242 related articles for article (PubMed ID: 37397861)
1. 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]
2. Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.
Alabi RO; Elmusrati M; Leivo I; Almangush A; Mäkitie AA
Int J Med Inform; 2024 Aug; 188():105464. PubMed ID: 38728812
[TBL] [Abstract][Full Text] [Related]
3. Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.
Le WT; Vorontsov E; Romero FP; Seddik L; Elsharief MM; Nguyen-Tan PF; Roberge D; Bahig H; Kadoury S
Sci Rep; 2022 Feb; 12(1):3183. PubMed ID: 35210482
[TBL] [Abstract][Full Text] [Related]
4. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.
Xia T; Zhao B; Li B; Lei Y; Song Y; Wang Y; Tang T; Ju S
J Magn Reson Imaging; 2024 Mar; 59(3):767-783. PubMed ID: 37647155
[TBL] [Abstract][Full Text] [Related]
5. Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study.
Mansouri Z; Salimi Y; Amini M; Hajianfar G; Oveisi M; Shiri I; Zaidi H
Radiat Oncol; 2024 Jan; 19(1):12. PubMed ID: 38254203
[TBL] [Abstract][Full Text] [Related]
6. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.
Huynh BN; Groendahl AR; Tomic O; Liland KH; Knudtsen IS; Hoebers F; van Elmpt W; Malinen E; Dale E; Futsaether CM
Front Med (Lausanne); 2023; 10():1217037. PubMed ID: 37711738
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer.
Forouzannezhad P; Maes D; Hippe DS; Thammasorn P; Iranzad R; Han J; Duan C; Liu X; Wang S; Chaovalitwongse WA; Zeng J; Bowen SR
Cancers (Basel); 2022 Feb; 14(5):. PubMed ID: 35267535
[TBL] [Abstract][Full Text] [Related]
9. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.
Jiang Y; Zhang Z; Yuan Q; Wang W; Wang H; Li T; Huang W; Xie J; Chen C; Sun Z; Yu J; Xu Y; Poultsides GA; Xing L; Zhou Z; Li G; Li R
Lancet Digit Health; 2022 May; 4(5):e340-e350. PubMed ID: 35461691
[TBL] [Abstract][Full Text] [Related]
10. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
11. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.
Bogowicz M; Jochems A; Deist TM; Tanadini-Lang S; Huang SH; Chan B; Waldron JN; Bratman S; O'Sullivan B; Riesterer O; Studer G; Unkelbach J; Barakat S; Brakenhoff RH; Nauta I; Gazzani SE; Calareso G; Scheckenbach K; Hoebers F; Wesseling FWR; Keek S; Sanduleanu S; Leijenaar RTH; Vergeer MR; Leemans CR; Terhaard CHJ; van den Brekel MWM; Hamming-Vrieze O; van der Heijden MA; Elhalawani HM; Fuller CD; Guckenberger M; Lambin P
Sci Rep; 2020 Mar; 10(1):4542. PubMed ID: 32161279
[TBL] [Abstract][Full Text] [Related]
12. Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging.
Chinnery T; Arifin A; Tay KY; Leung A; Nichols AC; Palma DA; Mattonen SA; Lang P
Can Assoc Radiol J; 2021 Feb; 72(1):73-85. PubMed ID: 32735452
[TBL] [Abstract][Full Text] [Related]
13. Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer.
Wu H; Liu X; Peng L; Yang Y; Zhou Z; Du D; Xu H; Lv W; Lu L
Phys Med Biol; 2023 Nov; 68(22):. PubMed ID: 37844604
[No Abstract] [Full Text] [Related]
14. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands.
Sheikh K; Lee SH; Cheng Z; Lakshminarayanan P; Peng L; Han P; McNutt TR; Quon H; Lee J
Radiat Oncol; 2019 Jul; 14(1):131. PubMed ID: 31358029
[TBL] [Abstract][Full Text] [Related]
15. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.
Ji GW; Zhu FP; Xu Q; Wang K; Wu MY; Tang WW; Li XC; Wang XH
EBioMedicine; 2019 Dec; 50():156-165. PubMed ID: 31735556
[TBL] [Abstract][Full Text] [Related]
16. Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.
Gong J; Wang T; Wang Z; Chu X; Hu T; Li M; Peng W; Feng F; Tong T; Gu Y
Cancer Imaging; 2024 Jan; 24(1):1. PubMed ID: 38167564
[TBL] [Abstract][Full Text] [Related]
17. Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China.
Xiang Y; Dong X; Zeng C; Liu J; Liu H; Hu X; Feng J; Du S; Wang J; Han Y; Luo Q; Chen S; Li Y
Front Immunol; 2022; 13():913703. PubMed ID: 35720336
[TBL] [Abstract][Full Text] [Related]
18. Effect of machine learning re-sampling techniques for imbalanced datasets in
Xie C; Du R; Ho JW; Pang HH; Chiu KW; Lee EY; Vardhanabhuti V
Eur J Nucl Med Mol Imaging; 2020 Nov; 47(12):2826-2835. PubMed ID: 32253486
[TBL] [Abstract][Full Text] [Related]
19. A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT.
Nikulin P; Zschaeck S; Maus J; Cegla P; Lombardo E; Furth C; Kaźmierska J; Rogasch JMM; Holzgreve A; Albert NL; Ferentinos K; Strouthos I; Hajiyianni M; Marschner SN; Belka C; Landry G; Cholewinski W; Kotzerke J; Hofheinz F; van den Hoff J
Eur J Nucl Med Mol Imaging; 2023 Jul; 50(9):2751-2766. PubMed ID: 37079128
[TBL] [Abstract][Full Text] [Related]
20. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation.
Xu H; Abdallah N; Marion JM; Chauvet P; Tauber C; Carlier T; Lu L; Hatt M
Eur J Nucl Med Mol Imaging; 2023 May; 50(6):1720-1734. PubMed ID: 36690882
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
