Literature DB >> 29662557

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications.

Geewon Lee1,2, So Hyeon Bak1,3, Ho Yun Lee1.   

Abstract

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

Entities:  

Keywords:  Biomarkers; Computed tomography; Image processing; Lung cancer

Year:  2017        PMID: 29662557      PMCID: PMC5897261          DOI: 10.1007/s13139-017-0506-5

Source DB:  PubMed          Journal:  Nucl Med Mol Imaging        ISSN: 1869-3474


  68 in total

1.  Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.

Authors:  Sanghun Choi; Eric A Hoffman; Sally E Wenzel; Mario Castro; Sean Fain; Nizar Jarjour; Mark L Schiebler; Kun Chen; Ching-Long Lin
Journal:  J Allergy Clin Immunol       Date:  2017-01-29       Impact factor: 10.793

2.  Role of quantitative CT in predicting hypoxemia and complications after lung lobectomy for cancer, with special reference to area of emphysema.

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Journal:  Chest       Date:  2005-11       Impact factor: 9.410

Review 3.  Review of automatic pulmonary lobe segmentation methods from CT.

Authors:  Tom Doel; David J Gavaghan; Vicente Grau
Journal:  Comput Med Imaging Graph       Date:  2014-10-28       Impact factor: 4.790

4.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.

Authors:  Balaji Ganeshan; Elleny Panayiotou; Kate Burnand; Sabina Dizdarevic; Ken Miles
Journal:  Eur Radiol       Date:  2011-11-17       Impact factor: 5.315

Review 5.  Lung cancer-a fractal viewpoint.

Authors:  Frances E Lennon; Gianguido C Cianci; Nicole A Cipriani; Thomas A Hensing; Hannah J Zhang; Chin-Tu Chen; Septimiu D Murgu; Everett E Vokes; Michael W Vannier; Ravi Salgia
Journal:  Nat Rev Clin Oncol       Date:  2015-07-14       Impact factor: 66.675

6.  Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system.

Authors:  Ra Gyoung Yoon; Joon Beom Seo; Namkug Kim; Hyun Joo Lee; Sang Min Lee; Young Kyung Lee; Jae Woo Song; Jin Woo Song; Dong Soon Kim
Journal:  Eur Radiol       Date:  2012-08-24       Impact factor: 5.315

Review 7.  Imaging of airways: chronic obstructive pulmonary disease.

Authors:  Julia Ley-Zaporozhan; Hans-Ulrich Kauczor
Journal:  Radiol Clin North Am       Date:  2009-03       Impact factor: 2.303

8.  Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.

Authors:  Jia Wu; Michael F Gensheimer; Xinzhe Dong; Daniel L Rubin; Sandy Napel; Maximilian Diehn; Billy W Loo; Ruijiang Li
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-03-24       Impact factor: 7.038

9.  Airway Remodelling in Asthma and COPD: Findings, Similarities, and Differences Using Quantitative CT.

Authors:  Gaël Dournes; François Laurent
Journal:  Pulm Med       Date:  2012-02-16

10.  Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: are they different from those in neoadjuvant concurrent chemoradiotherapy?

Authors:  Yousun Chong; Jae-Hun Kim; Ho Yun Lee; Yong Chan Ahn; Kyung Soo Lee; Myung-Ju Ahn; Jhingook Kim; Young Mog Shim; Joungho Han; Yoon-La Choi
Journal:  PLoS One       Date:  2014-02-26       Impact factor: 3.240

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  9 in total

1.  Heterogeneity Does Matter for Tumor Characterization.

Authors:  Won Woo Lee
Journal:  Nucl Med Mol Imaging       Date:  2018-05-18

2.  ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features.

Authors:  Jiaqi Li; Zhenbin Qiu; Chao Zhang; Sijie Chen; Mengmin Wang; Qiuchen Meng; Haiming Lu; Lei Wei; Hairong Lv; Wenzhao Zhong; Xuegong Zhang
Journal:  Eur Radiol       Date:  2022-08-24       Impact factor: 7.034

3.  CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study.

Authors:  Ji Won Lee; Chul Hwan Park; Kyunghwa Han; Jin Hur; Dong Jin Im; Kye Ho Lee; Tae Hoon Kim
Journal:  Sci Rep       Date:  2022-05-17       Impact factor: 4.996

4.  Radiomics for Classifying Histological Subtypes of Lung Cancer Based on Multiphasic Contrast-Enhanced Computed Tomography.

Authors:  Linning E; Lin Lu; Li Li; Hao Yang; Lawrence H Schwartz; Binsheng Zhao
Journal:  J Comput Assist Tomogr       Date:  2019 Mar/Apr       Impact factor: 1.826

5.  CT Radiomic Features for Predicting Resectability and TNM Staging in Thymic Epithelial Tumors.

Authors:  Jose Arimateia Batista Araujo-Filho; Maria Mayoral; Junting Zheng; Kay See Tan; Peter Gibbs; Annemarie Fernandes Shepherd; Andreas Rimner; Charles B Simone; Gregory Riely; James Huang; Michelle S Ginsberg
Journal:  Ann Thorac Surg       Date:  2021-04-09       Impact factor: 5.102

6.  Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study.

Authors:  Hong Lu; Wei Mu; Yoganand Balagurunathan; Jin Qi; Mahmoud A Abdalah; Alberto L Garcia; Zhaoxiang Ye; Robert J Gillies; Matthew B Schabath
Journal:  Cancer Imaging       Date:  2019-06-28       Impact factor: 3.909

7.  Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer.

Authors:  So Hyeon Bak; Hyunjin Park; Insuk Sohn; Seung Hak Lee; Myung-Ju Ahn; Ho Yun Lee
Journal:  Sci Rep       Date:  2019-06-19       Impact factor: 4.379

8.  Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study.

Authors:  Zixing Wang; Cuihong Yang; Wei Han; Xin Sui; Fuling Zheng; Fang Xue; Xiaoli Xu; Peng Wu; Yali Chen; Wentao Gu; Wei Song; Jingmei Jiang
Journal:  Insights Imaging       Date:  2022-04-28

9.  The impact of radiomics in predicting oncologic behavior of thymic epithelial tumors.

Authors:  Yoshiyuki Ozawa; Masaki Hara; Yuta Shibamoto
Journal:  Mediastinum       Date:  2019-06-21
  9 in total

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