of texture
SH Lee (2017)
10.1016/j.radonc.2015.02.015
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
T. Coroller (2015)
10.1117/12.2220768
Automatic lung nodule classification with radiomics approach
J. Ma (2016)
10.1016/j.radonc.2016.04.004
Radiomic phenotype features predict pathological response in non-small cell lung cancer.
T. Coroller (2016)
10.1016/j.tranon.2014.07.007
NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures
R. Colen (2014)
10.1016/j.cllc.2016.02.001
Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.
Y. Liu (2016)
10.1148/radiol.11100878
Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry.
J. Ko (2012)
10.1007/s00330-011-2319-8
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society
DP Naidich (2013)
10.1148/RADIOL.2372041887
Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society.
H. MacMahon (2005)
10.1148/RADIOL.2412051185
Distribution of stage I lung cancer growth rates determined with serial volumetric CT measurements.
S. Jennings (2006)
10.1016/j.jtho.2016.07.002
Predicting Malignant Nodules from Screening CT Scans
Samuel H. Hawkins (2016)
10.1016/j.ijrobp.2015.12.369
Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment.
Sarah A. Mattonen (2016)
10.1183/09031936.00197712
Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial
N. Horeweg (2013)
10.2214/AJR.05.1228
Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules.
M. Revel (2006)
10.1164/rccm.201107-1223OC
Doubling times and CT screen–detected lung cancers in the Pittsburgh Lung Screening Study.
D. Wilson (2012)
10.21037/tlcr.2017.01.04
Radiomics of pulmonary nodules and lung cancer.
Ryan Wilson (2017)
10.1148/radiol.14132187
Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.
Hee-Dong Chae (2014)
10.1371/journal.pone.0085167
Usefulness of Texture Analysis in Differentiating Transient from Persistent Part-solid Nodules(PSNs): A Retrospective Study
S. Lee (2014)
10.1148/radiol.12102489
Lung cancers diagnosed at annual CT screening: volume doubling times.
C. Henschke (2012)
Are twodimensional CT measurements of small noncalcified pulmonary nodules reliable? Radiology 2004;231:453-8
MP Revel (2004)
10.1038/srep11044
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Chintan Parmar (2015)



This paper is referenced by
10.1038/s41568-019-0142-8
Cancer overdiagnosis: a biological challenge and clinical dilemma
S. Srivastava (2019)
10.1117/12.2515609
Correlative hierarchical clustering-based low-rank dimensionality reduction of radiomics-driven phenotype in non-small cell lung cancer
B. Yousefi (2019)
10.21037/JTD.2020.03.68
Approaches to lung nodule risk assessment: clinician intuition versus prediction models.
A. Fox (2020)
10.1158/1078-0432.CCR-18-1305
A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors
W. Liang (2018)
10.1259/bjr.20170644
Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.
M. Silva (2018)
10.1053/j.ro.2018.02.006
Advances in Computed Tomography in Thoracic Imaging.
Asha Kandathil (2018)
10.1136/thoraxjnl-2018-212783
Lessons on managing pulmonary nodules from NELSON: we have come a long way
C. Horst (2019)
10.21037/qims.2019.09.07
A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma.
R. Zhang (2019)
10.21037/tlcr.2017.01.04
Radiomics of pulmonary nodules and lung cancer.
R. Wilson (2017)
10.21037/tlcr.2018.05.15
Lung cancer prediction using machine learning and advanced imaging techniques.
T. Kadir (2018)
10.1016/j.lungcan.2019.11.017
Long-term cancer risk associated with lung nodules observed on low-dose screening CT scans.
P. Pinsky (2019)
10.1016/j.diii.2020.10.004
Artificial intelligence solution to classify pulmonary nodules on CT.
D. Blanc (2020)
10.35940/ijitee.g5178.059720
Enhanced Lung Cancer Detection using Deep Learning Algorithm
Boddu Sekhar Babu (2020)
10.3390/diagnostics10090696
Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation
B. Palumbo (2020)
10.1186/s13244-019-0764-0
Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)
N. deSouza (2019)
10.1109/EExPolytech.2019.8906843
Radiomics: Extracting more Features using Endoscopic Imaging
F. Shariaty (2019)
10.1053/j.ro.2018.02.005
Computed Tomography Advances in Oncoimaging.
Ashita Rastogi (2018)
10.1007/s00330-019-06213-9
Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study
Wei Wu (2019)
10.1111/1759-7714.12821
Correlation between radiomic features based on contrast‐enhanced computed tomography images and Ki‐67 proliferation index in lung cancer: A preliminary study
Bodong Zhou (2018)
10.1164/rccm.201912-2505ED
2019 American Thoracic Society BEAR Cage Winning Proposal: Lung Imaging Using High-Performance Low-Field Magnetic Resonance Imaging
A. Campbell-Washburn (2020)
Correlation between radiomic features based on contrast-enhanced CT images and Ki-67 proliferation index in lung cancer: a preliminary study: radiomic feature and Ki-67 proliferation index
Bodong Zhou (2018)
10.1155/2018/6120703
A New Challenge for Radiologists: Radiomics in Breast Cancer
P. Crivelli (2018)
10.1007/s00261-020-02846-3
Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy
Jia Wang (2020)
10.1097/MD.0000000000019114
The diagnostic accuracy of artificial intelligence in thoracic diseases
Y. Yang (2020)
10.21037/PCM.2019.01.03
Radiomic features of the lung: a promising marker to predict response to immune-checkpoint inhibitors in non-small lung cancer patients
Paul Hofman (2019)
10.1038/s41598-020-60202-3
Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS
Gil Pinheiro (2020)
10.1038/s41598-018-20471-5
Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer
Chad Tang (2018)
10.1513/AnnalsATS.201803-173CME
Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules
Humberto K Choi (2018)
Convolutional Neural Networks for the Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors
Marguerite B. Basta (2020)
Application of radiomics and machine learning in head and neck cancers
Zhouying Peng (2020)
10.1109/BIBM47256.2019.8983174
Pixel-Level Clustering Reveals Intra-Tumor Heterogeneity in Non-Small Cell Lung Cancer
J. Li (2019)
10.3389/fphy.2018.00051
Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis
L. Papp (2018)
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