Page 163 - Read Online

Page 230 Grewal et al. Art Int Surg 2023;3:217-32 https://dx.doi.org/10.20517/ais.2023.28

76. Nasief H, Hall W, Zheng C, et al. Improving treatment response prediction for chemoradiation therapy of pancreatic cancer using a
combination of delta-radiomics and the clinical biomarker CA19-9. Front Oncol 2019;9:1464. DOI PubMed PMC
77. Nasief H, Zheng C, Schott D, et al. A machine learning based delta-radiomics process for early prediction of treatment response of
pancreatic cancer. NPJ Precis Oncol 2019;3:25. DOI PubMed PMC
78. Simpson G, Spieler B, Dogan N, et al. Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic
ablative body radiotherapy of pancreatic cancer: a pilot study. Med Phys 2020;47:3682-90. DOI
79. Yue Y, Osipov A, Fraass B, et al. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET
images for pancreatic cancer patients. J Gastrointest Oncol 2017;8:127-38. DOI PubMed PMC
80. Li X, Wan Y, Lou J, et al. Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using
radiomics of diagnostic computed tomography. EClinicalMedicine 2022;43:101215. DOI PubMed PMC
81. Parr E, Du Q, Zhang C, et al. Radiomics-based outcome prediction for pancreatic cancer following stereotactic body radiotherapy.
Cancers 2020;12:1051. DOI PubMed PMC
82. Tang TY, Li X, Zhang Q, et al. Development of a novel multiparametric MRI radiomic nomogram for preoperative evaluation of
early recurrence in resectable pancreatic cancer. J Magn Reson Imaging 2020;52:231-45. DOI
18
83. Wei M, Gu B, Song S, et al. A novel validated recurrence stratification system based on F-FDG PET/CT radiomics to guide
surveillance after resection of pancreatic cancer. Front Oncol 2021;11:650266. DOI PubMed PMC
84. Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by
the American Association for the study of liver diseases. Hepatology 2018;68:723-50. DOI
85. Joo I, Lee JM, Yoon JH. Imaging diagnosis of intrahepatic and perihilar cholangiocarcinoma: recent advances and challenges.
Radiology 2018;288:7-13. DOI PubMed
86. Potretzke TA, Tan BR, Doyle MB, Brunt EM, Heiken JP, Fowler KJ. Imaging features of biphenotypic primary liver carcinoma
(hepatocholangiocarcinoma) and the potential to mimic hepatocellular carcinoma: LI-RADS analysis of CT and MRI features in 61
cases. AJR Am J Roentgenol 2016;207:25-31. DOI PubMed
87. Gatos I, Tsantis S, Karamesini M, et al. Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI. Med
Phys 2017;44:3695-705. DOI
88. Jansen MJA, Kuijf HJ, Veldhuis WB, Wessels FJ, Viergever MA, Pluim JPW. Automatic classification of focal liver lesions based on
MRI and risk factors. PLoS One 2019;14:e0217053. DOI PubMed PMC
89. Li Z, Mao Y, Huang W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC
Med Imaging 2017;17:42. DOI PubMed PMC
90. Nie P, Yang G, Guo J, et al. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular
carcinoma in the non-cirrhotic liver. Cancer Imaging 2020;20:20. DOI PubMed PMC
91. Wu J, Liu A, Cui J, Chen A, Song Q, Xie L. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on
precontrast magnetic resonance images. BMC Med Imaging 2019;19:23. DOI PubMed PMC
92. Liu X, Khalvati F, Namdar K, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular
cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol
2021;31:244-55. DOI
93. Trivizakis E, Manikis GC, Nikiforaki K, et al. Extending 2-D convolutional neural networks to 3-D for advancing deep learning
cancer classification with application to MRI liver tumor differentiation. IEEE J Biomed Health Inform 2019;23:923-30. DOI
94. Canellas R, Mehrkhani F, Patino M, Kambadakone A, Sahani D. Characterization of portal vein thrombosis (neoplastic versus bland)
on CT images using software-based texture analysis and thrombus density (Hounsfield Units). AJR Am J Roentgenol 2016;207:W81-
7. DOI
95. Santambrogio R, Barabino M, D’Alessandro V, et al. Micronvasive behaviour of single small hepatocellular carcinoma: which
treatment? Updates Surg 2021;73:1359-69. DOI
96. He M, Zhang P, Ma X, He B, Fang C, Jia F. Radiomic feature-based predictive model for microvascular invasion in patients with
hepatocellular carcinoma. Front Oncol 2020;10:574228. DOI PubMed PMC
97. Ni M, Zhou X, Lv Q, et al. Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the
best model? Cancer Imaging 2019;19:60. DOI PubMed PMC
98. Qu C, Wang Q, Li C, et al. A radiomics model based on Gd-EOB-DTPA-enhanced MRI for the prediction of microvascular invasion
in solitary hepatocellular carcinoma ≤ 5 cm. Front Oncol 2022;12:831795. DOI PubMed PMC
99. Zhang W, Yang R, Liang F, et al. Prediction of microvascular invasion in hepatocellular carcinoma with a multi-disciplinary team-
like radiomics fusion model on dynamic contrast-enhanced computed tomography. Front Oncol 2021;11:660629. DOI PubMed
PMC
100. Chu H, Liu Z, Liang W, et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic
cholangiocarcinoma. Eur Radiol 2021;31:2368-76. DOI
101. Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in
oncology. Ann Oncol 2017;28:1191-206. DOI
102. Lopez A, Harada K, Mizrak Kaya D, Dong X, Song S, Ajani JA. Liquid biopsies in gastrointestinal malignancies: when is the big
day? Expert Rev Anticancer Ther 2018;18:19-38. DOI
103. Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK. Radiomics in stratification of pancreatic cystic lesions: machine