Literature DB >> 32497604

2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease.

Reyhaneh Manafi-Farid1, Najme Karamzade-Ziarati1, Reza Vali2, Felix M Mottaghy3, Mohsen Beheshti4.   

Abstract

Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; FDG PET/CT; Lung cancer; Molecular imaging; Radiomics

Year:  2020        PMID: 32497604     DOI: 10.1016/j.ymeth.2020.05.023

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  10 in total

1.  Methyltransferase-like 3 facilitates lung cancer progression by accelerating m6A methylation-mediated primary miR-663 processing and impeding SOCS6 expression.

Authors:  Shengshu Li; Xiaoxin Lu; Dongyang Zheng; Weizong Chen; Yuzhu Li; Fang Li
Journal:  J Cancer Res Clin Oncol       Date:  2022-07-30       Impact factor: 4.322

2.  Interventions of Advanced Lung Cancer Patient Receiving Chemotherapy by Computed Tomography Image Information Data Analysis-Based Soothing Care Plans.

Authors:  Juan Wang; Shuangping Lu; Qundan Zhang
Journal:  Comput Math Methods Med       Date:  2022-06-09       Impact factor: 2.809

Review 3.  ImmunoPET: Antibody-Based PET Imaging in Solid Tumors.

Authors:  Reyhaneh Manafi-Farid; Bahar Ataeinia; Shaghayegh Ranjbar; Zahra Jamshidi Araghi; Mohammad Mobin Moradi; Christian Pirich; Mohsen Beheshti
Journal:  Front Med (Lausanne)       Date:  2022-06-28

4.  Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter.

Authors:  Cheng Chang; Maomei Ruan; Bei Lei; Jian Feng; Wenhui Xie; Hong Yu; Wenlu Zhao; Yaqiong Ge; Shaofeng Duan; Wenjing Teng; Qianfu Wu; Xiaohua Qian; Lihua Wang; Hui Yan; Ciyi Liu; Liu Liu
Journal:  EJNMMI Res       Date:  2022-04-21       Impact factor: 3.434

5.  Prognostication Based on Texture Analysis of Baseline 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Nonsmall-Cell Lung Carcinoma Patients Who Underwent Platinum-Based Chemotherapy as First-Line Treatment.

Authors:  Akshima Sharma; Anil Kumar Pandey; Anshul Sharma; Geetanjali Arora; Anant Mohan; Ashu Seith Bhalla; Lalit Gupta; Shiba Kalyan Biswal; Rakesh Kumar
Journal:  Indian J Nucl Med       Date:  2021-09-23

6.  18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.

Authors:  Xiu-Qing Xue; Wen-Ji Yu; Xun Shi; Xiao-Liang Shao; Yue-Tao Wang
Journal:  Front Oncol       Date:  2022-08-08       Impact factor: 5.738

7.  Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules.

Authors:  Yavuz Sami Salihoğlu; Rabiye Uslu Erdemir; Büşra Aydur Püren; Semra Özdemir; Çağlar Uyulan; Türker Tekin Ergüzel; Hüseyin Ozan Tekin
Journal:  Mol Imaging Radionucl Ther       Date:  2022-06-27

8.  A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions.

Authors:  Fei Xie; Kun Zheng; Linwen Liu; Xiaona Jin; Lilan Fu; Zhaohui Zhu
Journal:  Front Oncol       Date:  2022-06-02       Impact factor: 5.738

9.  Frontiers and hotspots of 18F-FDG PET/CT radiomics: A bibliometric analysis of the published literature.

Authors:  Xinghai Liu; Xianwen Hu; Xiao Yu; Pujiao Li; Cheng Gu; Guosheng Liu; Yan Wu; Dandan Li; Pan Wang; Jiong Cai
Journal:  Front Oncol       Date:  2022-09-13       Impact factor: 5.738

10.  Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation.

Authors:  Xixi Guo; Yuze Li; Chunjie Yang; Yanjiang Hu; Yun Zhou; Zhenhua Wang; Liguo Zhang; Hongjun Hu; Yuemin Wu
Journal:  J Healthc Eng       Date:  2021-10-20       Impact factor: 2.682

  10 in total

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