Page 164 - Read Online

Grewal et al. Art Int Surg 2023;3:217-32 https://dx.doi.org/10.20517/ais.2023.28 Page 231

learning in action. Cancer Lett 2020;469:228-37. DOI
104. Wu M, Tan H, Gao F, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics
signature. Eur Radiol 2019;29:2802-11. DOI
105. Ye Z, Jiang H, Chen J, et al. Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular
carcinoma: a prospective study. Chin J Cancer Res 2019;31:806-17. DOI
106. Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: a state-of-the-art review. World J Gastrointest
Oncol 2021;13:1599-615. DOI
107. Miranda Magalhaes Santos JM, Clemente Oliveira B, Araujo-Filho JAB, et al. State-of-the-art in radiomics of hepatocellular
carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol 2020;45:342-53. DOI
108. Castaldo A, De Lucia DR, Pontillo G, et al. State of the art in artificial intelligence and radiomics in hepatocellular carcinoma.
Diagnostics 2021;11:1194. DOI
109. Granata V, Fusco R, Belli A, et al. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect
Agent Cancer 2022;17:13. DOI
110. Suh SW, Lee KW, Lee JM, et al. Prediction of aggressiveness in early-stage hepatocellular carcinoma for selection of surgical
resection. J Hepatol 2014;60:1219-24. DOI
111. Kim J, Choi SJ, Lee SH, Lee HY, Park H. Predicting survival using pretreatment ct for patients with hepatocellular carcinoma treated
with transarterial chemoembolization: comparison of models using radiomics. AJR Am J Roentgenol 2018;211:1026-34. DOI
112. Reimer RP, Reimer P, Mahnken AH. Assessment of therapy response to transarterial radioembolization for liver metastases by means
of post-treatment MRI-based texture analysis. Cardiovasc Intervent Radiol 2018;41:1545-56. DOI
113. Chen S, Feng S, Wei J, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model
based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 2019;29:4177-87. DOI
114. Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or
anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 2018;19:1180-91. DOI
115. Zhou W, Zhang L, Wang K, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-
enhanced MR images. J Magn Reson Imaging 2017;45:1476-84. DOI
116. Zhou Y, He L, Huang Y, et al. CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in
hepatocellular carcinoma. Abdom Radiol 2017;42:1695-704. DOI
117. Hu J, Wang Y, Deng L, et al. Development and validation of a nomogram for predicting the cancer-specific survival of fibrolamellar
hepatocellular carcinoma patients. Updates Surg 2022;74:1589-99. DOI
118. Liu Q, Li J, Liu F, et al. A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after
hepatectomy. Cancer Imaging 2020;20:82. DOI
119. Wang XH, Long LH, Cui Y, et al. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with
hepatocellular carcinoma. Br J Cancer 2020;122:978-85. DOI
120. Tabrizian P, Jibara G, Shrager B, Schwartz M, Roayaie S. Recurrence of hepatocellular cancer after resection: patterns, treatments,
and prognosis. Ann Surg 2015;261:947-55. DOI
121. Ji GW, Zhu FP, Xu Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular
carcinoma after resection: a multi-institutional study. EBioMedicine 2019;50:156-65. DOI
122. Yuan C, Wang Z, Gu D, et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics
nomogram. Cancer Imaging 2019;19:21. DOI
123. Zhang Z, Chen J, Jiang H, et al. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular
carcinoma after surgical resection. Ann Transl Med 2020;8:870. DOI
124. Ji GW, Zhang YD, Zhang H, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival
outcomes. Radiology 2019;290:90-8. DOI
125. Peng YT, Zhou CY, Lin P, et al. Preoperative ultrasound radiomics signatures for noninvasive evaluation of biological characteristics
of intrahepatic cholangiocarcinoma. Acad Radiol 2020;27:785-97. DOI
126. Xu L, Yang P, Liang W, et al. A radiomics approach based on support vector machine using MR images for preoperative lymph node
status evaluation in intrahepatic cholangiocarcinoma. Theranostics 2019;9:5374-85. DOI
127. Xiang F, Wei S, Liu X, Liang X, Yang L, Yan S. Radiomics analysis of contrast-enhanced CT for the preoperative prediction of
microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Front Oncol 2021;11:774117. DOI
128. Zhou Y, Zhou G, Zhang J, Xu C, Wang X, Xu P. Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging
biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol 2021;31:6846-55. DOI
129. Yang C, Huang M, Li S, et al. Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node
metastases of extrahepatic cholangiocarcinoma. Cancer Lett 2020;470:1-7. DOI
130. Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and
challenges. Theranostics 2019;9:1303-22. DOI
131. Zhao B. Understanding sources of variation to improve the reproducibility of radiomics. Front Oncol 2021;11:633176. DOI
132. Fiz F, Costa G, Gennaro N, et al. Contrast administration impacts CT-based radiomics of colorectal liver metastases and non-tumoral
liver parenchyma revealing the “Radiological” tumour microenvironment. Diagnostics 2021;11:1162. DOI
133. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin