Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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References
Are C, Chowdhury S, Ahmad H, Ravipati A, Song T, Shrikandhe S, Smith L (2016) Predictive global trends in the incidence and mortality of pancreatic cancer based on geographic location, socio-economic status, and demographic shift. J Surg Oncol 114 (6):736–742. https://doi.org/10.1002/jso.24410
Bhosale P, Cox V, Faria S, Javadi S, Viswanathan C, Koay E, Tamm E (2018) Genetics of pancreatic cancer and implications for therapy. Abdom Radiol (NY) 43 (2):404–414. https://doi.org/10.1007/s00261-017-1394-y
Siegel RL, Miller KD, Jemal A (2017) Cancer Statistics, 2017. CA Cancer J Clin 67 (1):7–30. https://doi.org/10.3322/caac.21387
Zins M, Matos C, Cassinotto C (2018) Pancreatic Adenocarcinoma Staging in the Era of Preoperative Chemotherapy and Radiation Therapy. Radiology 287 (2):374–390. https://doi.org/10.1148/radiol.2018171670
Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, Vilgrain V, Hoeffel C, Kanematsu M, Soyer P (2015) Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology 274 (1):45–63. https://doi.org/10.1148/radiol.14130778
Wartski M, Sauvanet A (2019) 18F-FDG PET/CT in pancreatic adenocarcinoma: a role at initial imaging staging? Diagn Interv Imaging 100
Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14 (12):749–762. https://doi.org/10.1038/nrclinonc.2017.141
Abunahel BM, Pontre B, Kumar H, Petrov MS (2021) Pancreas image mining: a systematic review of radiomics. European Radiology 31 (5):3447–3467. https://doi.org/10.1007/s00330-020-07376-6
Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK (2020) Radiomics in stratification of pancreatic cystic lesions: Machine learning in action. Cancer Lett 469:228–237. https://doi.org/10.1016/j.canlet.2019.10.023
Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Chassagnon G, Soyer P (2020) CT and MRI of pancreatic tumors: an update in the era of radiomics. Japanese Journal of Radiology 38 (12):1111–1124. https://doi.org/10.1007/s11604-020-01057-6
Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK (2020) Pancreatic cancer imaging: a new look at an old problem. Curr Probl Diagn Radiol. https://doi.org/10.1067/j.cpradiol.2020.08.002
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30 (9):1234–1248. https://doi.org/10.1016/j.mri.2012.06.010
Duron L, Balvay D, Vande Perre S, Bouchouicha A, Savatovsky J, Sadik JC, Thomassin-Naggara I, Fournier L, Lecler A (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 14 (3):e0213459. https://doi.org/10.1371/journal.pone.0213459
Matzner-Lober E, Suehs CM, Dohan A, Molinari N (2018) Thoughts on entering correlated imaging variables into a multivariable model: application to radiomics and texture analysis. Diagn Interv Imaging 99:269
Nougaret S, Tardieu M, Vargas HA, Reinhold C, Vande Perre S, Bonanno N, Sala E, Thomassin-Naggara I (2019) Ovarian cancer: An update on imaging in the era of radiomics. Diagn Interv Imaging 100 (10):647–655. https://doi.org/10.1016/j.diii.2018.11.007
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006
Akai H, Yasaka K, Kunimatsu A, Nojima M, Kokudo T, Kokudo N, Hasegawa K, Abe O, Ohtomo K, Kiryu S (2018) Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest. Diagn Interv Imaging 99 (10):643–651. https://doi.org/10.1016/j.diii.2018.05.008
Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine Learning for Medical Imaging. RadioGraphics 37 (2):505–515. https://doi.org/10.1148/rg.2017160130
England JR, Cheng PM (2018) Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. American Journal of Roentgenology 212 (3):513–519. https://doi.org/10.2214/AJR.18.20490
Li J, Lu J, Liang P, Li A, Hu Y, Shen Y (2018) Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers. Cancer medicine. https://doi.org/10.1002/cam4.1746
