151 related articles for article (PubMed ID: 37720416)
21. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.
He T; Fong JN; Moore LW; Ezeana CF; Victor D; Divatia M; Vasquez M; Ghobrial RM; Wong STC
Comput Med Imaging Graph; 2021 Apr; 89():101894. PubMed ID: 33725579
[TBL] [Abstract][Full Text] [Related]
22. Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease.
Sim KC; Kim MJ; Cho Y; Kim HJ; Park BJ; Sung DJ; Han YE; Han NY; Kim TH; Lee YJ
J Comput Assist Tomogr; 2022 Jul-Aug 01; 46(4):505-513. PubMed ID: 35483092
[TBL] [Abstract][Full Text] [Related]
23. Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study.
Lysdahlgaard S
Radiography (Lond); 2022 Aug; 28(3):718-724. PubMed ID: 35428570
[TBL] [Abstract][Full Text] [Related]
24. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
[TBL] [Abstract][Full Text] [Related]
25. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.
Mokrane FZ; Lu L; Vavasseur A; Otal P; Peron JM; Luk L; Yang H; Ammari S; Saenger Y; Rousseau H; Zhao B; Schwartz LH; Dercle L
Eur Radiol; 2020 Jan; 30(1):558-570. PubMed ID: 31444598
[TBL] [Abstract][Full Text] [Related]
26. Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease.
Zhou Z; Xiong Z; Cheng R; Luo Q; Li Y; Xie Q; Xiao P; Hu D; Hu X; Shen Y; Li Z
Eur Radiol; 2023 Mar; 33(3):1862-1872. PubMed ID: 36255487
[TBL] [Abstract][Full Text] [Related]
27. Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features.
Tan X; Yang X; Hu S; Ge Y; Wu Q; Wang J; Sun Z
J Xray Sci Technol; 2023; 31(1):49-61. PubMed ID: 36314190
[TBL] [Abstract][Full Text] [Related]
28. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.
Xu F; Zhu W; Shen Y; Wang J; Xu R; Qutesh C; Song L; Gan Y; Pu C; Hu H
Front Oncol; 2020; 10():872. PubMed ID: 32850301
[No Abstract] [Full Text] [Related]
29. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation.
Guo D; Gu D; Wang H; Wei J; Wang Z; Hao X; Ji Q; Cao S; Song Z; Jiang J; Shen Z; Tian J; Zheng H
Eur J Radiol; 2019 Aug; 117():33-40. PubMed ID: 31307650
[TBL] [Abstract][Full Text] [Related]
30. Post-transplant hepatic fibrosis in pediatric liver-small bowel transplant recipients: A single-center, retrospective, observational study.
Vo HD; Radio SJ; Reyes-Santiago EM; Mauch TJ
Pediatr Transplant; 2021 Aug; 25(5):e13965. PubMed ID: 33378567
[TBL] [Abstract][Full Text] [Related]
31. Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.
Zhao R; Zhao H; Ge YQ; Zhou FF; Wang LS; Yu HZ; Gong XJ
Can J Gastroenterol Hepatol; 2022; 2022():2249447. PubMed ID: 35775068
[TBL] [Abstract][Full Text] [Related]
32. Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.
Hu R; Chen I; Peoples J; Salameh JP; Gönen M; Romesser PB; Simpson AL; Reyngold M
Phys Imaging Radiat Oncol; 2022 Oct; 24():36-42. PubMed ID: 36148155
[TBL] [Abstract][Full Text] [Related]
33. Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months.
Rizzo S; Botta F; Raimondi S; Origgi D; Buscarino V; Colarieti A; Tomao F; Aletti G; Zanagnolo V; Del Grande M; Colombo N; Bellomi M
Eur Radiol; 2018 Nov; 28(11):4849-4859. PubMed ID: 29737390
[TBL] [Abstract][Full Text] [Related]
34. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?
Wang X; Zhao X; Li Q; Xia W; Peng Z; Zhang R; Li Q; Jian J; Wang W; Tang Y; Liu S; Gao X
Eur Radiol; 2019 Nov; 29(11):6049-6058. PubMed ID: 30887209
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer.
Lee H; Moon SH; Hong JY; Lee J; Hyun SH
Cancers (Basel); 2023 Jul; 15(15):. PubMed ID: 37568657
[TBL] [Abstract][Full Text] [Related]
37. Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer.
Huang YC; Tsai YS; Li CI; Chan RH; Yeh YM; Chen PC; Shen MR; Lin PC
Cancers (Basel); 2022 Apr; 14(8):. PubMed ID: 35454802
[TBL] [Abstract][Full Text] [Related]
38. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification.
Nitsch J; Sack J; Halle MW; Moltz JH; Wall A; Rutherford AE; Kikinis R; Meine H
Int J Comput Assist Radiol Surg; 2021 Mar; 16(3):457-466. PubMed ID: 33646521
[TBL] [Abstract][Full Text] [Related]
39. Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.
Dong X; Dan X; Yawen A; Haibo X; Huan L; Mengqi T; Linglong C; Zhao R
Thorac Cancer; 2020 Sep; 11(9):2650-2659. PubMed ID: 32767522
[TBL] [Abstract][Full Text] [Related]
40. Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study.
An P; Li X; Qin P; Ye Y; Zhang J; Guo H; Duan P; He Z; Song P; Li M; Wang J; Hu Y; Feng G; Lin Y
Math Biosci Eng; 2023 Feb; 20(4):6612-6629. PubMed ID: 37161120
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
