154 related articles for article (PubMed ID: 36682111)
1. Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging.
Lv W; Zhou Z; Peng J; Peng L; Lin G; Wu H; Xu H; Lu L
Comput Methods Programs Biomed; 2023 Mar; 230():107341. PubMed ID: 36682111
[TBL] [Abstract][Full Text] [Related]
2. Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer.
Lv W; Ashrafinia S; Ma J; Lu L; Rahmim A
IEEE J Biomed Health Inform; 2020 Aug; 24(8):2268-2277. PubMed ID: 31804945
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer.
Salmanpour MR; Hosseinzadeh M; Rezaeijo SM; Rahmim A
Comput Methods Programs Biomed; 2023 Oct; 240():107714. PubMed ID: 37473589
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer.
Chen L; Zhou Z; Sher D; Zhang Q; Shah J; Pham NL; Jiang S; Wang J
Phys Med Biol; 2019 Mar; 64(7):075011. PubMed ID: 30780137
[TBL] [Abstract][Full Text] [Related]
9. FDG-PET/CT Radiomics Models for The Early Prediction of Locoregional Recurrence in Head and Neck Cancer.
Cong H; Peng W; Tian Z; Vallières M; Chuanpei X; Aijun Z; Benxin Z
Curr Med Imaging; 2021; 17(3):374-383. PubMed ID: 32652919
[TBL] [Abstract][Full Text] [Related]
10. Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.
Rahmim A; Toosi A; Salmanpour MR; Dubljevic N; Janzen I; Shiri I; Yuan R; Ho C; Zaidi H; MacAulay C; Uribe C; Yousefirizi F
Quant Imaging Med Surg; 2023 Dec; 13(12):7680-7694. PubMed ID: 38106259
[TBL] [Abstract][Full Text] [Related]
11. Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning.
Zhou Z; Chen L; Sher D; Zhang Q; Shah J; Pham NL; Jiang S; Wang J
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():1-4. PubMed ID: 30440295
[TBL] [Abstract][Full Text] [Related]
12. Prognostic value of tumor metabolic imaging phenotype by FDG PET radiomics in HNSCC.
Yoon H; Ha S; Kwon SJ; Park SY; Kim J; O JH; Yoo IR
Ann Nucl Med; 2021 Mar; 35(3):370-377. PubMed ID: 33554314
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and structured atrous recurrent convolutional neural network.
Wang M; Jiang H
Phys Med Biol; 2023 Dec; 68(23):. PubMed ID: 37956448
[No Abstract] [Full Text] [Related]
15. 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]
16. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.
Huang B; Sollee J; Luo YH; Reddy A; Zhong Z; Wu J; Mammarappallil J; Healey T; Cheng G; Azzoli C; Korogodsky D; Zhang P; Feng X; Li J; Yang L; Jiao Z; Bai HX
EBioMedicine; 2022 Aug; 82():104127. PubMed ID: 35810561
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.
Wang Y; Lombardo E; Avanzo M; Zschaek S; Weingärtner J; Holzgreve A; Albert NL; Marschner S; Fanetti G; Franchin G; Stancanello J; Walter F; Corradini S; Niyazi M; Lang J; Belka C; Riboldi M; Kurz C; Landry G
Comput Methods Programs Biomed; 2022 Jul; 222():106948. PubMed ID: 35752119
[TBL] [Abstract][Full Text] [Related]
20. Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma.
Nie P; Yang G; Wang N; Yan L; Miao W; Duan Y; Wang Y; Gong A; Zhao Y; Wu J; Zhang C; Wang M; Cui J; Yu M; Li D; Sun Y; Wang Y; Wang Z
Eur J Nucl Med Mol Imaging; 2021 Jan; 48(1):217-230. PubMed ID: 32451603
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