Guo C, Zhuge X, Wang Q, Xiao W, Wang Z, Feng Z (2018) The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging 18
Huang Z, Li M, He D, Wei Y, Yu H, Wang Y, Yuan F, Song B (2019) Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study. Acad Radiol 26 (8):e189–e195. https://doi.org/10.1016/j.acra.2018.07.021
Park S, Chu LC, Hruban RH, Vogelstein B, Kinzler KW, Yuille AL, Fouladi DF, Shayesteh S, Ghandili S, Wolfgang CL, Burkhart R, He J, Fishman EK, Kawamoto S (2020) Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging 101 (9):555–564. https://doi.org/10.1016/j.diii.2020.03.002
Zhang Y, Cheng C, Liu Z, Wang L, Pan G, Sun G, Chang Y, Zuo C, Yang X (2019) Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in (18) F-FDG PET/CT. Med Phys 46 (10):4520–4530. https://doi.org/10.1002/mp.13733
Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK (2019) Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. AJR Am J Roentgenol 213 (2):349–357. https://doi.org/10.2214/AJR.18.20901
Cassinotto C, Chong J, Zogopoulos G, Reinhold C, Chiche L, Lafourcade JP, Cuggia A, Terrebonne E, Dohan A, Gallix B (2017) Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol 90:152–158. https://doi.org/10.1016/j.ejrad.2017.02.033
Attiyeh MA, Chakraborty J, Doussot A, Langdon-Embry L, Mainarich S, Gonen M, Balachandran VP, D'Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Simpson AL, Do RK (2018) Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis. Ann Surg Oncol 25 (4):1034–1042. https://doi.org/10.1245/s10434-017-6323-3
Yun G, Kim YH, Lee YJ, Kim B, Hwang JH, Choi DJ (2018) Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. https://doi.org/10.1038/s41598-018-25627-x
Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, Khalvati F, Haider MA (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma—a quantitative analysis. BMC Med Imaging 17 (1):38. https://doi.org/10.1186/s12880-017-0209-5
Kim HS, Kim YJ, Kim KG, Park JS (2019) Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer. Sci Rep. https://doi.org/10.1038/s41598-019-53831-w
Chakraborty J, Langdon-Embry L, Cunanan KM, Escalon JG, Allen PJ, Lowery MA, O'Reilly EM, Gönen M, Do RG, Simpson AL (2017) Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients. PloS one 12 (12):e0188022–e0188022. https://doi.org/10.1371/journal.pone.0188022
Choi TW, Kim JH, Yu MH, Park SJ, Han JK (2018) Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol 59 (4):383–392. https://doi.org/10.1177/0284185117725367
Tang TY, Li X, Zhang Q, Guo CX, Zhang XZ, Lao MY, Shen YN, Xiao WB, Ying SH, Sun K, Yu RS, Gao SL, Que RS, Chen W, Huang DB, Pang PP, Bai XL, Liang TB (2020) Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer. J Magn Reson Imaging 52 (1):231–245. https://doi.org/10.1002/jmri.27024
Xu W, Liu Y, Lu Z, Jin ZD, Hu YH, Yu JG, Li ZS (2013) A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy. World J Gastroenterol 19 (38):6479–6484. https://doi.org/10.3748/wjg.v19.i38.6479
Cui Y, Song J, Pollom E, Alagappan M, Shirato H, Chang DT, Koong AC, Li R (2016) Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 96 (1):102–109. https://doi.org/10.1016/j.ijrobp.2016.04.034
Cheng S-H, Cheng Y-J, Jin Z-Y, Xue H-D (2019) Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. European Journal of Radiology 113:188–197. https://doi.org/10.1016/j.ejrad.2019.02.009
Cozzi L, Comito T, Fogliata A, Franzese C, Franceschini D, Bonifacio C (2019) Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS ONE 14: e0210758-12
Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M (2019) CT texture analysis of pancreatic cancer. Eur Radiol 29:1067
Zhou HF, Han YQ, Lu J, Wei JW, Guo JH, Zhu HD, Huang M, Ji JS, Lv WF, Chen L, Zhu GY, Jin ZC, Tian J, Teng GJ (2019) Radiomics Facilitates Candidate Selection for Irradiation Stents Among Patients With Unresectable Pancreatic Cancer. Front Oncol 9:973. https://doi.org/10.3389/fonc.2019.00973
