169 related articles for article (PubMed ID: 32851505)
1. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas.
Park YW; Kang Y; Ahn SS; Ku CR; Kim EH; Kim SH; Lee EJ; Kim SH; Lee SK
Pituitary; 2020 Dec; 23(6):691-700. PubMed ID: 32851505
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Preoperative prediction of granulation pattern subtypes in GH-secreting pituitary adenomas.
Heng L; Liu X; Jia D; Guo W; Zhang S; Gao G; Gong L; Qu Y
Clin Endocrinol (Oxf); 2021 Jul; 95(1):134-142. PubMed ID: 33738801
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Clinicopathological significance of baseline T2-weighted signal intensity in functional pituitary adenomas.
Dogansen SC; Yalin GY; Tanrikulu S; Tekin S; Nizam N; Bilgic B; Sencer S; Yarman S
Pituitary; 2018 Aug; 21(4):347-354. PubMed ID: 29460202
[TBL] [Abstract][Full Text] [Related]
6. Quantitative analyses of T2-weighted MRI as a potential marker for response to somatostatin analogs in newly diagnosed acromegaly.
Heck A; Emblem KE; Casar-Borota O; Bollerslev J; Ringstad G
Endocrine; 2016 May; 52(2):333-43. PubMed ID: 26475495
[TBL] [Abstract][Full Text] [Related]
7. MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands.
Galm BP; Buckless C; Swearingen B; Torriani M; Klibanski A; Bredella MA; Tritos NA
Pituitary; 2020 Jun; 23(3):212-222. PubMed ID: 31897778
[TBL] [Abstract][Full Text] [Related]
8. Analysis of Diffusion-Weighted and T2-Weighted Imaging in the Prediction of Distinct Granulation Patterns of Somatotroph Adenomas.
Tang Y; Xie T; Guo Y; Liu S; Li C; Liu T; Zhao P; Yang L; Li Z; Yang H; Zhang X
World Neurosurg; 2024 Feb; 182():e334-e343. PubMed ID: 38052365
[TBL] [Abstract][Full Text] [Related]
9. Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes.
Liu CX; Heng LJ; Han Y; Wang SZ; Yan LF; Yu Y; Ren JL; Wang W; Hu YC; Cui GB
Front Oncol; 2021; 11():640375. PubMed ID: 34307124
[TBL] [Abstract][Full Text] [Related]
10. Percent reduction of growth hormone levels correlates closely with percent resected tumor volume in acromegaly.
Schwyzer L; Starke RM; Jane JA; Oldfield EH
J Neurosurg; 2015 Apr; 122(4):798-802. PubMed ID: 25423276
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. T2-weighted MRI signal predicts hormone and tumor responses to somatostatin analogs in acromegaly.
Potorac I; Petrossians P; Daly AF; Alexopoulou O; Borot S; Sahnoun-Fathallah M; Castinetti F; Devuyst F; Jaffrain-Rea ML; Briet C; Luca F; Lapoirie M; Zoicas F; Simoneau I; Diallo AM; Muhammad A; Kelestimur F; Nazzari E; Centeno RG; Webb SM; Nunes ML; Hana V; Pascal-Vigneron V; Ilovayskaya I; Nasybullina F; Achir S; Ferone D; Neggers SJ; Delemer B; Petit JM; Schöfl C; Raverot G; Goichot B; Rodien P; Corvilain B; Brue T; Schillo F; Tshibanda L; Maiter D; Bonneville JF; Beckers A
Endocr Relat Cancer; 2016 Nov; 23(11):871-881. PubMed ID: 27649724
[TBL] [Abstract][Full Text] [Related]
13. Prognostic Factors of Acromegalic Patients with Growth Hormone-Secreting Pituitary Adenoma After Transsphenoidal Surgery.
Taweesomboonyat C; Oearsakul T
World Neurosurg; 2021 Feb; 146():e1360-e1366. PubMed ID: 33309643
[TBL] [Abstract][Full Text] [Related]
14. Magnetic resonance spectroscopy may serve as a presurgical predictor of somatostatin analog therapy response in patients with growth hormone-secreting pituitary macroadenomas.
Hu J; Yan J; Zheng X; Zhang Y; Ran Q; Tang X; Shu T; Shen R; Duan L; Zhang D; Guo Q; Zhang W; Yang H; Li S
J Endocrinol Invest; 2019 Apr; 42(4):443-451. PubMed ID: 30171531
[TBL] [Abstract][Full Text] [Related]
15. Clinical, biological, radiological, and pathological comparison of sparsely and densely granulated somatotroph adenomas: a single center experience from a cohort of 131 patients with acromegaly.
Swanson AA; Erickson D; Donegan DM; Jenkins SM; Van Gompel JJ; Atkinson JLD; Erickson BJ; Giannini C
Pituitary; 2021 Apr; 24(2):192-206. PubMed ID: 33074402
[TBL] [Abstract][Full Text] [Related]
16. Clinicopathological features of growth hormone-producing pituitary adenomas: difference among various types defined by cytokeratin distribution pattern including a transitional form.
Obari A; Sano T; Ohyama K; Kudo E; Qian ZR; Yoneda A; Rayhan N; Mustafizur Rahman M; Yamada S
Endocr Pathol; 2008; 19(2):82-91. PubMed ID: 18629656
[TBL] [Abstract][Full Text] [Related]
17. Quantification of specific growth patterns and frequency of the empty sella phenomenon in growth hormone-secreting pituitary adenomas.
Bier G; Hempel JM; Grimm F; Ernemann U; Bender B; Honegger J
Eur J Radiol; 2018 Jul; 104():79-86. PubMed ID: 29857870
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.
Kim JY; Park JE; Jo Y; Shim WH; Nam SJ; Kim JH; Yoo RE; Choi SH; Kim HS
Neuro Oncol; 2019 Feb; 21(3):404-414. PubMed ID: 30107606
[TBL] [Abstract][Full Text] [Related]
20. Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas.
Baysal B; Eser MB; Dogan MB; Kursun MA
Medeni Med J; 2022 Mar; 37(1):36-43. PubMed ID: 35306784
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]