Literature DB >> 28185635

A review of lung cancer screening and the role of computer-aided detection.

B Al Mohammad1, P C Brennan2, C Mello-Thoms2.   

Abstract

Lung cancer is the leading cause of cancer-related death worldwide; however, early diagnosis of lung cancer leads to higher survival rates. The National Lung Screening Trial (NLST) demonstrated that scanning with low-dose computed tomography (LDCT) led to a 20% reduction in mortality rate in a high-risk population. This paper covers new developments in screening eligibility criteria and the possible benefits and the harm of screening with CT. To make the screening process more feasible and help reduce the rate of missed lung nodules, computer-aided detection (CAD) has been introduced to assist radiologists in lung nodule detection. The aim of this paper is to review how CAD works, its performance in lung nodule detection, and the factors that influence its performance. This paper also aims to investigate the effect of different types of CAD on CT in lung nodule detection and the effect of CAD on radiologists' decision outcomes.
Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2017        PMID: 28185635     DOI: 10.1016/j.crad.2017.01.002

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  25 in total

1.  A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Authors:  Naji Khosravan; Haydar Celik; Baris Turkbey; Elizabeth C Jones; Bradford Wood; Ulas Bagci
Journal:  Med Image Anal       Date:  2018-10-28       Impact factor: 8.545

2.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

Review 3.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

Review 4.  Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study.

Authors:  David S Gierada; William C Black; Caroline Chiles; Paul F Pinsky; David F Yankelevitz
Journal:  Radiol Imaging Cancer       Date:  2020-03-27

5.  Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction.

Authors:  Yangfan Ni; Zhe Xie; Dezhong Zheng; Yuanyuan Yang; Weidong Wang
Journal:  Quant Imaging Med Surg       Date:  2022-01

Review 6.  Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review.

Authors:  Bradley Maller; Tawee Tanvetyanon
Journal:  Cureus       Date:  2022-07-26

7.  Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Quant Imaging Med Surg       Date:  2021-04

8.  Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients.

Authors:  Allan F F Alves; Sérgio A Souza; Raul L Ruiz; Tarcísio A Reis; Agláia M G Ximenes; Erica N Hasimoto; Rodrigo P S Lima; José Ricardo A Miranda; Diana R Pina
Journal:  Phys Eng Sci Med       Date:  2021-03-17

9.  Epidemiology and outcomes of primary pediatric lung malignancies: Updates from the SEER database.

Authors:  Nathan J Smith; Devashis Mukherjee; Yu Wang; Ruta Brazauskas; Ariel A Nelson; Chandler S Cortina
Journal:  Am J Surg       Date:  2021-02-01       Impact factor: 3.125

10.  Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

Authors:  Kunwei Li; Kunfeng Liu; Yinghua Zhong; Mingzhu Liang; Peixin Qin; Haijun Li; Rongguo Zhang; Shaolin Li; Xueguo Liu
Journal:  Quant Imaging Med Surg       Date:  2021-08
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