614 related articles for article (PubMed ID: 35847942)
1. Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.
Yu Q; Wang A; Gu J; Li Q; Ning Y; Peng J; Lv F; Zhang X
Front Oncol; 2022; 12():913898. PubMed ID: 35847942
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT.
Hu J; Xia X; Wang P; Peng Y; Liu J; Xie X; Liao Y; Wan Q; Li X
Front Oncol; 2022; 12():848798. PubMed ID: 35814386
[TBL] [Abstract][Full Text] [Related]
4. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.
Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST
Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969
[TBL] [Abstract][Full Text] [Related]
5. MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.
Gu J; Yu Q; Li Q; Peng J; Lv F; Gong B; Zhang X
Front Oncol; 2022; 12():1003639. PubMed ID: 36212455
[TBL] [Abstract][Full Text] [Related]
6. CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors.
Lu Y; Liu H; Liu Q; Wang S; Zhu Z; Qiu J; Xing W
Front Oncol; 2023; 13():1118351. PubMed ID: 36969052
[TBL] [Abstract][Full Text] [Related]
7. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
[No Abstract] [Full Text] [Related]
8. Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.
Xiao DX; Zhong JP; Peng JD; Fan CG; Wang XC; Wen XL; Liao WW; Wang J; Yin XF
BMC Med Imaging; 2023 Oct; 23(1):159. PubMed ID: 37845636
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
11. Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study.
Yang Y; Cheng J; Peng Z; Yi L; Lin Z; He A; Jin M; Cui C; Liu Y; Zhong Q; Zuo M
Acad Radiol; 2024 Apr; 31(4):1615-1628. PubMed ID: 37949702
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study.
Xv Y; Lv F; Guo H; Zhou X; Tan H; Xiao M; Zheng Y
Insights Imaging; 2021 Nov; 12(1):170. PubMed ID: 34800179
[TBL] [Abstract][Full Text] [Related]
13. [Value of the application of enhanced CT radiomics and machine learning in preoperative prediction of microvascular invasion in hepatocellular carcinoma].
Yu YX; Hu CH; Wang XM; Fan YF; Hu MJ; Shi C; Hu S; Zhu M; Zhang Y
Zhonghua Yi Xue Za Zhi; 2021 May; 101(17):1239-1245. PubMed ID: 34865392
[No Abstract] [Full Text] [Related]
14. [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]
15. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in
Zhang Y; Cheng C; Liu Z; Wang L; Pan G; Sun G; Chang Y; Zuo C; Yang X
Med Phys; 2019 Oct; 46(10):4520-4530. PubMed ID: 31348535
[TBL] [Abstract][Full Text] [Related]
16. Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.
Dong W; Xiong S; Lei P; Wang X; Liu H; Liu Y; Zou H; Fan B; Qiu Y
Front Oncol; 2022; 12():944005. PubMed ID: 36081562
[TBL] [Abstract][Full Text] [Related]
17. Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.
Wang Y; Gao J; Yin Z; Wen Y; Sun M; Han R
Front Oncol; 2024; 14():1384105. PubMed ID: 38803533
[TBL] [Abstract][Full Text] [Related]
18. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion.
Wan Q; Zhou J; Xia X; Hu J; Wang P; Peng Y; Zhang T; Sun J; Song Y; Yang G; Li X
Front Oncol; 2021; 11():683587. PubMed ID: 34868905
[TBL] [Abstract][Full Text] [Related]
19. MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.
Tsuchiya M; Masui T; Terauchi K; Yamada T; Katyayama M; Ichikawa S; Noda Y; Goshima S
Eur Radiol; 2022 Jun; 32(6):4090-4100. PubMed ID: 35044510
[TBL] [Abstract][Full Text] [Related]
20. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