Yue Y, Osipov A, Fraass B, Sandler H, Zhang X, Nissen N, Hendifar A, Tuli R (2017) Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients. J Gastrointest Oncol 8 (1):127–138. https://doi.org/10.21037/jgo.2016.12.04
Chen X, Oshima K, Schott D, Wu H, Hall W, Song Y, Tao Y, Li D, Zheng C, Knechtges P, Erickson B, Li XA (2017) Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study. PLoS One 12 (6):e0178961. https://doi.org/10.1371/journal.pone.0178961
Ciaravino V, Cardobi N, de Robertis R, Capelli P, Melisi D, Simionato F, Marchegiani G, Salvia R, D'Onofrio M (2018) CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy. Anticancer Res 38 (8):4889–4895. https://doi.org/10.21873/anticanres.12803
Kaissis G, Ziegelmayer S, Lohofer F, Steiger K, Algul H, Muckenhuber A, Yen HY, Rummeny E, Friess H, Schmid R, Weichert W, Siveke JT, Braren R (2019) A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy. PLoS One 14 (10):e0218642. https://doi.org/10.1371/journal.pone.0218642
Nasief H, Zheng C, Schott D, Hall W, Tsai S, Erickson B, Allen Li X (2019) A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol 3:25. https://doi.org/10.1038/s41698-019-0096-z
Borhani AA, Dewan R, Furlan A, Seiser N, Zureikat AH, Singhi AD, Boone B, Bahary N, Hogg ME, Lotze M, Zeh HJ, III, Tublin ME (2020) Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma. AJR Am J Roentgenol 214 (2):362–369. https://doi.org/10.2214/AJR.19.21152
Saleh M, Bhosale PR, Yano M, Itani M, Elsayes AK, Halperin D, Bergsland EK, Morani AC (2020) New frontiers in imaging including radiomics updates for pancreatic neuroendocrine neoplasms. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02833-8
Nagtegaal ID, Odze RD, Klimstra D, Paradis V, Rugge M, Schirmacher P, Washington KM, Carneiro F, Cree IA (2020) The 2019 WHO classification of tumours of the digestive system. Histopathology 76 (2):182–188. https://doi.org/10.1111/his.13975
Canellas R, Burk KS, Parakh A, Sahani DV (2018) Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis. AJR Am J Roentgenol 210 (2):341–346. https://doi.org/10.2214/ajr.17.18417
Choi MH, Lee YJ, Yoon SB, Choi JI, Jung SE, Rha SE (2019) MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdom Radiol 44:122
D'Onofrio M, Ciaravino V, Cardobi N, De Robertis R, Cingarlini S, Landoni L, Capelli P, Bassi C, Scarpa A (2019) CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms. Sci Rep 9 (1):2176. https://doi.org/10.1038/s41598-018-38459-6
Gu D, Hu Y, Ding H, Wei J, Chen K, Liu H, Zeng M, Tian J (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880
Guo C, Zhuge X, Wang Z, Wang Q, Sun K, Feng Z (2019) Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdominal Radiology. https://doi.org/10.1007/s00261-018-1763-1
Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, Duan SF, Wang ZQ (2019) Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res 11:1933–1944. https://doi.org/10.2147/CMAR.S195376
Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, Zhang L, Wan D, Huang Q, Lu Y, Kuang Y, Niu T (2019) A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res 25 (2):584–594. https://doi.org/10.1158/1078-0432.Ccr-18-1305
Zhao Z, Bian Y, Jiang H, Fang X, Li J, Cao K, Ma C, Wang L, Zheng J, Yue X, Zhang H, Wang X, Madabhushi A, Xu J, Jin G, Lu J (2020) CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor. Acad Radiol 27 (12):e272–e281. https://doi.org/10.1016/j.acra.2020.01.002
Guo C, Zhuge X, Wang Q, Xiao W, Wang Z, Wang Z (2018) The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging. https://doi.org/10.1186/s40644-018-0170-8
He M, Liu Z, Lin Y, Wan J, Li J, Xu K, Wang Y, Jin Z, Tian J, Xue H (2019) Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. Eur J Radiol 117:102–111. https://doi.org/10.1016/j.ejrad.2019.05.024
