219 related articles for article (PubMed ID: 37671060)
1. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.
Baidya Kayal E; Ganguly S; Sasi A; Sharma S; Ds D; Saini M; Rangarajan K; Kandasamy D; Bakhshi S; Mehndiratta A
Front Oncol; 2023; 13():1212526. PubMed ID: 37671060
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography.
Lai YC; Wu KC; Tseng NC; Chen YJ; Chang CJ; Yen KY; Kao CH
Front Med (Lausanne); 2022; 9():773041. PubMed ID: 35372415
[TBL] [Abstract][Full Text] [Related]
4. Diagnostic efficacy of PET-FDG imaging in solitary pulmonary nodules. Potential role in evaluation and management.
Dewan NA; Gupta NC; Redepenning LS; Phalen JJ; Frick MP
Chest; 1993 Oct; 104(4):997-1002. PubMed ID: 8404239
[TBL] [Abstract][Full Text] [Related]
5. What Is the Comparative Ability of 18F-FDG PET/CT, 99mTc-MDP Skeletal Scintigraphy, and Whole-body MRI as a Staging Investigation to Detect Skeletal Metastases in Patients with Osteosarcoma and Ewing Sarcoma?
Aryal A; Kumar VS; Shamim SA; Gamanagatti S; Khan SA
Clin Orthop Relat Res; 2021 Aug; 479(8):1768-1779. PubMed ID: 33635285
[TBL] [Abstract][Full Text] [Related]
6. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.
Hendrix W; Hendrix N; Scholten ET; Mourits M; Trap-de Jong J; Schalekamp S; Korst M; van Leuken M; van Ginneken B; Prokop M; Rutten M; Jacobs C
Commun Med (Lond); 2023 Oct; 3(1):156. PubMed ID: 37891360
[TBL] [Abstract][Full Text] [Related]
7. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.
Garau N; Paganelli C; Summers P; Choi W; Alam S; Lu W; Fanciullo C; Bellomi M; Baroni G; Rampinelli C
Med Phys; 2020 Sep; 47(9):4125-4136. PubMed ID: 32488865
[TBL] [Abstract][Full Text] [Related]
8. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.
Schwyzer M; Martini K; Benz DC; Burger IA; Ferraro DA; Kudura K; Treyer V; von Schulthess GK; Kaufmann PA; Huellner MW; Messerli M
Eur Radiol; 2020 Apr; 30(4):2031-2040. PubMed ID: 31822970
[TBL] [Abstract][Full Text] [Related]
9. Clinical significance of small pulmonary nodules with little or no 18F-FDG uptake on PET/CT images of patients with nonthoracic malignancies.
O JH; Yoo IeR; Kim SH; Sohn HS; Chung SK
J Nucl Med; 2007 Jan; 48(1):15-21. PubMed ID: 17204694
[TBL] [Abstract][Full Text] [Related]
10. Is FDG-PET/CT Useful for Diagnosing Pulmonary Metastasis in Patients with Soft Tissue Sarcoma?
Hagi T; Nakamura T; Sugino Y; Matsubara T; Asanuma K; Sudo A
Anticancer Res; 2018 Jun; 38(6):3635-3639. PubMed ID: 29848720
[TBL] [Abstract][Full Text] [Related]
11. LUCIS: lung cancer imaging studies.
Harders SW
Dan Med J; 2012 Nov; 59(11):B4542. PubMed ID: 23171752
[TBL] [Abstract][Full Text] [Related]
12. Evaluating the Patient With a Pulmonary Nodule: A Review.
Mazzone PJ; Lam L
JAMA; 2022 Jan; 327(3):264-273. PubMed ID: 35040882
[TBL] [Abstract][Full Text] [Related]
13. AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma.
Kudo Y; Nakamura T; Matsubayashi J; Ichinose A; Goto Y; Amemiya R; Park J; Shimada Y; Kakihana M; Nagao T; Ohira T; Masumoto J; Ikeda N
Clin Lung Cancer; 2024 Apr; ():. PubMed ID: 38760224
[TBL] [Abstract][Full Text] [Related]
14. [Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].
Li X; Guo F; Zhou Z; Zhang F; Wang Q; Peng Z; Su D; Fan Y; Wang Y
Zhongguo Fei Ai Za Zhi; 2019 Jun; 22(6):336-340. PubMed ID: 31196366
[TBL] [Abstract][Full Text] [Related]
15. How Are Indeterminate Pulmonary Nodules at Diagnosis Associated with Survival in Patients with High-Grade Osteosarcoma?
Tsoi KM; Lowe M; Tsuda Y; Lex JR; Fujiwara T; Almeer G; Gregory J; Stevenson J; Evans SE; Botchu R; Jeys LM
Clin Orthop Relat Res; 2021 Feb; 479(2):298-308. PubMed ID: 32956141
[TBL] [Abstract][Full Text] [Related]
16. Combination of positron emission tomography/computed tomography and chest thin-layer high-resolution computed tomography for evaluation of pulmonary nodules: Correlation with imaging features, maximum standardized uptake value, and pathology.
Hou S; Lin X; Wang S; Shen Y; Meng Z; Jia Q; Tan J
Medicine (Baltimore); 2018 Aug; 97(31):e11640. PubMed ID: 30075545
[TBL] [Abstract][Full Text] [Related]
17. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.
Wang X; Gao M; Xie J; Deng Y; Tu W; Yang H; Liang S; Xu P; Zhang M; Lu Y; Fu C; Li Q; Fan L; Liu S
Front Oncol; 2022; 12():892890. PubMed ID: 35747810
[TBL] [Abstract][Full Text] [Related]
18. Evaluation of the Outcome of Lung Nodules Missed on 18F-FDG PET/MRI Compared with 18F-FDG PET/CT in Patients with Known Malignancies.
Sawicki LM; Grueneisen J; Buchbender C; Schaarschmidt BM; Gomez B; Ruhlmann V; Umutlu L; Antoch G; Heusch P
J Nucl Med; 2016 Jan; 57(1):15-20. PubMed ID: 26514173
[TBL] [Abstract][Full Text] [Related]
19. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
