162 related articles for article (PubMed ID: 36877288)
1. Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT.
Bernatz S; Koch V; Dos Santos DP; Ackermann J; Grünewald LD; Weitkamp I; Yel I; Martin SS; Lenga L; Scholtz JE; Vogl TJ; Mahmoudi S
Int J Comput Assist Radiol Surg; 2023 Oct; 18(10):1829-1839. PubMed ID: 36877288
[TBL] [Abstract][Full Text] [Related]
2. Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?
Mahmoudi S; Koch V; Santos DPD; Ackermann J; Grünewald LD; Weitkamp I; Yel I; Martin SS; Albrecht MH; Scholtz JE; Vogl TJ; Bernatz S
Eur J Radiol Open; 2023; 10():100459. PubMed ID: 36561422
[TBL] [Abstract][Full Text] [Related]
3. Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation.
Bernatz S; Böth I; Ackermann J; Burck I; Mahmoudi S; Lenga L; Martin SS; Scholtz JE; Koch V; Grünewald LD; Koch I; Stöver T; Wild PJ; Winkelmann R; Vogl TJ; Pinto Dos Santos D
J Comput Assist Tomogr; 2024 Mar-Apr 01; 48(2):323-333. PubMed ID: 38013237
[TBL] [Abstract][Full Text] [Related]
4. A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer.
Wang D; Zhuang Z; Wu S; Chen J; Fan X; Liu M; Zhu H; Wang M; Zou J; Zhou Q; Zhou P; Xue J; Meng X; Ju S; Zhang L
Front Oncol; 2022; 12():846840. PubMed ID: 35747803
[TBL] [Abstract][Full Text] [Related]
5. Dual-energy CT-based iodine quantification to differentiate abdominal malignant lymphoma from lymph node metastasis.
Martin SS; Czwikla R; Wichmann JL; Albrecht MH; Lenga L; Savage RH; Arendt C; Hammerstingl R; Vogl TJ; Kaltenbach B
Eur J Radiol; 2018 Aug; 105():255-260. PubMed ID: 30017291
[TBL] [Abstract][Full Text] [Related]
6. Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions.
Homayounieh F; Singh R; Nitiwarangkul C; Lades F; Schmidt B; Sedlmair M; Saini S; Kalra MK
AJR Am J Roentgenol; 2020 Aug; 215(2):398-405. PubMed ID: 32406776
[No Abstract] [Full Text] [Related]
7. Characterization of Benign and Malignant Pancreatic Lesions with DECT Quantitative Metrics and Radiomics.
Ebrahimian S; Singh R; Netaji A; Madhusudhan KS; Homayounieh F; Primak A; Lades F; Saini S; Kalra MK; Sharma S
Acad Radiol; 2022 May; 29(5):705-713. PubMed ID: 34412944
[TBL] [Abstract][Full Text] [Related]
8. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.
Li J; Dong D; Fang M; Wang R; Tian J; Li H; Gao J
Eur Radiol; 2020 Apr; 30(4):2324-2333. PubMed ID: 31953668
[TBL] [Abstract][Full Text] [Related]
9. Computed Tomography Radiomics to Differentiate Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma.
Mahmoudi S; Bernatz S; Ackermann J; Koch V; Dos Santos DP; Grünewald LD; Yel I; Martin SS; Scholtz JE; Stehle A; Walter D; Zeuzem S; Wild PJ; Vogl TJ; Kinzler MN
Clin Oncol (R Coll Radiol); 2023 May; 35(5):e312-e318. PubMed ID: 36804153
[TBL] [Abstract][Full Text] [Related]
10. Radiomics Features Using Dual-energy CT for Lymph Nodes After Preoperative Chemotherapy for Esophageal Cancer.
Tanaka Y; Sato Y; Horaguchi T; Fujibayashi S; Yasufuku I; Miyoshi T; Kaga T; Noda Y; Watanabe D; Ishihara T; Matsuo M; Matsuhashi N
Anticancer Res; 2024 Apr; 44(4):1661-1674. PubMed ID: 38537992
[TBL] [Abstract][Full Text] [Related]
11. Multiparametric Evaluation of Radiomics Features and Dual-Energy CT Iodine Maps for Discrimination and Outcome Prediction of Thymic Masses.
Mahmoudi S; Gruenewald LD; Eichler K; Althoff FC; Martin SS; Bernatz S; Booz C; Yel I; Kinzler MN; Ziegengeist NS; Torgashov K; Mohammed H; Geyer T; Scholtz JE; Hammerstingl RM; Weber C; Hardt SE; Sommer CM; Gruber-Rouh T; Leistner DM; Vogl TJ; Koch V
Acad Radiol; 2023 Dec; 30(12):3010-3021. PubMed ID: 37105804
[TBL] [Abstract][Full Text] [Related]
12. Radiomics analysis of dual-energy CT-derived iodine maps for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer.
Zhou Y; Su GY; Hu H; Ge YQ; Si Y; Shen MP; Xu XQ; Wu FY
Eur Radiol; 2020 Nov; 30(11):6251-6262. PubMed ID: 32500193
[TBL] [Abstract][Full Text] [Related]
13. Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT.
Chen Y; Zhong J; Wang L; Shi X; Lu W; Li J; Feng J; Xia Y; Chang R; Fan J; Chen L; Zhu Y; Yan F; Yao W; Zhang H
Eur Radiol; 2022 Aug; 32(8):5480-5490. PubMed ID: 35192011
[TBL] [Abstract][Full Text] [Related]
14. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.
Zhong J; Xia Y; Chen Y; Li J; Lu W; Shi X; Feng J; Yan F; Yao W; Zhang H
Eur Radiol; 2023 Feb; 33(2):812-824. PubMed ID: 36197579
[TBL] [Abstract][Full Text] [Related]
15. Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.
Li M; Fan Y; You H; Li C; Luo M; Zhou J; Li A; Zhang L; Yu X; Deng W; Zhou J; Zhang D; Zhang Z; Chen H; Xiao Y; Huang B; Wang J
Radiology; 2023 Aug; 308(2):e230255. PubMed ID: 37606573
[TBL] [Abstract][Full Text] [Related]
16. Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer.
Wu J; Zhang Q; Zhao Y; Liu Y; Chen A; Li X; Wu T; Li J; Guo Y; Liu A
Front Oncol; 2019; 9():1250. PubMed ID: 31824843
[No Abstract] [Full Text] [Related]
17. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.
Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A
Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180
[TBL] [Abstract][Full Text] [Related]
18. A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.
Brendlin AS; Peisen F; Almansour H; Afat S; Eigentler T; Amaral T; Faby S; Calvarons AF; Nikolaou K; Othman AE
J Immunother Cancer; 2021 Nov; 9(11):. PubMed ID: 34795006
[TBL] [Abstract][Full Text] [Related]
19. Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy.
Seidler M; Forghani B; Reinhold C; Pérez-Lara A; Romero-Sanchez G; Muthukrishnan N; Wichmann JL; Melki G; Yu E; Forghani R
Comput Struct Biotechnol J; 2019; 17():1009-1015. PubMed ID: 31406557
[TBL] [Abstract][Full Text] [Related]
20. Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings.
Nagano H; Takumi K; Nakajo M; Fukukura Y; Kumagae Y; Jinguji M; Tani A; Yoshiura T
AJR Am J Roentgenol; 2022 Jan; 218(1):66-74. PubMed ID: 34319164
[No Abstract] [Full Text] [Related]
[Next] [New Search]
