146 related articles for article (PubMed ID: 28532542)
1. [Threshold Segmentation of Pulmonary Subsolid Nodules on CT Images:
Detection and Quantification of the Solid Component].
Zheng W; Wang Q; Wang Y; Guo F; Wang X; Yu T
Zhongguo Fei Ai Za Zhi; 2017 May; 20(5):341-345. PubMed ID: 28532542
[TBL] [Abstract][Full Text] [Related]
2. Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation.
Scholten ET; Jacobs C; van Ginneken B; van Riel S; Vliegenthart R; Oudkerk M; de Koning HJ; Horeweg N; Prokop M; Gietema HA; Mali WP; de Jong PA
Eur Radiol; 2015 Feb; 25(2):488-96. PubMed ID: 25287262
[TBL] [Abstract][Full Text] [Related]
3. Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma.
Li Q; Gu YF; Fan L; Li QC; Xiao Y; Liu SY
Br J Radiol; 2018 Jul; 91(1088):20180251. PubMed ID: 29791206
[TBL] [Abstract][Full Text] [Related]
4. Sub-solid nodule detectability in seven observers of seventy-nine clinical cases: comparison between ultra-low-dose chest digital tomosynthesis with iterative reconstruction and chest radiography by receiver-operating characteristics analysis.
Nagatani Y; Takahashi M; Ikeda M; Nitta N; Miyata K; Hanaoka J; Nakano Y; Matsuo S; Hamada Y; Sonoda A; Otani H; Ushio N; Ohta S; Murakami Y; Kaneko C; Inoue A; Kida T; Murata K
Eur J Radiol; 2018 Oct; 107():166-174. PubMed ID: 30292262
[TBL] [Abstract][Full Text] [Related]
5. Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis.
Silva M; Schaefer-Prokop CM; Jacobs C; Capretti G; Ciompi F; van Ginneken B; Pastorino U; Sverzellati N
Invest Radiol; 2018 Aug; 53(8):441-449. PubMed ID: 29543693
[TBL] [Abstract][Full Text] [Related]
6. Volume and mass doubling times of persistent pulmonary subsolid nodules detected in patients without known malignancy.
Song YS; Park CM; Park SJ; Lee SM; Jeon YK; Goo JM
Radiology; 2014 Oct; 273(1):276-84. PubMed ID: 24927472
[TBL] [Abstract][Full Text] [Related]
7. Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.
Chen X; Feng B; Chen Y; Hao Y; Duan X; Cui E; Liu Z; Zhang C; Long W
J Comput Assist Tomogr; 2019; 43(5):817-824. PubMed ID: 31343995
[TBL] [Abstract][Full Text] [Related]
8. Improving the prediction of lung adenocarcinoma invasive component on CT: Value of a vessel removal algorithm during software segmentation of subsolid nodules.
Garzelli L; Goo JM; Ahn SY; Chae KJ; Park CM; Jung J; Hong H
Eur J Radiol; 2018 Mar; 100():58-65. PubMed ID: 29496080
[TBL] [Abstract][Full Text] [Related]
9. Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction.
Park S; Lee SM; Kim W; Park H; Jung KH; Do KH; Seo JB
Radiology; 2021 Apr; 299(1):211-219. PubMed ID: 33560190
[TBL] [Abstract][Full Text] [Related]
10. Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas.
Cohen JG; Goo JM; Yoo RE; Park CM; Lee CH; van Ginneken B; Chung DH; Kim YT
Eur Radiol; 2016 Dec; 26(12):4465-4474. PubMed ID: 27048527
[TBL] [Abstract][Full Text] [Related]
11. Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.
Qi LL; Wang JW; Yang L; Huang Y; Zhao SJ; Tang W; Jin YJ; Zhang ZW; Zhou Z; Yu YZ; Wang YZ; Wu N
Eur Radiol; 2021 Jun; 31(6):3884-3897. PubMed ID: 33219848
[TBL] [Abstract][Full Text] [Related]
12. Effect of Slab Thickness on the Detection of Pulmonary Nodules by Use of CT Maximum and Minimum Intensity Projection.
Li WJ; Chu ZG; Zhang Y; Li Q; Zheng YN; Lv FJ
AJR Am J Roentgenol; 2019 Sep; 213(3):562-567. PubMed ID: 31063429
[No Abstract] [Full Text] [Related]
13. Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model.
Zhao H; Marshall HM; Yang IA; Bowman RV; Ayres J; Crossin J; Lau M; Slaughter RE; Redmond S; Passmore L; McCaul E; Courtney D; Leong SC; Windsor M; Zimmerman PV; Fong KM
Br J Radiol; 2016; 89(1060):20160016. PubMed ID: 26882046
[TBL] [Abstract][Full Text] [Related]
14. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: analysis of data from the randomised, controlled NELSON trial.
Walter JE; Heuvelmans MA; de Jong PA; Vliegenthart R; van Ooijen PMA; Peters RB; Ten Haaf K; Yousaf-Khan U; van der Aalst CM; de Bock GH; Mali W; Groen HJM; de Koning HJ; Oudkerk M
Lancet Oncol; 2016 Jul; 17(7):907-916. PubMed ID: 27283862
[TBL] [Abstract][Full Text] [Related]
15. Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?
Digumarthy SR; Padole AM; Rastogi S; Price M; Mooradian MJ; Sequist LV; Kalra MK
Cancer Imaging; 2019 Jun; 19(1):36. PubMed ID: 31182167
[TBL] [Abstract][Full Text] [Related]
16. Optimization of CT windowing for diagnosing invasiveness of adenocarcinoma presenting as sub-solid nodules.
Cui X; Fan S; Heuvelmans MA; Han D; Zhao Y; Groen HJM; Dorrius MD; Oudkerk M; de Bock GH; Vliegenthart R; Ye Z
Eur J Radiol; 2020 Jul; 128():108981. PubMed ID: 32371183
[TBL] [Abstract][Full Text] [Related]
17. Measurement Variability of Persistent Pulmonary Subsolid Nodules on Same-Day Repeat CT: What Is the Threshold to Determine True Nodule Growth during Follow-Up?
Kim H; Park CM; Song YS; Sunwoo L; Choi YR; Kim JI; Kim JH; Bae JS; Lee JH; Goo JM
PLoS One; 2016; 11(2):e0148853. PubMed ID: 26859665
[TBL] [Abstract][Full Text] [Related]
18. Subsolid Lung Nodule Classification: A CT Criterion for Improving Interobserver Agreement.
Revel MP; Mannes I; Benzakoun J; Guinet C; Léger T; Grenier P; Lupo A; Fournel L; Chassagnon G; Bommart S
Radiology; 2018 Jan; 286(1):316-325. PubMed ID: 28796590
[TBL] [Abstract][Full Text] [Related]
19. CT Manifestations of Tumor Spread Through Airspaces in Pulmonary Adenocarcinomas Presenting as Subsolid Nodules.
de Margerie-Mellon C; Onken A; Heidinger BH; VanderLaan PA; Bankier AA
J Thorac Imaging; 2018 Nov; 33(6):402-408. PubMed ID: 30067571
[TBL] [Abstract][Full Text] [Related]
20. Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules.
Kim H; Park CM; Gwak J; Hwang EJ; Lee SY; Jung J; Hong H; Goo JM
AJR Am J Roentgenol; 2019 Mar; 212(3):505-512. PubMed ID: 30476456
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]