318 related articles for article (PubMed ID: 32253486)
21. The prognostic role of 18F-fluorodeoxyglucose PET in head and neck cancer depends on HPV status.
Moan JM; Amdal CD; Malinen E; Svestad JG; Bogsrud TV; Dale E
Radiother Oncol; 2019 Nov; 140():54-61. PubMed ID: 31177043
[TBL] [Abstract][Full Text] [Related]
22. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma.
Bogowicz M; Riesterer O; Stark LS; Studer G; Unkelbach J; Guckenberger M; Tanadini-Lang S
Acta Oncol; 2017 Nov; 56(11):1531-1536. PubMed ID: 28820287
[TBL] [Abstract][Full Text] [Related]
23. Evaluation of the Prognostic Value of FDG PET/CT Parameters for Patients With Surgically Treated Head and Neck Cancer: A Systematic Review.
Creff G; Devillers A; Depeursinge A; Palard-Novello X; Acosta O; Jegoux F; Castelli J
JAMA Otolaryngol Head Neck Surg; 2020 May; 146(5):471-479. PubMed ID: 32215611
[TBL] [Abstract][Full Text] [Related]
24. Conversion of adverse data corpus to shrewd output using sampling metrics.
Ashraf S; Saleem S; Ahmed T; Aslam Z; Muhammad D
Vis Comput Ind Biomed Art; 2020 Aug; 3(1):19. PubMed ID: 32779031
[TBL] [Abstract][Full Text] [Related]
25. Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer.
Lv W; Xu H; Han X; Zhang H; Ma J; Rahmim A; Lu L
Cancers (Basel); 2022 Mar; 14(7):. PubMed ID: 35406449
[TBL] [Abstract][Full Text] [Related]
26. Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques.
Salmanpour MR; Rezaeijo SM; Hosseinzadeh M; Rahmim A
Diagnostics (Basel); 2023 May; 13(10):. PubMed ID: 37238180
[TBL] [Abstract][Full Text] [Related]
27. The importance of feature aggregation in radiomics: a head and neck cancer study.
Fontaine P; Acosta O; Castelli J; De Crevoisier R; Müller H; Depeursinge A
Sci Rep; 2020 Nov; 10(1):19679. PubMed ID: 33184313
[TBL] [Abstract][Full Text] [Related]
28. Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models.
Bogowicz M; Leijenaar RTH; Tanadini-Lang S; Riesterer O; Pruschy M; Studer G; Unkelbach J; Guckenberger M; Konukoglu E; Lambin P
Radiother Oncol; 2017 Dec; 125(3):385-391. PubMed ID: 29122358
[TBL] [Abstract][Full Text] [Related]
29. PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma.
Haider SP; Mahajan A; Zeevi T; Baumeister P; Reichel C; Sharaf K; Forghani R; Kucukkaya AS; Kann BH; Judson BL; Prasad ML; Burtness B; Payabvash S
Eur J Nucl Med Mol Imaging; 2020 Dec; 47(13):2978-2991. PubMed ID: 32399621
[TBL] [Abstract][Full Text] [Related]
30. Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma.
Fujima N; Andreu-Arasa VC; Meibom SK; Mercier GA; Salama AR; Truong MT; Sakai O
Clin Radiol; 2021 Sep; 76(9):711.e1-711.e7. PubMed ID: 33934877
[TBL] [Abstract][Full Text] [Related]
31. Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option.
Wang K; Dohopolski M; Zhang Q; Sher D; Wang J
Med Phys; 2023 Apr; 50(4):2212-2223. PubMed ID: 36484346
[TBL] [Abstract][Full Text] [Related]
32. DBCSMOTE: a clustering-based oversampling technique for data-imbalanced warfarin dose prediction.
Tao Y; Zhang Y; Jiang B
BMC Med Genomics; 2020 Oct; 13(Suppl 10):152. PubMed ID: 33087117
[TBL] [Abstract][Full Text] [Related]
33. Machine learning-based analysis of [
Cysouw MCF; Jansen BHE; van de Brug T; Oprea-Lager DE; Pfaehler E; de Vries BM; van Moorselaar RJA; Hoekstra OS; Vis AN; Boellaard R
Eur J Nucl Med Mol Imaging; 2021 Feb; 48(2):340-349. PubMed ID: 32737518
[TBL] [Abstract][Full Text] [Related]
34. Improved support vector machine classification for imbalanced medical datasets by novel hybrid sampling combining modified mega-trend-diffusion and bagging extreme learning machine model.
Lin LS; Kao CH; Li YJ; Chen HH; Chen HY
Math Biosci Eng; 2023 Sep; 20(10):17672-17701. PubMed ID: 38052532
[TBL] [Abstract][Full Text] [Related]
35. Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.
Kazmierski M; Welch M; Kim S; McIntosh C; Rey-McIntyre K; Huang SH; Patel T; Tadic T; Milosevic M; Liu FF; Ryczkowski A; Kazmierska J; Ye Z; Plana D; Aerts HJWL; Kann BH; Bratman SV; Hope AJ; Haibe-Kains B
Cancer Res Commun; 2023 Jun; 3(6):1140-1151. PubMed ID: 37397861
[TBL] [Abstract][Full Text] [Related]
36. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.
Oliveira C; Amstutz F; Vuong D; Bogowicz M; Hüllner M; Foerster R; Basler L; Schröder C; Eboulet EI; Pless M; Thierstein S; Peters S; Hillinger S; Tanadini-Lang S; Guckenberger M
EJNMMI Res; 2021 Aug; 11(1):79. PubMed ID: 34417899
[TBL] [Abstract][Full Text] [Related]
37. Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.
Lv W; Yuan Q; Wang Q; Ma J; Feng Q; Chen W; Rahmim A; Lu L
Mol Imaging Biol; 2019 Oct; 21(5):954-964. PubMed ID: 30671740
[TBL] [Abstract][Full Text] [Related]
38. Multiparametric functional MRI and
Martens RM; Koopman T; Lavini C; Ali M; Peeters CFW; Noij DP; Zwezerijnen G; Marcus JT; Vergeer MR; Leemans CR; de Bree R; de Graaf P; Boellaard R; Castelijns JA
Eur Radiol; 2021 Feb; 31(2):616-628. PubMed ID: 32851444
[TBL] [Abstract][Full Text] [Related]
39. [18F]-FDG PET and MRI radiomic signatures to predict the risk and the location of tumor recurrence after re-irradiation in head and neck cancer.
Beddok A; Orlhac F; Calugaru V; Champion L; Ala Eddine C; Nioche C; Créhange G; Buvat I
Eur J Nucl Med Mol Imaging; 2023 Jan; 50(2):559-571. PubMed ID: 36282298
[TBL] [Abstract][Full Text] [Related]
40. Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.
Zhong J; Frood R; Brown P; Nelstrop H; Prestwich R; McDermott G; Currie S; Vaidyanathan S; Scarsbrook AF
Clin Radiol; 2021 Jan; 76(1):78.e9-78.e17. PubMed ID: 33036778
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
