Literature DB >> 23943197

The promise and limits of PET texture analysis.

Nai-Ming Cheng1, Yu-Hua Dean Fang, Tzu-Chen Yen.   

Abstract

Metabolic heterogeneity is a recognized characteristic of malignant tumors. Positron emission tomography (PET) texture analysis evaluated intratumoral heterogeneity in the uptake of (18)F-fluorodeoxyglucose. There were recent evidences that PET textural features were of prognostic significance in patients with different solid tumors. Unfortunately, there are still crucial standardization challenges to transform PET texture parameters from their current use as research tools into the arena of validated technologies for use in oncology practice. Testing its generalizability, robustness, consistency, and limitations is necessary before implementing it in daily patient care.

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Year:  2013        PMID: 23943197     DOI: 10.1007/s12149-013-0759-8

Source DB:  PubMed          Journal:  Ann Nucl Med        ISSN: 0914-7187            Impact factor:   2.668


  18 in total

Review 1.  Characterization of PET/CT images using texture analysis: the past, the present… any future?

Authors:  Mathieu Hatt; Florent Tixier; Larry Pierce; Paul E Kinahan; Catherine Cheze Le Rest; Dimitris Visvikis
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-06-06       Impact factor: 9.236

Review 2.  Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

Authors:  Faiq Shaikh; Benjamin Franc; Erastus Allen; Evis Sala; Omer Awan; Kenneth Hendrata; Safwan Halabi; Sohaib Mohiuddin; Sana Malik; Dexter Hadley; Rasu Shrestha
Journal:  J Am Coll Radiol       Date:  2018-02-01       Impact factor: 5.532

3.  Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization.

Authors:  Lijun Lu; Wenbing Lv; Jun Jiang; Jianhua Ma; Qianjin Feng; Arman Rahmim; Wufan Chen
Journal:  Mol Imaging Biol       Date:  2016-12       Impact factor: 3.488

Review 4.  Towards enhanced PET quantification in clinical oncology.

Authors:  Habib Zaidi; Nicolas Karakatsanis
Journal:  Br J Radiol       Date:  2017-11-22       Impact factor: 3.039

5.  Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result.

Authors:  Seunggyun Ha; Hongyoon Choi; Gi Jeong Cheon; Keon Wook Kang; June-Key Chung; Euishin Edmund Kim; Dong Soo Lee
Journal:  Nucl Med Mol Imaging       Date:  2014-06-11

6.  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

Review 7.  Radiomics in precision medicine for lung cancer.

Authors:  Julie Constanzo; Lise Wei; Huan-Hsin Tseng; Issam El Naqa
Journal:  Transl Lung Cancer Res       Date:  2017-12

Review 8.  Applications and limitations of radiomics.

Authors:  Stephen S F Yip; Hugo J W L Aerts
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

9.  Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis.

Authors:  Joseph J Foy; Samuel G Armato; Hania A Al-Hallaq
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-21

10.  Variation in algorithm implementation across radiomics software.

Authors:  Joseph J Foy; Kayla R Robinson; Hui Li; Maryellen L Giger; Hania Al-Hallaq; Samuel G Armato
Journal:  J Med Imaging (Bellingham)       Date:  2018-12-04
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