151 related articles for article (PubMed ID: 37931483)
1. 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]
2. Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.
Li X; Jones P; Zhao M
Sci Rep; 2024 May; 14(1):11022. PubMed ID: 38745042
[TBL] [Abstract][Full Text] [Related]
3. Radiomics-based Prediction of Re-hemorrhage in Cerebral Cavernous Malformation after Gamma Knife Radiosurgery.
Kuo PH; Lee CC; Lu CF
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3668-3671. PubMed ID: 34892032
[TBL] [Abstract][Full Text] [Related]
4. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.
Abdollahi H; Mofid B; Shiri I; Razzaghdoust A; Saadipoor A; Mahdavi A; Galandooz HM; Mahdavi SR
Radiol Med; 2019 Jun; 124(6):555-567. PubMed ID: 30607868
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting.
Sakai Y; Yang C; Kihira S; Tsankova N; Khan F; Hormigo A; Lai A; Cloughesy T; Nael K
Int J Mol Sci; 2020 Oct; 21(21):. PubMed ID: 33121211
[TBL] [Abstract][Full Text] [Related]
7. Morbidity After Symptomatic Hemorrhage of Cerebral Cavernous Malformation: A Nomogram Approach to Risk Assessment.
Ma L; Zhang S; Li Z; Wu CX; Wang Z; Zhan L; Hao Q; Wang H; Ye X; Chen X; Liu YO; Wang S; Zhao YL
Stroke; 2020 Oct; 51(10):2997-3006. PubMed ID: 32951540
[TBL] [Abstract][Full Text] [Related]
8. Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification.
Shu X; Liu Y; Qiao X; Ai G; Liu L; Liao J; Deng Z; He X
Br J Radiol; 2023 Mar; 96(1143):20220238. PubMed ID: 36475858
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.
Kandemirli SG; Kocak B; Naganawa S; Ozturk K; Yip SSF; Chopra S; Rivetti L; Aldine AS; Jones K; Cayci Z; Moritani T; Sato TS
World Neurosurg; 2021 Jul; 151():e78-e85. PubMed ID: 33819703
[TBL] [Abstract][Full Text] [Related]
11. A Roadmap for Developing Plasma Diagnostic and Prognostic Biomarkers of Cerebral Cavernous Angioma With Symptomatic Hemorrhage (CASH).
Girard R; Li Y; Stadnik A; Shenkar R; Hobson N; Romanos S; Srinath A; Moore T; Lightle R; Shkoukani A; Akers A; Carroll T; Christoforidis GA; Koenig JI; Lee C; Piedad K; Greenberg SM; Kim H; Flemming KD; Ji Y; Awad IA
Neurosurgery; 2021 Feb; 88(3):686-697. PubMed ID: 33469662
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events.
Durmaz ES; Karabacak M; Ozkara BB; Kargın OA; Raimoglu U; Tokdil H; Durmaz E; Adaletli I
Eur Radiol; 2023 Jul; 33(7):4611-4620. PubMed ID: 36633675
[TBL] [Abstract][Full Text] [Related]
14. An investigation of machine learning methods in delta-radiomics feature analysis.
Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF
PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910
[TBL] [Abstract][Full Text] [Related]
15. Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning.
Liu J; Tao W; Wang Z; Chen X; Wu B; Liu M
Ther Adv Neurol Disord; 2021; 14():17562864211060029. PubMed ID: 35173809
[TBL] [Abstract][Full Text] [Related]
16. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.
Shiri I; Sorouri M; Geramifar P; Nazari M; Abdollahi M; Salimi Y; Khosravi B; Askari D; Aghaghazvini L; Hajianfar G; Kasaeian A; Abdollahi H; Arabi H; Rahmim A; Radmard AR; Zaidi H
Comput Biol Med; 2021 May; 132():104304. PubMed ID: 33691201
[TBL] [Abstract][Full Text] [Related]
17. Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics.
Yu H; Wang Z; Sun Y; Bo W; Duan K; Song C; Hu Y; Zhou J; Mu Z; Wu N
Front Psychiatry; 2022; 13():1105496. PubMed ID: 36699499
[TBL] [Abstract][Full Text] [Related]
18. Reliable? The Value of Early Postoperative Magnetic Resonance Imaging after Cerebral Cavernous Malformation Surgery.
Chen B; Göricke S; Wrede K; Jabbarli R; Wälchli T; Jägersberg M; Sure U; Dammann P
World Neurosurg; 2017 Jul; 103():138-144. PubMed ID: 28391022
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.
Jaberipour M; Soliman H; Sahgal A; Sadeghi-Naini A
Sci Rep; 2021 Nov; 11(1):21620. PubMed ID: 34732781
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
