BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

415 related articles for article (PubMed ID: 32087466)

  • 1. A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.
    Peng A; Dai H; Duan H; Chen Y; Huang J; Zhou L; Chen L
    Eur J Radiol; 2020 Apr; 125():108892. PubMed ID: 32087466
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery.
    Zhang S; Song G; Zang Y; Jia J; Wang C; Li C; Tian J; Dong D; Zhang Y
    Eur Radiol; 2018 Sep; 28(9):3692-3701. PubMed ID: 29572634
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.
    Cuocolo R; Ugga L; Solari D; Corvino S; D'Amico A; Russo D; Cappabianca P; Cavallo LM; Elefante A
    Neuroradiology; 2020 Dec; 62(12):1649-1656. PubMed ID: 32705290
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma.
    Fan Y; Liu Z; Hou B; Li L; Liu X; Liu Z; Wang R; Lin Y; Feng F; Tian J; Feng M
    Eur J Radiol; 2019 Dec; 121():108647. PubMed ID: 31561943
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.
    Niu J; Zhang S; Ma S; Diao J; Zhou W; Tian J; Zang Y; Jia W
    Eur Radiol; 2019 Mar; 29(3):1625-1634. PubMed ID: 30255254
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.
    Ugga L; Cuocolo R; Solari D; Guadagno E; D'Amico A; Somma T; Cappabianca P; Del Basso de Caro ML; Cavallo LM; Brunetti A
    Neuroradiology; 2019 Dec; 61(12):1365-1373. PubMed ID: 31375883
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst.
    Wang Y; Chen S; Shi F; Cheng X; Xu Q; Li J; Luo S; Jiang P; Wei Y; Zhou C; Zheng L; Xia K; Lu G; Zhang Z
    Comput Math Methods Med; 2021; 2021():6438861. PubMed ID: 34422095
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI.
    Zeynalova A; Kocak B; Durmaz ES; Comunoglu N; Ozcan K; Ozcan G; Turk O; Tanriover N; Kocer N; Kizilkilic O; Islak C
    Neuroradiology; 2019 Jul; 61(7):767-774. PubMed ID: 31011772
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency.
    Mendi BAR; Batur H; Çay N; Çakır BT
    Acta Radiol; 2023 Aug; 64(8):2470-2478. PubMed ID: 37170546
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics.
    Liu CX; Wang SZ; Heng LJ; Han Y; Ma YH; Yan LF; Yu Y; Wang W; Hu YC; Cui GB
    J Comput Assist Tomogr; 2022 Jan-Feb 01; 46(1):124-130. PubMed ID: 35099144
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.
    Huang ZS; Xiao X; Li XD; Mo HZ; He WL; Deng YH; Lu LJ; Wu YK; Liu H
    J Magn Reson Imaging; 2021 Nov; 54(5):1541-1550. PubMed ID: 34085336
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Application of Contrast-Enhanced 3-Dimensional T2-Weighted Volume Isotropic Turbo Spin Echo Acquisition Sequence in the Diagnosis of Prolactin-Secreting Pituitary Microadenomas.
    Guo R; Wu Y; Guo G; Zhou H; Liu S; Yao Z; Xiao Y
    J Comput Assist Tomogr; 2022 Jan-Feb 01; 46(1):116-123. PubMed ID: 35099143
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.
    Kocak B; Durmaz ES; Kadioglu P; Polat Korkmaz O; Comunoglu N; Tanriover N; Kocer N; Islak C; Kizilkilic O
    Eur Radiol; 2019 Jun; 29(6):2731-2739. PubMed ID: 30506213
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI.
    Zhang Y; Zheng J; Huang Z; Teng Y; Chen C; Xu J
    Eur Radiol; 2023 Nov; 33(11):7482-7493. PubMed ID: 37488296
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.
    Cepeda S; Arrese I; García-García S; Velasco-Casares M; Escudero-Caro T; Zamora T; Sarabia R
    World Neurosurg; 2021 Feb; 146():e1147-e1159. PubMed ID: 33259973
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.
    He L; Li H; Dudley JA; Maloney TC; Brady SL; Somasundaram E; Trout AT; Dillman JR
    AJR Am J Roentgenol; 2019 Sep; 213(3):592-601. PubMed ID: 31120779
    [No Abstract]   [Full Text] [Related]  

  • 17. Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas.
    Rui W; Qiao N; Wu Y; Zhang Y; Aili A; Zhang Z; Ye H; Wang Y; Zhao Y; Yao Z
    Eur Radiol; 2022 Mar; 32(3):1570-1578. PubMed ID: 34837512
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas.
    Wang X; Dai Y; Lin H; Cheng J; Zhang Y; Cao M; Zhou Y
    Eur Radiol; 2023 May; 33(5):3312-3321. PubMed ID: 36738323
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data.
    Liu RX; Li H; Towbin AJ; Ata NA; Smith EA; Tkach JA; Denson LA; He L; Dillman JR
    AJR Am J Roentgenol; 2024 Jan; 222(1):e2329812. PubMed ID: 37530398
    [No Abstract]   [Full Text] [Related]  

  • 20. Pituitary adenoma consistency: Direct correlation of ultrahigh field 7T MRI with histopathological analysis.
    Yao A; Rutland JW; Verma G; Banihashemi A; Padormo F; Tsankova NM; Delman BN; Shrivastava RK; Balchandani P
    Eur J Radiol; 2020 May; 126():108931. PubMed ID: 32146344
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.