409 related articles for article (PubMed ID: 30466899)
41. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters.
Liang L; Zhang H; Lei H; Zhou H; Wu Y; Shen J
Technol Cancer Res Treat; 2022; 21():15330338221119748. PubMed ID: 36259167
[No Abstract] [Full Text] [Related]
42. Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation.
Yang S; Wang Y; Shi Y; Yang G; Yan Q; Shen J; Wang Q; Zhang H; Yang S; Shan F; Zhang Z
Magn Reson Imaging; 2022 Jan; 85():80-86. PubMed ID: 34666158
[TBL] [Abstract][Full Text] [Related]
43. 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]
44. Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram.
Zhang R; Jia S; Zhai L; Wu F; Zhang S; Li F
BMC Med Imaging; 2024 Apr; 24(1):98. PubMed ID: 38678222
[TBL] [Abstract][Full Text] [Related]
45. Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules.
Wang B; Tang Y; Chen Y; Hamal P; Zhu Y; Wang T; Sun Y; Lu Y; Bhuva MS; Meng X; Yang Y; Ai Z; Wu C; Sun X
Lung Cancer; 2020 Jan; 139():103-110. PubMed ID: 31760351
[TBL] [Abstract][Full Text] [Related]
46. The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.
Liu Q; Huang Y; Chen H; Liu Y; Liang R; Zeng Q
BMC Cancer; 2020 Jun; 20(1):533. PubMed ID: 32513144
[TBL] [Abstract][Full Text] [Related]
47. HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules.
Zhang Y; Shen Y; Qiang JW; Ye JD; Zhang J; Zhao RY
Eur Radiol; 2016 Sep; 26(9):2921-8. PubMed ID: 26662263
[TBL] [Abstract][Full Text] [Related]
48. Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.
Zheng J; Kong J; Wu S; Li Y; Cai J; Yu H; Xie W; Qin H; Wu Z; Huang J; Lin T
Cancer; 2019 Dec; 125(24):4388-4398. PubMed ID: 31469418
[TBL] [Abstract][Full Text] [Related]
49. A wavelet features derived radiomics nomogram for prediction of malignant and benign early-stage lung nodules.
Jing R; Wang J; Li J; Wang X; Li B; Xue F; Shao G; Xue H
Sci Rep; 2021 Nov; 11(1):22330. PubMed ID: 34785692
[TBL] [Abstract][Full Text] [Related]
50. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.
Jiang Y; Xiong Z; Zhao W; Zhang J; Guo Y; Li G; Li Z
Gen Thorac Cardiovasc Surg; 2022 Oct; 70(10):880-890. PubMed ID: 35301662
[TBL] [Abstract][Full Text] [Related]
51. A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules.
Lv Y; Ye J; Yin YL; Ling J; Pan XP
Clin Radiol; 2022 Oct; 77(10):e741-e748. PubMed ID: 35840455
[TBL] [Abstract][Full Text] [Related]
52. A semiautomated radiomics model based on multimodal dual-layer spectral CT for preoperative discrimination of the invasiveness of pulmonary ground-glass nodules.
Wang Y; Chen H; Chen Y; Zhong Z; Huang H; Sun P; Zhang X; Wan Y; Li L; Ye T; Pan F; Yang L
J Thorac Dis; 2023 May; 15(5):2505-2516. PubMed ID: 37324063
[TBL] [Abstract][Full Text] [Related]
53. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.
Ye G; Wu G; Li K; Zhang C; Zhuang Y; Liu H; Song E; Qi Y; Li Y; Yang F; Liao Y
Acad Radiol; 2024 Apr; 31(4):1686-1697. PubMed ID: 37802672
[TBL] [Abstract][Full Text] [Related]
54. Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.
Yan J; Xue X; Gao C; Guo Y; Wu L; Zhou C; Chen F; Xu M
Quant Imaging Med Surg; 2022 Jan; 12(1):642-652. PubMed ID: 34993108
[TBL] [Abstract][Full Text] [Related]
55. Value of
Shao X; Niu R; Shao X; Jiang Z; Wang Y
EJNMMI Res; 2020 Jul; 10(1):80. PubMed ID: 32661639
[TBL] [Abstract][Full Text] [Related]
56. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
Wu YJ; Liu YC; Liao CY; Tang EK; Wu FZ
Sci Rep; 2021 Jan; 11(1):66. PubMed ID: 33462251
[TBL] [Abstract][Full Text] [Related]
57. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
[TBL] [Abstract][Full Text] [Related]
58. A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection.
Kim H; Goo JM; Park CM
Eur Radiol; 2019 Apr; 29(4):1674-1683. PubMed ID: 30255253
[TBL] [Abstract][Full Text] [Related]
59. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.
Ninomiya K; Yanagawa M; Tsubamoto M; Sato Y; Suzuki Y; Hata A; Kikuchi N; Yoshida Y; Yamagata K; Doi S; Ogawa R; Tokuda Y; Kido S; Tomiyama N
Jpn J Radiol; 2024 Jun; 42(6):590-598. PubMed ID: 38413550
[TBL] [Abstract][Full Text] [Related]
60. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection.
Xie T; Wang X; Li M; Tong T; Yu X; Zhou Z
Eur Radiol; 2020 May; 30(5):2513-2524. PubMed ID: 32006171
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]