65 related articles for article (PubMed ID: 33713414)
1. Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma.
Park YW; Eom J; Kim S; Kim H; Ahn SS; Ku CR; Kim EH; Lee EJ; Kim SH; Lee SK
J Clin Endocrinol Metab; 2021 Jul; 106(8):e3069-e3077. PubMed ID: 33713414
[TBL] [Abstract][Full Text] [Related]
2. Prolactinoma extension as a contributing factor in dopamine agonist-induced CSF rhinorrhea: a systematic review of the literature.
Milton CK; Lee BJ; Voronovich ZA; Conner AK; McKinney KA; El Rassi ET; Lim J; Glenn CA
Br J Neurosurg; 2023 Oct; 37(5):976-981. PubMed ID: 33783287
[TBL] [Abstract][Full Text] [Related]
3. Comparison and analysis of multiple machine learning models for discriminating benign and malignant testicular lesions based on magnetic resonance imaging radiomics.
Feng Y; Feng Z; Wang L; Lv W; Liu Z; Min X; Li J; Zhang J
Front Med (Lausanne); 2023; 10():1279622. PubMed ID: 38188340
[TBL] [Abstract][Full Text] [Related]
4. Commentary: Clinical characteristics of male prolactinoma patients mainly presenting with severe obesity and the metabolic response to dopamine agonist therapy.
Andereggen L; Christ E
Front Endocrinol (Lausanne); 2024; 15():1371468. PubMed ID: 38510701
[No Abstract] [Full Text] [Related]
5. Early Prognostication of Critical Patients With Spinal Cord Injury: A Machine Learning Study With 1485 Cases.
Fan G; Liu H; Yang S; Luo L; Pang M; Liu B; Zhang L; Han L; Rong L; Liao X
Spine (Phila Pa 1976); 2024 Jun; 49(11):754-762. PubMed ID: 37921018
[TBL] [Abstract][Full Text] [Related]
6. Automated selection of abdominal MRI series using a DICOM metadata classifier and selective use of a pixel-based classifier.
Miller CM; Zhu Z; Mazurowski MA; Bashir MR; Wiggins WF
Abdom Radiol (NY); 2024 Jun; ():. PubMed ID: 38860997
[TBL] [Abstract][Full Text] [Related]
7. Between a rock and a hard place: the role of DA induced tumor fibrosis in prolactinoma management.
Mamelak AN
J Clin Endocrinol Metab; 2024 Feb; ():. PubMed ID: 38380908
[No Abstract] [Full Text] [Related]
8. Correction: Aromatase inhibitors as a therapeutic strategy for male prolactinoma resistant to dopamine agonists: a retrospective cohort study and literature review.
Zúñiga D; Stumpf MAM; Monteiro ALS; Glezer A
J Endocrinol Invest; 2024 May; 47(5):1313. PubMed ID: 38079105
[No Abstract] [Full Text] [Related]
9. Revisiting the Role of Insulin-like Growth Factor-1 Measurement After Surgical Treatment of Acromegaly.
Jung IH; Choi S; Ku CR; Lee SG; Lee EJ; Kim SH; Kim EH
J Clin Endocrinol Metab; 2021 Jun; 106(7):e2589-e2599. PubMed ID: 33738470
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Imaging of Bone Sarcomas and Soft-Tissue Sarcomas.
Igrec J; Fuchsjäger MH
Rofo; 2021 Oct; 193(10):1171-1182. PubMed ID: 33772487
[TBL] [Abstract][Full Text] [Related]
12. Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.
Chen BT; Jin T; Ye N; Mambetsariev I; Wang T; Wong CW; Chen Z; Rockne RC; Colen RR; Holodny AI; Sampath S; Salgia R
Front Oncol; 2021; 11():621088. PubMed ID: 33747933
[No Abstract] [Full Text] [Related]
13. Imaging cerebral microbleeds in Cushing's disease evaluated by quantitative susceptibility mapping: an observational cross-sectional study.
Jiang H; Yang W; Sun Y; Yan F; Sun Q; Wei H; Bian LG
Eur J Endocrinol; 2021 Apr; 184(4):565-574. PubMed ID: 33730688
[TBL] [Abstract][Full Text] [Related]
14. A deep survival interpretable radiomics model of hepatocellular carcinoma patients.
Wei L; Owen D; Rosen B; Guo X; Cuneo K; Lawrence TS; Ten Haken R; El Naqa I
Phys Med; 2021 Feb; 82():295-305. PubMed ID: 33714190
[TBL] [Abstract][Full Text] [Related]
15. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.
Chan L; Nadkarni GN; Fleming F; McCullough JR; Connolly P; Mosoyan G; El Salem F; Kattan MW; Vassalotti JA; Murphy B; Donovan MJ; Coca SG; Damrauer SM
Diabetologia; 2021 Jul; 64(7):1504-1515. PubMed ID: 33797560
[TBL] [Abstract][Full Text] [Related]
16. Case Report and Literature Review: Ectopic Thyrotropin-Secreting Pituitary Adenoma in the Suprasellar Region.
Li X; Zhao B; Hou B; Wang J; Zhu J; Yao Y; Lian X
Front Endocrinol (Lausanne); 2021; 12():619161. PubMed ID: 33776916
[TBL] [Abstract][Full Text] [Related]
17. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach.
Kim JP; Kim J; Jang H; Kim J; Kang SH; Kim JS; Lee J; Na DL; Kim HJ; Seo SW; Park H
Sci Rep; 2021 Mar; 11(1):6954. PubMed ID: 33772041
[TBL] [Abstract][Full Text] [Related]
18. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.
Papadimitroulas P; Brocki L; Christopher Chung N; Marchadour W; Vermet F; Gaubert L; Eleftheriadis V; Plachouris D; Visvikis D; Kagadis GC; Hatt M
Phys Med; 2021 Mar; 83():108-121. PubMed ID: 33765601
[TBL] [Abstract][Full Text] [Related]
19. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma.
Chang C; Sun X; Wang G; Yu H; Zhao W; Ge Y; Duan S; Qian X; Wang R; Lei B; Wang L; Liu L; Ruan M; Yan H; Liu C; Chen J; Xie W
Front Oncol; 2021; 11():603882. PubMed ID: 33738250
[TBL] [Abstract][Full Text] [Related]
20. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI.
Le NQK; Hung TNK; Do DT; Lam LHT; Dang LH; Huynh TT
Comput Biol Med; 2021 May; 132():104320. PubMed ID: 33735760
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]