Literature DB >> 25248853

Heterogeneity in [18F]fluorodeoxyglucose positron emission tomography/computed tomography of non-small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging.

Ober van Gómez López, Ana María García Vicente, Antonio Francisco Honguero Martínez, Angel María Soriano Castrejón, German Andrés Jiménez Londoño, José Manuel Udias, Pablo León Atance.   

Abstract

To investigate the relationships between tumor heterogeneity, assessed by texture analysis of [18F]fluorodeoxyglucose-positron emission tomography (FDG-PET) images, metabolic parameters, and pathologic staging in patients with non-small cell lung carcinoma (NSCLC). A retrospective analysis of 38 patients with histologically confirmed NSCLC who underwent staging FDG-PET/computed tomography was performed. Tumor images were segmented using a standardized uptake value (SUV) cutoff of 2.5. Five textural features, related to the heterogeneity of gray-level distribution, were computed (energy, entropy, contrast, homogeneity, and correlation). Additionally, metabolic parameters such as SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as pathologic staging, histologic subtype, and tumor diameter, were obtained. Finally, a correlation analysis was carried out. Of 38 tumors, 63.2% were epidermoid and 36.8% were adenocarcinomas. The mean ± standard deviation values of MTV and TLG were 30.47 ± 25.17 mL and 197.81 ± 251.11 g, respectively. There was a positive relationship of all metabolic parameters (SUVmax, SUVmean, MTV, and TLG) with entropy, correlation, and homogeneity and a negative relationship with energy and contrast. The T component of the pathologic TNM staging (pT) was similarly correlated with these textural parameters. Textural features associated with tumor heterogeneity were shown to be related to global metabolic parameters and pathologic staging.

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Year:  2014        PMID: 25248853     DOI: 10.2310/7290.2014.00032

Source DB:  PubMed          Journal:  Mol Imaging        ISSN: 1535-3508            Impact factor:   4.488


  16 in total

1.  Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.

Authors:  David V Fried; Osama Mawlawi; Lifei Zhang; Xenia Fave; Shouhao Zhou; Geoffrey Ibbott; Zhongxing Liao; Laurence E Court
Journal:  Radiology       Date:  2015-07-15       Impact factor: 11.105

Review 2.  Radiomics in Oncological PET/CT: Clinical Applications.

Authors:  Jeong Won Lee; Sang Mi Lee
Journal:  Nucl Med Mol Imaging       Date:  2017-10-20

3.  Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

Authors:  Hangfan Liu; Hongming Li; Mohamad Habes; Yuemeng Li; Pamela Boimel; James Janopaul-Naylor; Ying Xiao; Edgar Ben-Josef; Yong Fan
Journal:  IEEE Trans Biomed Eng       Date:  2020-01-27       Impact factor: 4.538

4.  Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker.

Authors:  Francesco Giganti; Sofia Antunes; Annalaura Salerno; Alessandro Ambrosi; Paolo Marra; Roberto Nicoletti; Elena Orsenigo; Damiano Chiari; Luca Albarello; Carlo Staudacher; Antonio Esposito; Alessandro Del Maschio; Francesco De Cobelli
Journal:  Eur Radiol       Date:  2016-08-23       Impact factor: 5.315

5.  Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

Authors:  Seyhan Karacavus; Bülent Yılmaz; Arzu Tasdemir; Ömer Kayaaltı; Eser Kaya; Semra İçer; Oguzhan Ayyıldız
Journal:  J Digit Imaging       Date:  2018-04       Impact factor: 4.056

6.  PET Imaging-Based Phenotyping as a Predictive Biomarker of Response to Tyrosine Kinase Inhibitor Therapy in Non-small Cell Lung Cancer: Are We There Yet?

Authors:  Victor H Gerbaudo; Chun K Kim
Journal:  Nucl Med Mol Imaging       Date:  2016-10-11

7.  Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients.

Authors:  Eric Wolsztynski; Finbarr O'Sullivan; Janet F Eary
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-29

Review 8.  Clinical applications of textural analysis in non-small cell lung cancer.

Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

9.  Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness.

Authors:  Song Liu; Huanhuan Zheng; Xia Pan; Ling Chen; Minke Shi; Yue Guan; Yun Ge; Jian He; Zhengyang Zhou
Journal:  J Thorac Dis       Date:  2017-11       Impact factor: 2.895

Review 10.  PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology.

Authors:  M Sollini; L Cozzi; L Antunovic; A Chiti; M Kirienko
Journal:  Sci Rep       Date:  2017-03-23       Impact factor: 4.379

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