366 related articles for article (PubMed ID: 33067484)
1. Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.
Suh CH; Lee KH; Choi YJ; Chung SR; Baek JH; Lee JH; Yun J; Ham S; Kim N
Sci Rep; 2020 Oct; 10(1):17525. PubMed ID: 33067484
[TBL] [Abstract][Full Text] [Related]
2. Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.
Sim Y; Kim M; Kim J; Lee SK; Han K; Sohn B
Eur Radiol; 2024 May; 34(5):3102-3112. PubMed ID: 37848774
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.
Sohn B; Choi YS; Ahn SS; Kim H; Han K; Lee SK; Kim J
Laryngoscope; 2021 Mar; 131(3):E851-E856. PubMed ID: 33070337
[TBL] [Abstract][Full Text] [Related]
4. Correlation between Human Papillomavirus Status and Quantitative MR Imaging Parameters including Diffusion-Weighted Imaging and Texture Features in Oropharyngeal Carcinoma.
Ravanelli M; Grammatica A; Tononcelli E; Morello R; Leali M; Battocchio S; Agazzi GM; Buglione di Monale E Bastia M; Maroldi R; Nicolai P; Farina D
AJNR Am J Neuroradiol; 2018 Oct; 39(10):1878-1883. PubMed ID: 30213805
[TBL] [Abstract][Full Text] [Related]
5. Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.
Ren J; Yuan Y; Qi M; Tao X
Eur Radiol; 2020 Dec; 30(12):6858-6866. PubMed ID: 32591885
[TBL] [Abstract][Full Text] [Related]
6. Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma.
Boot PA; Mes SW; de Bloeme CM; Martens RM; Leemans CR; Boellaard R; van de Wiel MA; de Graaf P
Oral Oncol; 2023 Feb; 137():106307. PubMed ID: 36657208
[TBL] [Abstract][Full Text] [Related]
7. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas.
Bagher-Ebadian H; Siddiqui F; Ghanem AI; Zhu S; Lu M; Movsas B; Chetty IJ
Biomed Phys Eng Express; 2022 Jun; 8(4):. PubMed ID: 34781281
[No Abstract] [Full Text] [Related]
8. Technical note: On the development of an outcome-driven frequency filter for improving radiomics-based modeling of human papillomavirus (HPV) in patients with oropharyngeal squamous cell carcinoma.
Bagher-Ebadian H; Zhu S; Siddiqui F; Lu M; Movsas B; Chetty IJ
Med Phys; 2021 Nov; 48(11):7552-7562. PubMed ID: 34390003
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Clinical variables and magnetic resonance imaging-based radiomics predict human papillomavirus status of oropharyngeal cancer.
Bos P; van den Brekel MWM; Gouw ZAR; Al-Mamgani A; Waktola S; Aerts HJWL; Beets-Tan RGH; Castelijns JA; Jasperse B
Head Neck; 2021 Feb; 43(2):485-495. PubMed ID: 33029923
[TBL] [Abstract][Full Text] [Related]
11. Application of radiomics for the prediction of HPV status for patients with head and neck cancers.
Bagher-Ebadian H; Lu M; Siddiqui F; Ghanem AI; Wen N; Wu Q; Liu C; Movsas B; Chetty IJ
Med Phys; 2020 Feb; 47(2):563-575. PubMed ID: 31853980
[TBL] [Abstract][Full Text] [Related]
12. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
Chen W; Lin G; Chen Y; Cheng F; Li X; Ding J; Zhong Y; Kong C; Chen M; Xia S; Lu C; Ji J
BMC Cancer; 2024 Apr; 24(1):418. PubMed ID: 38580939
[TBL] [Abstract][Full Text] [Related]
13. Development and Testing of a Machine Learning Model Using
Woo C; Jo KH; Sohn B; Park K; Cho H; Kang WJ; Kim J; Lee SK
Korean J Radiol; 2023 Jan; 24(1):51-61. PubMed ID: 36606620
[TBL] [Abstract][Full Text] [Related]
14. Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.
Wang H; Song B; Ye N; Ren J; Sun X; Dai Z; Zhang Y; Chen BT
Eur J Radiol; 2020 Jan; 122():108755. PubMed ID: 31783344
[TBL] [Abstract][Full Text] [Related]
15. Intravoxel incoherent motion diffusion-weighted imaging for oropharyngeal squamous cell carcinoma: Correlation with human papillomavirus Status.
Vidiri A; Marzi S; Gangemi E; Benevolo M; Rollo F; Farneti A; Marucci L; Spasiano F; Sperati F; Di Giuliano F; Pellini R; Sanguineti G
Eur J Radiol; 2019 Oct; 119():108640. PubMed ID: 31442928
[TBL] [Abstract][Full Text] [Related]
16. Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma.
Li Q; Xu T; Gong J; Xiang S; Shen C; Zhou X; Hu C; Wu B; Lu X
Quant Imaging Med Surg; 2023 Apr; 13(4):2234-2247. PubMed ID: 37064405
[TBL] [Abstract][Full Text] [Related]
17. Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma.
Ranjbar S; Ning S; Zwart CM; Wood CP; Weindling SM; Wu T; Mitchell JR; Li J; Hoxworth JM
J Comput Assist Tomogr; 2018; 42(2):299-305. PubMed ID: 29189396
[TBL] [Abstract][Full Text] [Related]
18. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma.
Bogowicz M; Riesterer O; Ikenberg K; Stieb S; Moch H; Studer G; Guckenberger M; Tanadini-Lang S
Int J Radiat Oncol Biol Phys; 2017 Nov; 99(4):921-928. PubMed ID: 28807534
[TBL] [Abstract][Full Text] [Related]
19.
Jo KH; Kim J; Cho H; Kang WJ; Lee SK; Sohn B
Yonsei Med J; 2023 Dec; 64(12):738-744. PubMed ID: 37992746
[TBL] [Abstract][Full Text] [Related]
20. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.
Tang Y; Yang CM; Su S; Wang WJ; Fan LP; Shu J
BMC Cancer; 2021 Nov; 21(1):1268. PubMed ID: 34819043
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
