321 related articles for article (PubMed ID: 36810703)
21. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors.
Zheng Y; Zhou D; Liu H; Wen M
Eur Radiol; 2022 Oct; 32(10):6953-6964. PubMed ID: 35484339
[TBL] [Abstract][Full Text] [Related]
22. Radiomics based on enhanced CT for differentiating between pulmonary tuberculosis and pulmonary adenocarcinoma presenting as solid nodules or masses.
Zhao W; Xiong Z; Jiang Y; Wang K; Zhao M; Lu X; Liu A; Qin D; Li Z
J Cancer Res Clin Oncol; 2023 Jul; 149(7):3395-3408. PubMed ID: 35939114
[TBL] [Abstract][Full Text] [Related]
23. Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography.
Zhong Y; Xu H; Zhang W; Li H; Yu TF; Yuan M
J Comput Assist Tomogr; 2022 Nov-Dec 01; 46(6):878-883. PubMed ID: 35830384
[TBL] [Abstract][Full Text] [Related]
24. Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram.
Feng B; Chen X; Chen Y; Li Z; Hao Y; Zhang C; Li R; Liao Y; Zhang X; Huang Y; Long W
Clin Radiol; 2019 Jul; 74(7):570.e1-570.e11. PubMed ID: 31056198
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. [Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules].
Xu HJ; Yang Q; He P; Luo HH; Deng WM; Liu Z; Luo DH
Zhonghua Yi Xue Za Zhi; 2023 Nov; 103(41):3279-3286. PubMed ID: 37926572
[No Abstract] [Full Text] [Related]
27. Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients.
Wang H; Chen Y; Qiu J; Xie J; Lu W; Ma J; Jia M
Med Phys; 2024 Apr; 51(4):2578-2588. PubMed ID: 37966123
[TBL] [Abstract][Full Text] [Related]
28. Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.
Li Y; Lyu B; Wang R; Peng Y; Ran H; Zhou B; Liu Y; Bai G; Huai Q; Chen X; Zeng C; Wu Q; Zhang C; Gao S
Thorac Cancer; 2024 Feb; 15(6):466-476. PubMed ID: 38191149
[TBL] [Abstract][Full Text] [Related]
29. The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.
Zhang R; Wei Y; Shi F; Ren J; Zhou Q; Li W; Chen B
BMC Cancer; 2022 Nov; 22(1):1118. PubMed ID: 36319968
[TBL] [Abstract][Full Text] [Related]
30. Establishment of a Predictive Model for Surgical Resection of Ground-Glass Nodules.
Liu CL; Zhang F; Cai Q; Shen YY; Chen SQ
J Am Coll Radiol; 2019 Apr; 16(4 Pt A):435-445. PubMed ID: 30466899
[TBL] [Abstract][Full Text] [Related]
31. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules.
Cai J; Liu H; Yuan H; Wu Y; Xu Q; Lv Y; Li J; Fu J; Ye J
Clin Radiol; 2021 Feb; 76(2):143-151. PubMed ID: 33187676
[TBL] [Abstract][Full Text] [Related]
32. Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.
Wang Q; Xu S; Zhang G; Zhang X; Gu J; Yang S; Zeng M; Zhang Z
J Appl Clin Med Phys; 2022 Nov; 23(11):e13759. PubMed ID: 35998185
[TBL] [Abstract][Full Text] [Related]
33. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis.
Hu X; Ye W; Li Z; Chen C; Cheng S; Lv X; Weng W; Li J; Weng Q; Pang P; Xu M; Chen M; Ji J
Br J Radiol; 2020 Oct; 93(1114):20190762. PubMed ID: 32686958
[TBL] [Abstract][Full Text] [Related]
34. 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]
35. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
36. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
[TBL] [Abstract][Full Text] [Related]
37. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.
Shen Y; Xu F; Zhu W; Hu H; Chen T; Li Q
Ann Transl Med; 2020 Mar; 8(5):171. PubMed ID: 32309318
[TBL] [Abstract][Full Text] [Related]
38. Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm?
Wu S; Zhang N; Wu Z; Ren J; E L
Acad Radiol; 2022 Feb; 29 Suppl 2():S47-S52. PubMed ID: 33189549
[TBL] [Abstract][Full Text] [Related]
39. Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules.
Zhao W; Xu Y; Yang Z; Sun Y; Li C; Jin L; Gao P; He W; Wang P; Shi H; Hua Y; Li M
Eur J Radiol; 2019 Mar; 112():161-168. PubMed ID: 30777206
[TBL] [Abstract][Full Text] [Related]
40. Use of CT radiomics to differentiate minimally invasive adenocarcinomas and invasive adenocarcinomas presenting as pure ground-glass nodules larger than 10 mm.
Xiong Z; Jiang Y; Che S; Zhao W; Guo Y; Li G; Liu A; Li Z
Eur J Radiol; 2021 Aug; 141():109772. PubMed ID: 34022476
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]