Literature DB >> 30620678

Radiomics in Pulmonary Lesion Imaging.

Cameron Hassani1, Bino A Varghese1, Jorge Nieva2, Vinay Duddalwar1.   

Abstract

OBJECTIVE: Diagnostic imaging has traditionally relied on a limited set of qualitative imaging characteristics for the diagnosis and management of lung cancer. Radiomics-the extraction and analysis of quantitative features from imaging-can identify additional imaging characteristics that cannot be seen by the eye. These features can potentially be used to diagnose cancer, identify mutations, and predict prognosis in an accurate and noninvasive fashion. This article provides insights about trends in radiomics of lung cancer and challenges to widespread adoption.
CONCLUSION: Radiomic studies are currently limited to a small number of cancer types. Its application across various centers are nonstandardized, leading to difficulties in comparing and generalizing results. The tools available to apply radiomics are specialized and limited in scope, blunting widespread use and clinical integration in the general population. Increasing the number of multicenter studies and consortiums and inclusion of radiomics in resident training will bring more attention and clarity to the growing field of radiomics.

Entities:  

Keywords:  lung cancer; pulmonary opacities; radiomics; texture

Mesh:

Year:  2019        PMID: 30620678     DOI: 10.2214/AJR.18.20623

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   6.582


  16 in total

1.  Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.

Authors:  Francesco Bianconi; Mario Luca Fravolini; Sofia Pizzoli; Isabella Palumbo; Matteo Minestrini; Maria Rondini; Susanna Nuvoli; Angela Spanu; Barbara Palumbo
Journal:  Quant Imaging Med Surg       Date:  2021-07

2.  Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules.

Authors:  Fabien Maldonado; Cyril Varghese; Srinivasan Rajagopalan; Fenghai Duan; Aneri B Balar; Dhairya A Lakhani; Sanja L Antic; Pierre P Massion; Tucker F Johnson; Ronald A Karwoski; Richard A Robb; Brian J Bartholmai; Tobias Peikert
Journal:  Eur Respir J       Date:  2021-04-01       Impact factor: 16.671

3.  Pre-treatment CT imaging in stage IIIA lung cancer: Can we predict local recurrence after definitive chemoradiotherapy?

Authors:  Andrew J Plodkowski; Jose Arimateia Batista Araujo-Filho; Cameron D A Simmers; Jeffrey Girshman; Micheal Raj; Junting Zheng; Andreas Rimner; Michelle S Ginsberg
Journal:  Clin Imaging       Date:  2020-07-17       Impact factor: 1.605

4.  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

5.  Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors.

Authors:  Thomas Weikert; Tugba Akinci D'Antonoli; Jens Bremerich; Bram Stieltjes; Gregor Sommer; Alexander W Sauter
Journal:  Contrast Media Mol Imaging       Date:  2019-07-01       Impact factor: 3.161

6.  Three dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers.

Authors:  Rui Han; Roshan Arjal; Jin Dong; Hong Jiang; Huan Liu; Dongyou Zhang; Lu Huang
Journal:  Thorac Cancer       Date:  2020-09-18       Impact factor: 3.500

7.  Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.

Authors:  Francesco Rizzetto; Francesca Calderoni; Cristina De Mattia; Arianna Defeudis; Valentina Giannini; Simone Mazzetti; Lorenzo Vassallo; Silvia Ghezzi; Andrea Sartore-Bianchi; Silvia Marsoni; Salvatore Siena; Daniele Regge; Alberto Torresin; Angelo Vanzulli
Journal:  Eur Radiol Exp       Date:  2020-11-10

8.  A Nomogram Based on CT Deep Learning Signature: A Potential Tool for the Prediction of Overall Survival in Resected Non-Small Cell Lung Cancer Patients.

Authors:  Ting Lin; Jinhai Mai; Meng Yan; Zhenhui Li; Xianyue Quan; Xin Chen
Journal:  Cancer Manag Res       Date:  2021-03-30       Impact factor: 3.989

9.  Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.

Authors:  Rong Niu; Jianxiong Gao; Xiaoliang Shao; Jianfeng Wang; Zhenxing Jiang; Yunmei Shi; Feifei Zhang; Yuetao Wang; Xiaonan Shao
Journal:  Front Oncol       Date:  2021-12-17       Impact factor: 6.244

10.  MRI-Based Radiomics for Differentiating Orbital Cavernous Hemangioma and Orbital Schwannoma.

Authors:  Liang Chen; Ya Shen; Xiao Huang; Hua Li; Jian Li; Ruili Wei; Weihua Yang
Journal:  Front Med (Lausanne)       Date:  2021-12-16
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