Literature DB >> 33882244

Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.

Meilinuer Abdurixiti1, Mayila Nijiati2, Rongfang Shen1, Qiu Ya3, Naibijiang Abuduxiku1, Mayidili Nijiati3.   

Abstract

OBJECTIVES: To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC).
METHODS: We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms "radiomics", "PET/CT", "NSCLC", and "EGFR". The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented.
RESULTS: Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898-0.998].
CONCLUSIONS: The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research. ADVANCES IN KNOWLEDGE: Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.

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Year:  2021        PMID: 33882244      PMCID: PMC8173688          DOI: 10.1259/bjr.20201272

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.629


  43 in total

1.  Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review.

Authors:  R W Y Granzier; T J A van Nijnatten; H C Woodruff; M L Smidt; M B I Lobbes
Journal:  Eur J Radiol       Date:  2019-11-06       Impact factor: 3.528

2.  Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer.

Authors:  Jianyuan Zhang; Xinming Zhao; Yan Zhao; Jingmian Zhang; Zhaoqi Zhang; Jianfang Wang; Yingchen Wang; Meng Dai; Jingya Han
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-11-14       Impact factor: 9.236

3.  Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications?

Authors:  Amandine Crombé; David Fadli; Antoine Italiano; Olivier Saut; Xavier Buy; Michèle Kind
Journal:  Eur J Radiol       Date:  2020-09-12       Impact factor: 3.528

4.  A meta-analysis of adjuvant EGFR-TKIs for patients with resected non-small cell lung cancer.

Authors:  Hua Cheng; Xiao-Jian Li; Xiao-Jin Wang; Zuo-Wen Chen; Rui-Qi Wang; Hong-Cheng Zhong; Tian-Chi Wu; Qing-Dong Cao
Journal:  Lung Cancer       Date:  2019-08-05       Impact factor: 5.705

5.  Correlation of EGFR or KRAS mutation status with 18F-FDG uptake on PET-CT scan in lung adenocarcinoma.

Authors:  Kazuya Takamochi; Kaoru Mogushi; Hideya Kawaji; Kota Imashimizu; Mariko Fukui; Shiaki Oh; Masayoshi Itoh; Yoshihide Hayashizaki; Weijey Ko; Masao Akeboshi; Kenji Suzuki
Journal:  PLoS One       Date:  2017-04-19       Impact factor: 3.240

6.  Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications.

Authors:  Jose M Castillo T; Muhammad Arif; Wiro J Niessen; Ivo G Schoots; Jifke F Veenland
Journal:  Cancers (Basel)       Date:  2020-06-17       Impact factor: 6.639

7.  Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Authors:  Shuo Wang; Jingyun Shi; Zhaoxiang Ye; Di Dong; Dongdong Yu; Mu Zhou; Ying Liu; Olivier Gevaert; Kun Wang; Yongbei Zhu; Hongyu Zhou; Zhenyu Liu; Jie Tian
Journal:  Eur Respir J       Date:  2019-03-28       Impact factor: 16.671

8.  Relationship of EGFR Mutation to Glucose Metabolic Activity and Asphericity of Metabolic Tumor Volume in Lung Adenocarcinoma.

Authors:  Wonseok Whi; Seunggyun Ha; Sungwoo Bae; Hongyoon Choi; Jin Chul Paeng; Gi Jeong Cheon; Keon Wook Kang; Dong Soo Lee
Journal:  Nucl Med Mol Imaging       Date:  2020-06-14

9.  Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis.

Authors:  Stephan Ursprung; Lucian Beer; Annemarie Bruining; Ramona Woitek; Grant D Stewart; Ferdia A Gallagher; Evis Sala
Journal:  Eur Radiol       Date:  2020-02-14       Impact factor: 5.315

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  2 in total

1.  Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.

Authors:  Shima Sepehri; Olena Tankyevych; Andrei Iantsen; Dimitris Visvikis; Mathieu Hatt; Catherine Cheze Le Rest
Journal:  Front Oncol       Date:  2021-10-18       Impact factor: 6.244

2.  New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes.

Authors:  Komei Kameyama; Kazuhiro Imai; Koichi Ishiyama; Shinogu Takashima; Shoji Kuriyama; Maiko Atari; Yoshiaki Ishii; Akihito Kobayashi; Shugo Takahashi; Mirai Kobayashi; Yuzu Harata; Yusuke Sato; Satoru Motoyama; Manabu Hashimoto; Kyoko Nomura; Yoshihiro Minamiya
Journal:  Thorac Cancer       Date:  2022-01-20       Impact factor: 3.500

  2 in total

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