223 related articles for article (PubMed ID: 30527183)
1. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results.
Schwyzer M; Ferraro DA; Muehlematter UJ; Curioni-Fontecedro A; Huellner MW; von Schulthess GK; Kaufmann PA; Burger IA; Messerli M
Lung Cancer; 2018 Dec; 126():170-173. PubMed ID: 30527183
[TBL] [Abstract][Full Text] [Related]
2. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
Teramoto A; Fujita H; Yamamuro O; Tamaki T
Med Phys; 2016 Jun; 43(6):2821-2827. PubMed ID: 27277030
[TBL] [Abstract][Full Text] [Related]
3. Investigation of small lung lesion detection for lung cancer screening in low dose FDG PET imaging by deep neural networks.
Guo H; Wu J; Xie Z; Tham IWK; Zhou L; Yan J
Front Public Health; 2022; 10():1047714. PubMed ID: 36438275
[TBL] [Abstract][Full Text] [Related]
4. A pilot study on lung cancer detection based on regional metabolic activity distribution in digital low-dose 18F-FDG PET.
Messerli M; Muehlematter UJ; Fassbind S; Franzen D; Ferraro DA; Huellner MW; Treyer V; Curioni-Fontecedro A; Burger IA
Br J Radiol; 2021 Mar; 94(1119):20200244. PubMed ID: 33529052
[TBL] [Abstract][Full Text] [Related]
5. Automated interpretation of PET/CT images in patients with lung cancer.
Gutte H; Jakobsson D; Olofsson F; Ohlsson M; Valind S; Loft A; Edenbrandt L; Kjaer A
Nucl Med Commun; 2007 Feb; 28(2):79-84. PubMed ID: 17198346
[TBL] [Abstract][Full Text] [Related]
6. Assessment of indeterminate pulmonary nodules detected in lung cancer screening: Diagnostic accuracy of FDG PET/CT.
Garcia-Velloso MJ; Bastarrika G; de-Torres JP; Lozano MD; Sanchez-Salcedo P; Sancho L; Nuñez-Cordoba JM; Campo A; Alcaide AB; Torre W; Richter JA; Zulueta JJ
Lung Cancer; 2016 Jul; 97():81-6. PubMed ID: 27237032
[TBL] [Abstract][Full Text] [Related]
7. Detecting Pulmonary Nodules in Lung Cancer Patients Using Whole Body FDG PET/CT, High-resolution Lung Reformat of FDG PET/CT, or Diagnostic Breath Hold Chest CT.
Flavell RR; Behr SC; Mabray MC; Hernandez-Pampaloni M; Naeger DM
Acad Radiol; 2016 Sep; 23(9):1123-9. PubMed ID: 27283073
[TBL] [Abstract][Full Text] [Related]
8. Quantitative Accuracy and Lesion Detectability of Low-Dose
Schaefferkoetter JD; Yan J; Sjöholm T; Townsend DW; Conti M; Tam JK; Soo RA; Tham I
J Nucl Med; 2017 Mar; 58(3):399-405. PubMed ID: 27688481
[TBL] [Abstract][Full Text] [Related]
9. More advantages in detecting bone and soft tissue metastases from prostate cancer using
Pianou NK; Stavrou PZ; Vlontzou E; Rondogianni P; Exarhos DN; Datseris IE
Hell J Nucl Med; 2019; 22(1):6-9. PubMed ID: 30843003
[TBL] [Abstract][Full Text] [Related]
10. Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.
Kawata Y; Arimura H; Ikushima K; Jin Z; Morita K; Tokunaga C; Yabu-Uchi H; Shioyama Y; Sasaki T; Honda H; Sasaki M
Phys Med; 2017 Oct; 42():141-149. PubMed ID: 29173908
[TBL] [Abstract][Full Text] [Related]
11. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
Suzuki K; Li F; Sone S; Doi K
IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
[TBL] [Abstract][Full Text] [Related]
12. Validation of low-dose lung cancer PET-CT protocol and PET image improvement using machine learning.
Nai YH; Schaefferkoetter J; Fakhry-Darian D; O'Doherty S; Totman JJ; Conti M; Townsend DW; Sinha AK; Tan TH; Tham I; Alexander DC; Reilhac A
Phys Med; 2021 Jan; 81():285-294. PubMed ID: 33341375
[TBL] [Abstract][Full Text] [Related]
13. Incidence of Brain Metastases on Follow-up
Nia ES; Garland LL; Eshghi N; Nia BB; Avery RJ; Kuo PH
J Nucl Med Technol; 2017 Sep; 45(3):193-197. PubMed ID: 28705927
[TBL] [Abstract][Full Text] [Related]
14. Role of delayed-time-point imaging during abdominal and pelvic cancer screening using FDG-PET/CT in the general population.
Naganawa S; Yoshikawa T; Yasaka K; Maeda E; Hayashi N; Abe O
Medicine (Baltimore); 2017 Nov; 96(46):e8832. PubMed ID: 29145346
[TBL] [Abstract][Full Text] [Related]
15. Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection.
Wang R; Sui X; Schoepf UJ; Song W; Xue H; Jin Z; Schmidt B; Flohr TG; Canstein C; Spearman JV; Chen J; Meinel FG
AJR Am J Roentgenol; 2015 Apr; 204(4):743-9. PubMed ID: 25794063
[TBL] [Abstract][Full Text] [Related]
16. Clinical utility of F-18 FDG PET-CT in the initial evaluation of lung cancer.
Madsen PH; Holdgaard PC; Christensen JB; Høilund-Carlsen PF
Eur J Nucl Med Mol Imaging; 2016 Oct; 43(11):2084-97. PubMed ID: 27164899
[TBL] [Abstract][Full Text] [Related]
17. PET/MR imaging in the detection and characterization of pulmonary lesions: technical and diagnostic evaluation in comparison to PET/CT.
Rauscher I; Eiber M; Fürst S; Souvatzoglou M; Nekolla SG; Ziegler SI; Rummeny EJ; Schwaiger M; Beer AJ
J Nucl Med; 2014 May; 55(5):724-9. PubMed ID: 24652827
[TBL] [Abstract][Full Text] [Related]
18. FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review.
Groheux D; Quere G; Blanc E; Lemarignier C; Vercellino L; de Margerie-Mellon C; Merlet P; Querellou S
Diagn Interv Imaging; 2016 Oct; 97(10):1003-1017. PubMed ID: 27567555
[TBL] [Abstract][Full Text] [Related]
19. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.
Prahs P; Radeck V; Mayer C; Cvetkov Y; Cvetkova N; Helbig H; Märker D
Graefes Arch Clin Exp Ophthalmol; 2018 Jan; 256(1):91-98. PubMed ID: 29127485
[TBL] [Abstract][Full Text] [Related]
20. Quantitative CT density histogram values and standardized uptake values of FDG-PET/CT with respiratory gating can distinguish solid adenocarcinomas from squamous cell carcinomas of the lung.
Tsubakimoto M; Yamashiro T; Tamashiro Y; Murayama S
Eur J Radiol; 2018 Mar; 100():108-115. PubMed ID: 29496067
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]