| 1 |
Mai RY, Zeng J, Meng WD, Lu HZ, Liang R, Lin Y, Wu GB, Li LQ, Ma L, Ye JZ, Bai T. Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion. BMC Cancer 2021;21:283. [PMID: 33726693 DOI: 10.1186/s12885-021-07969-4] [Reference Citation Analysis]
|
| 2 |
Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer 2021;21:1268. [PMID: 34819043 DOI: 10.1186/s12885-021-08947-6] [Reference Citation Analysis]
|
| 3 |
Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Reference Citation Analysis]
|
| 4 |
Tang S, Ou J, Wu YP, Li R, Chen TW, Zhang XM. Contrast-enhanced CT radiomics features to predict recurrence of locally advanced oesophageal squamous cell cancer within 2 years after trimodal therapy: A case-control study. Medicine (Baltimore) 2021;100:e26557. [PMID: 34232198 DOI: 10.1097/MD.0000000000026557] [Reference Citation Analysis]
|
| 5 |
Bell M, Turkbey EB, Escorcia FE. Radiomics, Radiogenomics, and Next-Generation Molecular Imaging to Augment Diagnosis of Hepatocellular Carcinoma. Cancer J. 2020;26:108-115. [PMID: 32205534 DOI: 10.1097/ppo.0000000000000435] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 6 |
Tu HB, Chen LH, Huang YJ, Feng SY, Lin JL, Zeng YY. Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy. World J Clin Cases 2021; 9(24): 7009-7021 [PMID: 34540956 DOI: 10.12998/wjcc.v9.i24.7009] [Reference Citation Analysis]
|
| 7 |
Tang S, Ou J, Liu J, Wu YP, Wu CQ, Chen TW, Zhang XM, Li R, Tang MJ, Yang LQ, Tan BG, Lu FL, Hu J. Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy. Cancer Imaging 2021;21:38. [PMID: 34039403 DOI: 10.1186/s40644-021-00407-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 8 |
Zhu HB, Zheng ZY, Zhao H, Zhang J, Zhu H, Li YH, Dong ZY, Xiao LS, Kuang JJ, Zhang XL, Liu L. Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma.Diagn Interv Radiol. 2020;26:411-419. [PMID: 32490826 DOI: 10.5152/dir.2020.19623] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 9 |
Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics 2022;12:1085. [DOI: 10.3390/diagnostics12051085] [Reference Citation Analysis]
|
