Literature DB >> 27688481

Quantitative Accuracy and Lesion Detectability of Low-Dose 18F-FDG PET for Lung Cancer Screening.

Joshua D Schaefferkoetter1,2, Jianhua Yan3, Therese Sjöholm4, David W Townsend4,2, Maurizio Conti5, John Kit Chung Tam6, Ross A Soo7,8, Ivan Tham4,9.   

Abstract

Lung cancer remains responsible for more deaths worldwide than any other cancer, but recently there has been a significant shift in the clinical paradigm regarding the initial management of subjects at high risk for this disease. Low-dose CT has demonstrated significant improvements over planar x-ray screening for patient prognoses and is now performed in the United States. Specificity of this modality, however, is poor, and the additional information from PET has the potential to improve its accuracy. Routine screening requires consideration of the effective dose delivered to the patient, and this work investigates image quality of PET for low-dose conditions, in the context of lung lesion detectability. Reduced radiotracer doses were simulated by randomly discarding counts from clinical lung cancer scans acquired in list-mode. Bias and reproducibility of lesion activity values were relatively stable even at low total counts of around 5 million trues. Additionally, numeric observer models were developed and trained with the results of 2 physicians and 3 postdoctoral researchers with PET experience in a detection task; detection sensitivity of the observers was well correlated with lesion signal-to-noise ratio. The models were used prospectively to survey detectability of lung cancer lesions, and the findings suggested a lower limit around 10 million true counts for maximizing performance. Under the acquisition parameters used in this study, this translates to an effective patient dose of less than 0.4 mSv, potentially allowing a complete low-dose PET/CT lung screening scan to be obtained under 1 mSv.
© 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  PET/CT; low dose; lung cancer; screening

Mesh:

Substances:

Year:  2016        PMID: 27688481     DOI: 10.2967/jnumed.116.177592

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  10 in total

1.  Quantitative analysis of image metrics for reduced and standard dose pediatric 18F-FDG PET/MRI examinations.

Authors:  Pietro Zucchetta; Marco Branchini; Alessandra Zorz; Valentina Bodanza; Diego Cecchin; Marta Paiusco; Franco Bui
Journal:  Br J Radiol       Date:  2019-01-23       Impact factor: 3.039

2.  Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose.

Authors:  Qing Ye; Jing Wu; Yihuan Lu; Mika Naganawa; Jean-Dominique Gallezot; Tianyu Ma; Yaqiang Liu; Lynn Tanoue; Frank Detterbeck; Justin Blasberg; Ming-Kai Chen; Michael Casey; Richard E Carson; Chi Liu
Journal:  Phys Med Biol       Date:  2018-09-06       Impact factor: 3.609

3.  Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

Authors:  Yihuan Lu; Kathryn Fontaine; Tim Mulnix; John A Onofrey; Silin Ren; Vladimir Panin; Judson Jones; Michael E Casey; Robert Barnett; Peter Kench; Roger Fulton; Richard E Carson; Chi Liu
Journal:  J Nucl Med       Date:  2018-02-09       Impact factor: 10.057

4.  Validation of Deep Learning-based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma.

Authors:  Ashok J Theruvath; Florian Siedek; Ketan Yerneni; Anne M Muehe; Sheri L Spunt; Allison Pribnow; Michael Moseley; Ying Lu; Qian Zhao; Praveen Gulaka; Akshay Chaudhari; Heike E Daldrup-Link
Journal:  Radiol Artif Intell       Date:  2021-10-06

5.  Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Authors:  Xue Dong; Yang Lei; Tonghe Wang; Kristin Higgins; Tian Liu; Walter J Curran; Hui Mao; Jonathon A Nye; Xiaofeng Yang
Journal:  Phys Med Biol       Date:  2020-03-02       Impact factor: 3.609

6.  A pilot study on lung cancer detection based on regional metabolic activity distribution in digital low-dose 18F-FDG PET.

Authors:  Michael Messerli; Urs J Muehlematter; Saskia Fassbind; Daniel Franzen; Daniela A Ferraro; Martin W Huellner; Valerie Treyer; Alessandra Curioni-Fontecedro; Irene A Burger
Journal:  Br J Radiol       Date:  2021-02-02       Impact factor: 3.039

7.  Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space.

Authors:  Amirhossein Sanaat; Hossein Arabi; Ismini Mainta; Valentina Garibotto; Habib Zaidi
Journal:  J Nucl Med       Date:  2020-01-10       Impact factor: 11.082

8.  PET imaging of COVID-19: the target and the number.

Authors:  E Guedj; A Verger; S Cammilleri
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-04-17       Impact factor: 9.236

9.  Phantom-based image quality assessment of clinical 18F-FDG protocols in digital PET/CT and comparison to conventional PMT-based PET/CT.

Authors:  Silvano Gnesin; Christine Kieffer; Konstantinos Zeimpekis; Jean-Pierre Papazyan; Renaud Guignard; John O Prior; Francis R Verdun; Thiago V M Lima
Journal:  EJNMMI Phys       Date:  2020-01-06

10.  Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging.

Authors:  Xin Gao; Ivan W K Tham; Jianhua Yan
Journal:  Transl Cancer Res       Date:  2020-08       Impact factor: 1.241

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.