210 related articles for article (PubMed ID: 33865064)
1. Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models.
Bos P; van den Brekel MWM; Gouw ZAR; Al-Mamgani A; Taghavi M; Waktola S; Aerts HJWL; Castelijns JA; Beets-Tan RGH; Jasperse B
Eur J Radiol; 2021 Jun; 139():109701. PubMed ID: 33865064
[TBL] [Abstract][Full Text] [Related]
2. Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer.
Bos P; van den Brekel MWM; Taghavi M; Gouw ZAR; Al-Mamgani A; Waktola S; Aerts HJWL; Beets-Tan RGH; Castelijns JA; Jasperse B
Eur J Radiol; 2022 Mar; 148():110167. PubMed ID: 35086005
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.
Mes SW; van Velden FHP; Peltenburg B; Peeters CFW; Te Beest DE; van de Wiel MA; Mekke J; Mulder DC; Martens RM; Castelijns JA; Pameijer FA; de Bree R; Boellaard R; Leemans CR; Brakenhoff RH; de Graaf P
Eur Radiol; 2020 Nov; 30(11):6311-6321. PubMed ID: 32500196
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.
Bos P; Martens RM; de Graaf P; Jasperse B; van Griethuysen JJM; Boellaard R; Leemans CR; Beets-Tan RGH; van de Wiel MA; van den Brekel MWM; Castelijns JA
Eur Radiol; 2023 Apr; 33(4):2850-2860. PubMed ID: 36460924
[TBL] [Abstract][Full Text] [Related]
9. Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer.
Bos P; van den Brekel MWM; Taghavi M; Gouw ZAR; Al-Mamgani A; Waktola S; J W L Aerts H; Beets-Tan RGH; Castelijns JA; Jasperse B
Phys Med; 2022 Sep; 101():36-43. PubMed ID: 35882094
[TBL] [Abstract][Full Text] [Related]
10. Predicting the need for a replan in oropharyngeal cancer: A radiomic, clinical, and dosimetric model.
Chinnery TA; Lang P; Nichols AC; Mattonen SA
Med Phys; 2024 May; 51(5):3510-3520. PubMed ID: 38100260
[TBL] [Abstract][Full Text] [Related]
11. Impact of
Haider SP; Zeevi T; Sharaf K; Gross M; Mahajan A; Kann BH; Judson BL; Prasad ML; Burtness B; Aboian M; Canis M; Reichel CA; Baumeister P; Payabvash S
J Nucl Med; 2024 May; 65(5):803-809. PubMed ID: 38514087
[TBL] [Abstract][Full Text] [Related]
12. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.
Abdollahi H; Mofid B; Shiri I; Razzaghdoust A; Saadipoor A; Mahdavi A; Galandooz HM; Mahdavi SR
Radiol Med; 2019 Jun; 124(6):555-567. PubMed ID: 30607868
[TBL] [Abstract][Full Text] [Related]
13. CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.
Ma B; Guo J; Zhai TT; van der Schaaf A; Steenbakkers RJHM; van Dijk LV; Both S; Langendijk JA; Zhang W; Qiu B; van Ooijen PMA; Sijtsema NM
Med Phys; 2023 Oct; 50(10):6190-6200. PubMed ID: 37219816
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Machine learning and magnetic resonance imaging radiomics for predicting human papilloma virus status and prognostic factors in oropharyngeal squamous cell carcinoma.
Park YM; Lim JY; Koh YW; Kim SH; Choi EC
Head Neck; 2022 Apr; 44(4):897-903. PubMed ID: 35044020
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.
Wang J; Zhu X; Zeng J; Liu C; Shen W; Sun X; Lin Q; Fang J; Chen Q; Ji Y
Eur Radiol; 2023 Dec; 33(12):8554-8563. PubMed ID: 37439939
[TBL] [Abstract][Full Text] [Related]
18. Association of quantitative MRI-based radiomic features with prognostic factors and recurrence rate in oropharyngeal squamous cell carcinoma.
Giannitto C; Marvaso G; Botta F; Raimondi S; Alterio D; Ciardo D; Volpe S; De Piano F; Ancona E; Tagliabue M; Origgi D; Chiocca S; Maffini FA; Ansarin M; Bagnardi V; Cattani F; Nolè F; Preda L; Orecchia R; Cassano E; Cremonesi M; Starzyńska A; Bellomi M; Jereczek-Fossa BA
Neoplasma; 2020 Nov; 67(6):1437-1446. PubMed ID: 32787435
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma.
Haider SP; Zeevi T; Baumeister P; Reichel C; Sharaf K; Forghani R; Kann BH; Judson BL; Prasad ML; Burtness B; Mahajan A; Payabvash S
Cancers (Basel); 2020 Jul; 12(7):. PubMed ID: 32635216
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]