These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
239 related articles for article (PubMed ID: 36202934)
1. Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Poirot MG; Caan MWA; Ruhe HG; Bjørnerud A; Groote I; Reneman L; Marquering HA Sci Rep; 2022 Oct; 12(1):16712. PubMed ID: 36202934 [TBL] [Abstract][Full Text] [Related]
2. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Zhao W; Hu Z; Kazerooni AF; Körzdörfer G; Nittka M; Davatzikos C; Viswanath SE; Wang X; Badve C; Ma D Invest Radiol; 2024 May; 59(5):359-371. PubMed ID: 37812483 [TBL] [Abstract][Full Text] [Related]
3. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms. Altazi BA; Zhang GG; Fernandez DC; Montejo ME; Hunt D; Werner J; Biagioli MC; Moros EG J Appl Clin Med Phys; 2017 Nov; 18(6):32-48. PubMed ID: 28891217 [TBL] [Abstract][Full Text] [Related]
4. Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features. Tixier F; Um H; Young RJ; Veeraraghavan H Med Phys; 2019 Aug; 46(8):3582-3591. PubMed ID: 31131906 [TBL] [Abstract][Full Text] [Related]
5. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. Gross M; Huber S; Arora S; Ze'evi T; Haider SP; Kucukkaya AS; Iseke S; Kuhn TN; Gebauer B; Michallek F; Dewey M; Vilgrain V; Sartoris R; Ronot M; Jaffe A; Strazzabosco M; Chapiro J; Onofrey JA Eur Radiol; 2024 Aug; 34(8):5056-5065. PubMed ID: 38217704 [TBL] [Abstract][Full Text] [Related]
6. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Lin YC; Lin CH; Lu HY; Chiang HJ; Wang HK; Huang YT; Ng SH; Hong JH; Yen TC; Lai CH; Lin G Eur Radiol; 2020 Mar; 30(3):1297-1305. PubMed ID: 31712961 [TBL] [Abstract][Full Text] [Related]
7. Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study. Chen H; Li S; Zhang Y; Liu L; Lv X; Yi Y; Ruan G; Ke C; Feng Y Eur Radiol; 2022 Oct; 32(10):7248-7259. PubMed ID: 35420299 [TBL] [Abstract][Full Text] [Related]
8. Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features. Yang L; Wang T; Zhang J; Kang S; Xu S; Wang K BMC Med Imaging; 2024 Mar; 24(1):56. PubMed ID: 38443817 [TBL] [Abstract][Full Text] [Related]
9. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning. Lin YC; Lin G; Pandey S; Yeh CH; Wang JJ; Lin CY; Ho TY; Ko SF; Ng SH Eur Radiol; 2023 Sep; 33(9):6548-6556. PubMed ID: 37338554 [TBL] [Abstract][Full Text] [Related]
10. Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features. Fujita S; Hagiwara A; Yasaka K; Akai H; Kunimatsu A; Kiryu S; Fukunaga I; Kato S; Akashi T; Kamagata K; Wada A; Abe O; Aoki S Eur Radiol; 2022 Jul; 32(7):4791-4800. PubMed ID: 35304637 [TBL] [Abstract][Full Text] [Related]
11. Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR. Vuong D; Tanadini-Lang S; Huellner MW; Veit-Haibach P; Unkelbach J; Andratschke N; Kraft J; Guckenberger M; Bogowicz M Med Phys; 2019 Apr; 46(4):1677-1685. PubMed ID: 30714158 [TBL] [Abstract][Full Text] [Related]
12. Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy. Dutta A; Chan J; Haworth A; Dubowitz DJ; Kneebone A; Reynolds HM Phys Imaging Radiat Oncol; 2024 Jan; 29():100530. PubMed ID: 38275002 [TBL] [Abstract][Full Text] [Related]
13. Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: Koçak B; Yüzkan S; Mutlu S; Karagülle M; Kala A; Kadıoğlu M; Solak S; Sunman Ş; Temiz ZH; Ganiyusufoğlu AK Diagn Interv Radiol; 2024 May; 30(3):152-162. PubMed ID: 38073244 [TBL] [Abstract][Full Text] [Related]
14. Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images. Pandey U; Saini J; Kumar M; Gupta R; Ingalhalikar M J Magn Reson Imaging; 2021 Feb; 53(2):394-407. PubMed ID: 32864820 [TBL] [Abstract][Full Text] [Related]
15. Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor. Gitto S; Cuocolo R; Giannetta V; Badalyan J; Di Luca F; Fusco S; Zantonelli G; Albano D; Messina C; Sconfienza LM J Imaging Inform Med; 2024 Jun; 37(3):1187-1200. PubMed ID: 38332405 [TBL] [Abstract][Full Text] [Related]
16. Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. Qiu Q; Duan J; Duan Z; Meng X; Ma C; Zhu J; Lu J; Liu T; Yin Y Quant Imaging Med Surg; 2019 Mar; 9(3):453-464. PubMed ID: 31032192 [TBL] [Abstract][Full Text] [Related]
17. Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study. Moribata Y; Kurata Y; Nishio M; Kido A; Otani S; Himoto Y; Nishio N; Furuta A; Onishi H; Masui K; Kobayashi T; Nakamoto Y Sci Rep; 2023 Jan; 13(1):628. PubMed ID: 36635425 [TBL] [Abstract][Full Text] [Related]