153 related articles for article (PubMed ID: 37672052)
1. Non-invasive differential diagnosis of teratomas from other intracranial germ cell tumours using MRI-based fractal and radiomic analyses.
Liu S; Wang X; Liu X; Li S; Liao H; Qiu X
Eur Radiol; 2024 Mar; 34(3):1434-1443. PubMed ID: 37672052
[TBL] [Abstract][Full Text] [Related]
2. T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.
Zheng HD; Huang QY; Huang QM; Ke XT; Ye K; Lin S; Xu JH
World J Gastrointest Oncol; 2024 Mar; 16(3):819-832. PubMed ID: 38577440
[TBL] [Abstract][Full Text] [Related]
3. Multiparametric Framework Magnetic Resonance Imaging Assessment of Subtypes of Intracranial Germ Cell Tumors Using Susceptibility Weighted Imaging, Diffusion-Weighted Imaging, and Dynamic Susceptibility-Contrast Perfusion-Weighted Imaging Combined With Conventional Magnetic Resonance Imaging.
Li Y; Wang P; Zhang J; Li J; Chen L; Qiu X
J Magn Reson Imaging; 2022 Oct; 56(4):1232-1242. PubMed ID: 35278008
[TBL] [Abstract][Full Text] [Related]
4. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
5. Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature.
Liu Q; Liu S; Mao Y; Kang X; Yu M; Chen G
Eur Radiol; 2024 Jan; ():. PubMed ID: 38206403
[TBL] [Abstract][Full Text] [Related]
6. Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.
Baessler B; Nestler T; Pinto Dos Santos D; Paffenholz P; Zeuch V; Pfister D; Maintz D; Heidenreich A
Eur Radiol; 2020 Apr; 30(4):2334-2345. PubMed ID: 31828413
[TBL] [Abstract][Full Text] [Related]
7. Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.
Liu J; Zeng P; Guo W; Wang C; Geng Y; Lang N; Yuan H
J Magn Reson Imaging; 2021 Oct; 54(4):1303-1311. PubMed ID: 33979466
[TBL] [Abstract][Full Text] [Related]
8. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma.
Wei Q; Yuan W; Jia Z; Chen J; Li L; Yan Z; Liao Y; Mao L; Hu S; Liu X; Chen W
Abdom Radiol (NY); 2023 Feb; 48(2):458-470. PubMed ID: 36460837
[TBL] [Abstract][Full Text] [Related]
9. MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.
Tsuchiya M; Masui T; Terauchi K; Yamada T; Katyayama M; Ichikawa S; Noda Y; Goshima S
Eur Radiol; 2022 Jun; 32(6):4090-4100. PubMed ID: 35044510
[TBL] [Abstract][Full Text] [Related]
10. Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment.
Bao D; Liu Z; Geng Y; Li L; Xu H; Zhang Y; Hu L; Zhao X; Zhao Y; Luo D
Cancer Imaging; 2022 Jan; 22(1):10. PubMed ID: 35090572
[TBL] [Abstract][Full Text] [Related]
11. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
[TBL] [Abstract][Full Text] [Related]
12. Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.
Jang K; Russo C; Di Ieva A
Neuroradiology; 2020 Jul; 62(7):771-790. PubMed ID: 32249351
[TBL] [Abstract][Full Text] [Related]
13. Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.
Jabal MS; Mohammed MA; Kobeissi H; Lanzino G; Brinjikji W; Flemming KD
J Stroke Cerebrovasc Dis; 2024 Jan; 33(1):107462. PubMed ID: 37931483
[TBL] [Abstract][Full Text] [Related]
14. Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images.
Liu S; Zhou Y; Wang C; Shen J; Zheng Y
BMC Med Imaging; 2023 Aug; 23(1):101. PubMed ID: 37528338
[TBL] [Abstract][Full Text] [Related]
15. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.
Sun R; Limkin EJ; Vakalopoulou M; Dercle L; Champiat S; Han SR; Verlingue L; Brandao D; Lancia A; Ammari S; Hollebecque A; Scoazec JY; Marabelle A; Massard C; Soria JC; Robert C; Paragios N; Deutsch E; Ferté C
Lancet Oncol; 2018 Sep; 19(9):1180-1191. PubMed ID: 30120041
[TBL] [Abstract][Full Text] [Related]
16. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
[TBL] [Abstract][Full Text] [Related]
18. MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.
Park CJ; Choi SH; Kim D; Kim SB; Han K; Ahn SS; Lee WH; Choi EC; Keum KC; Kim J
Eur Radiol; 2023 Nov; ():. PubMed ID: 37926740
[TBL] [Abstract][Full Text] [Related]
19. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging.
Roy S; Whitehead TD; Quirk JD; Salter A; Ademuyiwa FO; Li S; An H; Shoghi KI
EBioMedicine; 2020 Sep; 59():102963. PubMed ID: 32891051
[TBL] [Abstract][Full Text] [Related]
20. Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas.
Zhang M; Tam L; Wright J; Mohammadzadeh M; Han M; Chen E; Wagner M; Nemalka J; Lai H; Eghbal A; Ho CY; Lober RM; Cheshier SH; Vitanza NA; Grant GA; Prolo LM; Yeom KW; Jaju A
AJNR Am J Neuroradiol; 2022 Apr; 43(4):603-610. PubMed ID: 35361575
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
