BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Morshid A, Elsayes KM, Khalaf AM, Elmohr MM, Yu J, Kaseb AO, Hassan M, Mahvash A, Wang Z, Hazle JD, Fuentes D. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell. 2019;1. [PMID: 31858078 DOI: 10.1148/ryai.2019180021] [Cited by in Crossref: 22] [Cited by in F6Publishing: 19] [Article Influence: 7.3] [Reference Citation Analysis]
Number Citing Articles
1 Chapiro J, Duncan JS. From Code to Bedside: Introducing Predictive Intelligence to Interventional Oncology. Radiol Artif Intell 2019;1:e190139. [PMID: 32076661 DOI: 10.1148/ryai.2019190139] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
2 Gan JM, Kikano EG, Smith DA, Bui MT, Tirumani SH, Ramaiya NH. Role of Key Guidelines in an Era of Precision Oncology: A Primer for the Radiologist. AJR Am J Roentgenol 2021;216:1112-25. [PMID: 33502227 DOI: 10.2214/AJR.20.23025] [Reference Citation Analysis]
3 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
4 Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021;73:2546-63. [PMID: 33098140 DOI: 10.1002/hep.31603] [Cited by in Crossref: 25] [Cited by in F6Publishing: 13] [Article Influence: 25.0] [Reference Citation Analysis]
5 Newbury A, Ferguson C, Valero DA, Kutcher-Diaz R, McIntosh L, Karamanian A, Harman A. Interventional oncology update. Eur J Radiol Open 2022;9:100430. [PMID: 35761853 DOI: 10.1016/j.ejro.2022.100430] [Reference Citation Analysis]
6 Sheen H, Kim JS, Lee JK, Choi SY, Baek SY, Kim JY. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (NY) 2021;46:2839-49. [PMID: 33388805 DOI: 10.1007/s00261-020-02884-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Dong Z, Lin Y, Lin F, Luo X, Lin Z, Zhang Y, Li L, Li ZP, Feng ST, Cai H, Peng Z. Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography. J Hepatocell Carcinoma 2021;8:1473-84. [PMID: 34877267 DOI: 10.2147/JHC.S334674] [Reference Citation Analysis]
8 Nam D, Chapiro J. Machine Learning-Based Surveillance Strategy after Complete Ablation of Initially Recurrent Hepatocellular Carcinoma: Worth the Risk? J Vasc Interv Radiol 2021;32:1558-9. [PMID: 34717834 DOI: 10.1016/j.jvir.2021.08.014] [Reference Citation Analysis]
9 Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021;29:451-63. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [Reference Citation Analysis]
10 Kobe A, Zgraggen J, Messmer F, Puippe G, Sartoretti T, Alkadhi H, Pfammatter T, Mannil M. Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study. Eur J Radiol Open 2021;8:100375. [PMID: 34485629 DOI: 10.1016/j.ejro.2021.100375] [Reference Citation Analysis]
11 D'Amore B, Smolinski-Zhao S, Daye D, Uppot RN. Role of Machine Learning and Artificial Intelligence in Interventional Oncology. Curr Oncol Rep 2021;23:70. [PMID: 33880651 DOI: 10.1007/s11912-021-01054-6] [Reference Citation Analysis]
12 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2(2): 42-55 [DOI: 10.35712/aig.v2.i2.42] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Papaconstantinou D, Hewitt DB, Brown ZJ, Schizas D, Tsilimigras DI, Pawlik TM. Patient stratification in hepatocellular carcinoma: impact on choice of therapy. Expert Rev Anticancer Ther 2022. [PMID: 35157530 DOI: 10.1080/14737140.2022.2041415] [Reference Citation Analysis]
15 Moawad AW, Fuentes D, Khalaf AM, Blair KJ, Szklaruk J, Qayyum A, Hazle JD, Elsayes KM. Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization. Front Oncol 2020;10:572. [PMID: 32457831 DOI: 10.3389/fonc.2020.00572] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Kuang Y, Li R, Jia P, Ye W, Zhou R, Zhu R, Wang J, Lin S, Pang P, Ji W. MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm. Abdom Radiol (NY) 2021;46:3772-89. [PMID: 33713159 DOI: 10.1007/s00261-021-02992-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
17 O'Rourke C, Jayaraman S, El-Maraghi RH, Singal AG, Kielar AZ. Chronic Liver Disease and Liver Cancer: What the Hepatologists, Oncologists, and Surgeons Want to Know from Radiologists. Magn Reson Imaging Clin N Am 2021;29:269-78. [PMID: 34243916 DOI: 10.1016/j.mric.2021.05.001] [Reference Citation Analysis]
