1377 related articles for article (PubMed ID: 32696256)
21. Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure.
Zhu WS; Shi SY; Yang ZH; Song C; Shen J
World J Gastroenterol; 2020 Mar; 26(11):1208-1220. PubMed ID: 32231424
[TBL] [Abstract][Full Text] [Related]
22. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.
Mao B; Ma J; Duan S; Xia Y; Tao Y; Zhang L
Eur Radiol; 2021 Jul; 31(7):4576-4586. PubMed ID: 33447862
[TBL] [Abstract][Full Text] [Related]
23. A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.
Deng X; Liao Z
Clin Radiol; 2024 Jun; 79(6):e817-e825. PubMed ID: 38413354
[TBL] [Abstract][Full Text] [Related]
24. [Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images].
Gao F; Yan B; Zeng L; Wu M; Tan H; Hai J; Ning P; Shi D
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2019 Aug; 36(4):581-589. PubMed ID: 31441258
[TBL] [Abstract][Full Text] [Related]
25. Deep Learning-Based Radiomics Models for Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images and Clinical Data.
Wang W; Chen Q; Iwamoto Y; Han X; Zhang Q; Hu H; Lin L; Chen YW
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():4881-4884. PubMed ID: 31946954
[TBL] [Abstract][Full Text] [Related]
26. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation.
Shan QY; Hu HT; Feng ST; Peng ZP; Chen SL; Zhou Q; Li X; Xie XY; Lu MD; Wang W; Kuang M
Cancer Imaging; 2019 Feb; 19(1):11. PubMed ID: 30813956
[TBL] [Abstract][Full Text] [Related]
27. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI.
Feng ST; Jia Y; Liao B; Huang B; Zhou Q; Li X; Wei K; Chen L; Li B; Wang W; Chen S; He X; Wang H; Peng S; Chen ZB; Tang M; Chen Z; Hou Y; Peng Z; Kuang M
Eur Radiol; 2019 Sep; 29(9):4648-4659. PubMed ID: 30689032
[TBL] [Abstract][Full Text] [Related]
28. Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma.
Ning P; Gao F; Hai J; Wu M; Chen J; Zhu S; Wang M; Shi D
Abdom Radiol (NY); 2020 Jan; 45(1):64-72. PubMed ID: 31486869
[TBL] [Abstract][Full Text] [Related]
29. Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection.
Li SQ; Su LL; Xu TF; Ren LY; Chen DB; Qin WY; Yan XZ; Fan JX; Chen HS; Liao WJ
World J Gastroenterol; 2023 Jul; 29(26):4186-4199. PubMed ID: 37475840
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
[TBL] [Abstract][Full Text] [Related]
32. 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]
33. Noninvasive Prediction of Ki-67 Expression in Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics: A Multicenter Study.
Zhang L; Duan S; Qi Q; Li Q; Ren S; Liu S; Mao B; Zhang Y; Wang S; Yang L; Liu R; Liu L; Li Y; Li N; Zhang L
J Ultrasound Med; 2023 May; 42(5):1113-1122. PubMed ID: 36412932
[TBL] [Abstract][Full Text] [Related]
34. Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging.
Dong H; Yang L; Shaofeng D; Lili G
Technol Cancer Res Treat; 2024; 23():15330338241245943. PubMed ID: 38660703
[TBL] [Abstract][Full Text] [Related]
35. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.
Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST
Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969
[TBL] [Abstract][Full Text] [Related]
36. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT.
Nayak A; Baidya Kayal E; Arya M; Culli J; Krishan S; Agarwal S; Mehndiratta A
Int J Comput Assist Radiol Surg; 2019 Aug; 14(8):1341-1352. PubMed ID: 31062266
[TBL] [Abstract][Full Text] [Related]
37. Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.
Dercle L; Ma J; Xie C; Chen AP; Wang D; Luk L; Revel-Mouroz P; Otal P; Peron JM; Rousseau H; Lu L; Schwartz LH; Mokrane FZ; Zhao B
Eur J Radiol; 2020 Apr; 125():108850. PubMed ID: 32070870
[TBL] [Abstract][Full Text] [Related]
38. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners.
Hu HT; Shan QY; Chen SL; Li B; Feng ST; Xu EJ; Li X; Long JY; Xie XY; Lu MD; Kuang M; Shen JX; Wang W
Radiol Med; 2020 Aug; 125(8):697-705. PubMed ID: 32200455
[TBL] [Abstract][Full Text] [Related]
39. Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.
Zhou Z; Xia T; Zhang T; Du M; Zhong J; Huang Y; Xuan K; Xu G; Wan Z; Ju S; Xu J
Abdom Radiol (NY); 2024 Feb; 49(2):611-624. PubMed ID: 38051358
[TBL] [Abstract][Full Text] [Related]
40. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.
Du D; Feng H; Lv W; Ashrafinia S; Yuan Q; Wang Q; Yang W; Feng Q; Chen W; Rahmim A; Lu L
Mol Imaging Biol; 2020 Jun; 22(3):730-738. PubMed ID: 31338709
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]