203 related articles for article (PubMed ID: 37505244)
61. A matched-pair analysis comparing stereotactic radiosurgery with whole-brain radiotherapy for patients with multiple brain metastases.
El Shafie RA; Celik A; Weber D; Schmitt D; Lang K; König L; Bernhardt D; Höne S; Forster T; von Nettelbladt B; Adeberg S; Debus J; Rieken S
J Neurooncol; 2020 May; 147(3):607-618. PubMed ID: 32239433
[TBL] [Abstract][Full Text] [Related]
62. CT-Based Radiomics Signature With Machine Learning Predicts MYCN Amplification in Pediatric Abdominal Neuroblastoma.
Chen X; Wang H; Huang K; Liu H; Ding H; Zhang L; Zhang T; Yu W; He L
Front Oncol; 2021; 11():687884. PubMed ID: 34109133
[TBL] [Abstract][Full Text] [Related]
63. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer.
Ying M; Pan J; Lu G; Zhou S; Fu J; Wang Q; Wang L; Hu B; Wei Y; Shen J
BMC Cancer; 2022 May; 22(1):524. PubMed ID: 35534797
[TBL] [Abstract][Full Text] [Related]
64. Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.
Peng Y; Wang Y; Wen Z; Xiang H; Guo L; Su L; He Y; Pang H; Zhou P; Zhan X
Front Neurol; 2024; 15():1321923. PubMed ID: 38327618
[TBL] [Abstract][Full Text] [Related]
65. Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: A multi-dataset study.
Song F; Song X; Feng Y; Fan G; Sun Y; Zhang P; Li J; Liu F; Zhang G
Med Phys; 2023 Jul; 50(7):4351-4365. PubMed ID: 36682051
[TBL] [Abstract][Full Text] [Related]
66. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer.
Cui Y; Liu H; Ren J; Du X; Xin L; Li D; Yang X; Wang D
Eur Radiol; 2020 Apr; 30(4):1948-1958. PubMed ID: 31942672
[TBL] [Abstract][Full Text] [Related]
67. Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.
Zhou Z; Sanders JW; Johnson JM; Gule-Monroe MK; Chen MM; Briere TM; Wang Y; Son JB; Pagel MD; Li J; Ma J
Radiology; 2020 May; 295(2):407-415. PubMed ID: 32181729
[TBL] [Abstract][Full Text] [Related]
68. Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma.
Xu A; Chu X; Zhang S; Zheng J; Shi D; Lv S; Li F; Weng X
BMC Cancer; 2022 Aug; 22(1):872. PubMed ID: 35945526
[TBL] [Abstract][Full Text] [Related]
69. Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?
Cai L; Sidey-Gibbons C; Nees J; Riedel F; Schäfgen B; Togawa R; Killinger K; Heil J; Pfob A; Golatta M
Eur Radiol; 2024 Apr; 34(4):2560-2573. PubMed ID: 37707548
[TBL] [Abstract][Full Text] [Related]
70. Prognostic grading system specifically for elderly patients with brain metastases after stereotactic radiosurgery: a 2-institution study.
Yamamoto M; Serizawa T; Higuchi Y; Nagano O; Aiyama H; Koiso T; Watanabe S; Kawabe T; Sato Y; Kasuya H
J Neurosurg; 2018 Dec; 129(Suppl1):95-102. PubMed ID: 30544299
[TBL] [Abstract][Full Text] [Related]
71. CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer.
Sun F; Chen Y; Chen X; Sun X; Xing L
Eur J Radiol; 2021 Jan; 134():109411. PubMed ID: 33246270
[TBL] [Abstract][Full Text] [Related]
72. 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]
73. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.
Xin Z; Yan W; Feng Y; Yunzhi L; Zhang Y; Wang D; Chen W; Peng J; Guo C; Chen Z; Wang X; Zhu J; Lei J
Cancer Med; 2023 Oct; 12(19):19383-19393. PubMed ID: 37772478
[TBL] [Abstract][Full Text] [Related]
74. Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.
Wu A; Wu C; Zeng Q; Cao Y; Shu X; Luo L; Feng Z; Tu Y; Jie Z; Zhu Y; Zhou F; Huang Y; Li Z
Sci Rep; 2023 May; 13(1):8442. PubMed ID: 37231100
[TBL] [Abstract][Full Text] [Related]
75. Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment.
Luo Y; Chen H; Gui M
Diagnostics (Basel); 2023 Jul; 13(15):. PubMed ID: 37568874
[TBL] [Abstract][Full Text] [Related]
76. A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19.
Chen H; Zeng M; Wang X; Su L; Xia Y; Yang Q; Liu D
Br J Radiol; 2021 Jan; 94(1117):20200634. PubMed ID: 33296222
[TBL] [Abstract][Full Text] [Related]
77. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival.
Zhang X; Lu H; Tian Q; Feng N; Yin L; Xu X; Du P; Liu Y
Eur Radiol; 2019 Oct; 29(10):5528-5538. PubMed ID: 30847586
[TBL] [Abstract][Full Text] [Related]
78. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.
Chen H; Zhang X; Wang X; Quan X; Deng Y; Lu M; Wei Q; Ye Q; Zhou Q; Xiang Z; Liang C; Yang W; Zhao Y
Eur Radiol; 2021 Oct; 31(10):7913-7924. PubMed ID: 33825032
[TBL] [Abstract][Full Text] [Related]
79. Machine learning-based ensemble prediction model for the gamma passing rate of VMAT-SBRT plan.
Sun W; Mo Z; Li Y; Xiao J; Jia L; Huang S; Liao C; Du J; He S; Chen L; Zhang W; Yang X
Phys Med; 2024 Jan; 117():103204. PubMed ID: 38154373
[TBL] [Abstract][Full Text] [Related]
80. Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.
Russo G; Stefano A; Alongi P; Comelli A; Catalfamo B; Mantarro C; Longo C; Altieri R; Certo F; Cosentino S; Sabini MG; Richiusa S; Barbagallo GMV; Ippolito M
Curr Oncol; 2021 Dec; 28(6):5318-5331. PubMed ID: 34940083
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]