18 Zhao Y, Wang N, Wu J, Zhang Q, Lin T, Yao Y, Chen Z, Wang M, Sheng L, Liu J, Song Q, Wang F, An X, Guo Y, Li X, Wu T, Liu AL. Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma. Front Oncol. 2021;11:582788. [PMID: 33868988 DOI: 10.3389/fonc.2021.582788] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
19 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Mazaheri S, Loya MF, Newsome J, Lungren M, Gichoya JW. Challenges of Implementing Artificial Intelligence in Interventional Radiology. Semin Intervent Radiol 2021;38:554-9. [PMID: 34853501 DOI: 10.1055/s-0041-1736659] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
22 Sartoris R, Gregory J, Dioguardi Burgio M, Ronot M, Vilgrain V. HCC advances in diagnosis and prognosis: Digital and Imaging. Liver Int 2021;41 Suppl 1:73-7. [PMID: 34155790 DOI: 10.1111/liv.14865] [Reference Citation Analysis]
23 Ramachandran A, Bhalla D, Rangarajan K, Pramanik R, Banerjee S, Arora C. Building and evaluating an artificial intelligence algorithm: A practical guide for practicing oncologists. Artif Intell Cancer 2022; 3(3): 42-53 [DOI: 10.35713/aic.v3.i3.42] [Reference Citation Analysis]
24 Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27(32): 5341-5350 [PMID: 34539136 DOI: 10.3748/wjg.v27.i32.5341] [Reference Citation Analysis]
25 Blidisel A, Marcovici I, Coricovac D, Hut F, Dehelean CA, Cretu OM. Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective. Cancers (Basel) 2021;13:3651. [PMID: 34359553 DOI: 10.3390/cancers13153651] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
26 Seah J, Boeken T, Sapoval M, Goh GS. Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol. [DOI: 10.1007/s00270-021-03044-4] [Reference Citation Analysis]
27 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
28 Wu T, Cooper SA, Shah VH. Omics and AI advance biomarker discovery for liver disease. Nat Med 2022;28:1131-2. [PMID: 35710988 DOI: 10.1038/s41591-022-01853-9] [Reference Citation Analysis]
29 Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37): 5617-5628 [PMID: 33088156 DOI: 10.3748/wjg.v26.i37.5617] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
30 Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021;54:5307-46. [DOI: 10.1007/s10462-021-10023-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Gregory J, Dioguardi Burgio M, Corrias G, Vilgrain V, Ronot M. Evaluation of liver tumour response by imaging. JHEP Rep 2020;2:100100. [PMID: 32514496 DOI: 10.1016/j.jhepr.2020.100100] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
32 Wei P. Radiomics, deep learning and early diagnosis in oncology. Emerg Top Life Sci 2021;5:829-35. [PMID: 34874454 DOI: 10.1042/ETLS20210218] [Reference Citation Analysis]
33 Li Y, Xu Z, An C, Chen H, Li X. Multi-Task Deep Learning Approach for Simultaneous Objective Response Prediction and Tumor Segmentation in HCC Patients with Transarterial Chemoembolization. JPM 2022;12:248. [DOI: 10.3390/jpm12020248] [Reference Citation Analysis]
34 Kishore SA, Bajwa R, Madoff DC. Embolotherapeutic Strategies for Hepatocellular Carcinoma: 2020 Update. Cancers (Basel) 2020;12:E791. [PMID: 32224882 DOI: 10.3390/cancers12040791] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
35 Wei ZQ, Zhang YW. Transcatheter arterial chemoembolization followed by surgical resection for hepatocellular carcinoma: a focus on its controversies and screening of patients most likely to benefit. Chin Med J (Engl) 2021;134:2275-86. [PMID: 34593696 DOI: 10.1097/CM9.0000000000001767] [Reference Citation Analysis]
36 Spieler B, Sabottke C, Moawad AW, Gabr AM, Bashir MR, Do RKG, Yaghmai V, Rozenberg R, Gerena M, Yacoub J, Elsayes KM. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021;46:3660-71. [PMID: 33786653 DOI: 10.1007/s00261-021-03056-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
37 Klontzas ME, Manikis GC, Nikiforaki K, Vassalou EE, Spanakis K, Stathis I, Kakkos GA, Matthaiou N, Zibis AH, Marias K, Karantanas AH. Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip. Diagnostics (Basel) 2021;11:1686. [PMID: 34574027 DOI: 10.3390/diagnostics11091686] [Reference Citation Analysis]