152 related articles for article (PubMed ID: 24063427)
1. Accuracy of clinicians and models for estimating the probability that a pulmonary nodule is malignant.
Balekian AA; Silvestri GA; Simkovich SM; Mestaz PJ; Sanders GD; Daniel J; Porcel J; Gould MK
Ann Am Thorac Soc; 2013 Dec; 10(6):629-35. PubMed ID: 24063427
[TBL] [Abstract][Full Text] [Related]
2. Communication About the Probability of Cancer in Indeterminate Pulmonary Nodules.
Maiga AW; Deppen SA; Massion PP; Callaway-Lane C; Pinkerman R; Dittus RS; Lambright ES; Nesbitt JC; Grogan EL
JAMA Surg; 2018 Apr; 153(4):353-357. PubMed ID: 29261826
[TBL] [Abstract][Full Text] [Related]
3. Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules.
Schultz EM; Sanders GD; Trotter PR; Patz EF; Silvestri GA; Owens DK; Gould MK
Thorax; 2008 Apr; 63(4):335-41. PubMed ID: 17965070
[TBL] [Abstract][Full Text] [Related]
4. Evaluation of models for predicting the probability of malignancy in patients with pulmonary nodules.
Li Y; Hu H; Wu Z; Yan G; Wu T; Liu S; Chen W; Lu Z
Biosci Rep; 2020 Feb; 40(2):. PubMed ID: 32068231
[TBL] [Abstract][Full Text] [Related]
5. Risk of malignancy in pulmonary nodules: A validation study of four prediction models.
Al-Ameri A; Malhotra P; Thygesen H; Plant PK; Vaidyanathan S; Karthik S; Scarsbrook A; Callister ME
Lung Cancer; 2015 Jul; 89(1):27-30. PubMed ID: 25864782
[TBL] [Abstract][Full Text] [Related]
6. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules.
Gould MK; Ananth L; Barnett PG;
Chest; 2007 Feb; 131(2):383-8. PubMed ID: 17296637
[TBL] [Abstract][Full Text] [Related]
7. Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules.
Choi HK; Ghobrial M; Mazzone PJ
Ann Am Thorac Soc; 2018 Oct; 15(10):1117-1126. PubMed ID: 30272500
[TBL] [Abstract][Full Text] [Related]
8. Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction.
Soardi GA; Perandini S; Larici AR; Del Ciello A; Rizzardi G; Solazzo A; Mancino L; Bernhart M; Motton M; Montemezzi S
Eur Radiol; 2017 May; 27(5):1929-1933. PubMed ID: 27553937
[TBL] [Abstract][Full Text] [Related]
9. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.
González Maldonado S; Delorme S; Hüsing A; Motsch E; Kauczor HU; Heussel CP; Kaaks R
JAMA Netw Open; 2020 Feb; 3(2):e1921221. PubMed ID: 32058555
[TBL] [Abstract][Full Text] [Related]
10. The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules.
Mehta HJ; Ravenel JG; Shaftman SR; Tanner NT; Paoletti L; Taylor KK; Tammemagi MC; Gomez M; Nietert PJ; Gould MK; Silvestri GA
Chest; 2014 Mar; 145(3):464-472. PubMed ID: 23949741
[TBL] [Abstract][Full Text] [Related]
11. Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.
Perandini S; Soardi GA; Motton M; Rossi A; Signorini M; Montemezzi S
Eur Radiol; 2016 Sep; 26(9):3071-6. PubMed ID: 26645862
[TBL] [Abstract][Full Text] [Related]
12. Indeterminate lung nodules in cancer patients: pretest probability of malignancy and the role of 18F-FDG PET/CT.
Evangelista L; Panunzio A; Polverosi R; Pomerri F; Rubello D
AJR Am J Roentgenol; 2014 Mar; 202(3):507-14. PubMed ID: 24555586
[TBL] [Abstract][Full Text] [Related]
13. Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi-institutional study.
She Y; Zhao L; Dai C; Ren Y; Jiang G; Xie H; Zhu H; Sun X; Yang P; Chen Y; Shi S; Shi W; Yu B; Xie D; Chen C
J Surg Oncol; 2017 Nov; 116(6):756-762. PubMed ID: 28570780
[TBL] [Abstract][Full Text] [Related]
14. [Development of a predicting model to estimate the probability of malignancy of solitary pulmonary nodules].
Tian R; Su MG; Tian Y; Li FL; Kuang AR
Sichuan Da Xue Xue Bao Yi Xue Ban; 2012 May; 43(3):404-8. PubMed ID: 22812247
[TBL] [Abstract][Full Text] [Related]
15. Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation.
Tanner NT; Porter A; Gould MK; Li XJ; Vachani A; Silvestri GA
Chest; 2017 Aug; 152(2):263-270. PubMed ID: 28115167
[TBL] [Abstract][Full Text] [Related]
16. Estimating the probability of malignancy in solitary pulmonary nodules. A Bayesian approach.
Cummings SR; Lillington GA; Richard RJ
Am Rev Respir Dis; 1986 Sep; 134(3):449-52. PubMed ID: 3752700
[TBL] [Abstract][Full Text] [Related]
17. Pulmonary nodule malignancy probability: a diagnostic accuracy meta-analysis of the Mayo model.
Papalampidou A; Papoutsi E; Katsaounou PA
Clin Radiol; 2022 Jun; 77(6):443-450. PubMed ID: 35422255
[TBL] [Abstract][Full Text] [Related]
18. Multi-observer concordance and accuracy of the British Thoracic Society scale and other visual assessment qualitative criteria for solid pulmonary nodule assessment using FDG PET-CT.
Fatania K; Brown PJ; Xie C; McDermott G; Callister MEJ; Graham R; Subesinghe M; Gleeson FV; Scarsbrook AF
Clin Radiol; 2020 Nov; 75(11):878.e21-878.e28. PubMed ID: 32709393
[TBL] [Abstract][Full Text] [Related]
19. Development and validation of a clinical prediction model to estimate the probability of malignancy in solitary pulmonary nodules in Chinese people.
Li Y; Chen KZ; Wang J
Clin Lung Cancer; 2011 Sep; 12(5):313-9. PubMed ID: 21889113
[TBL] [Abstract][Full Text] [Related]
20. Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population.
Cui X; Heuvelmans MA; Han D; Zhao Y; Fan S; Zheng S; Sidorenkov G; Groen HJM; Dorrius MD; Oudkerk M; de Bock GH; Vliegenthart R; Ye Z
Transl Lung Cancer Res; 2019 Oct; 8(5):605-613. PubMed ID: 31737497
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