Wang YW, Zhang XH, Wang BT, Wang Y, Liu MQ, Wang HY, Ye HY, Chen ZY (2019) Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma. Chin Med Sci J 34 (1):1–9. https://doi.org/10.24920/003531
Yu H, Huang Z, Li M, Wei Y, Zhang L, Yang C, Zhang Y, Song B (2020) Differential Diagnosis of Nonhypervascular Pancreatic Neuroendocrine Neoplasms From Pancreatic Ductal Adenocarcinomas, Based on Computed Tomography Radiological Features and Texture Analysis. Acad Radiol 27 (3):332–341. https://doi.org/10.1016/j.acra.2019.06.012
Reinert CP, Baumgartner K, Hepp T, Bitzer M, Horger M (2020) Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase. Abdom Radiol (NY) 45 (3):750–758. https://doi.org/10.1007/s00261-020-02406-9
Lin X, Xu L, Wu A, Guo C, Chen X, Wang Z (2019) Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol 60:553
Li X, Zhu H, Qian X, Chen N, Lin X (2020) MRI texture analysis for differentiating nonfunctional pancreatic neuroendocrine neoplasms from solid pseudopapillary neoplasms of the pancreas. Acad Radiol 27:815
Klibansky DA, Reid-Lombardo KM, Gordon SR, Gardner TB (2012) The clinical relevance of the increasing incidence of intraductal papillary mucinous neoplasm. Clin Gastroenterol Hepatol 10 (5):555–558. https://doi.org/10.1016/j.cgh.2011.12.029
Tanaka M, Fernández-Del Castillo C, Kamisawa T, Jang JY, Levy P, Ohtsuka T, Salvia R, Shimizu Y, Tada M, Wolfgang CL (2017) Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology 17 (5):738–753. https://doi.org/10.1016/j.pan.2017.07.007
Machicado JD, Koay EJ, Krishna SG (2020) Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions. Diagnostics (Basel). https://doi.org/10.3390/diagnostics10070505
Hanania AN, Bantis LE, Feng Z, Wang H, Tamm EP, Katz MH (2016) Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget 7:85776
Permuth JB, Choi J, Balarunathan Y, Kim J, Chen DT, Chen L, Orcutt S, Doepker MP, Gage K, Zhang G, Latifi K, Hoffe S, Jiang K, Coppola D, Centeno BA, Magliocco A, Li Q, Trevino J, Merchant N, Gillies R, Malafa M (2016) Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget 7 (52):85785–85797. https://doi.org/10.18632/oncotarget.11768
Dmitriev K, Kaufman AE, Javed AA, Hruban RH, Fishman EK, Lennon AM, Saltz JH (2017) Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble. Med Image Comput Comput Assist Interv 10435:150–158. https://doi.org/10.1007/978-3-319-66179-7_18
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ (2018) CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys 45:5019
Attiyeh MA, Chakraborty J, Gazit L, Langdon-Embry L, Gonen M, Balachandran VP, D'Angelica MI, DeMatteo RP, Jarnagin WR, Kingham TP, Allen PJ, Do RK, Simpson AL (2019) Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. HPB (Oxford) 21 (2):212–218. https://doi.org/10.1016/j.hpb.2018.07.016
Wei R, Lin K, Yan W, Guo Y, Wang Y, Li J, Zhu J (2019) Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images. Technology in cancer research & treatment 18:1533033818824339-1533033818824339. https://doi.org/10.1177/1533033818824339
Yang J, Guo X, Ou X, Zhang W, Ma X (2019) Discrimination of pancreatic serous cystadenomas from mucinous cystadenomas with CT textural features: based on machine learning. Front Oncol 9:494
Shen X, Yang F, Yang P, Yang M, Xu L, Zhuo J, Wang J, Lu D, Liu Z, Zheng SS, Niu T, Xu X (2020) A Contrast-Enhanced Computed Tomography Based Radiomics Approach for Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes: A Feasibility Study. Front Oncol 10:248. https://doi.org/10.3389/fonc.2020.00248
Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z (2020) Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection. Eur Radiol 30 (5):2513–2524. https://doi.org/10.1007/s00330-019-06600-2
Xie H, Ma S, Guo X, Zhang X, Wang X (2020) Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. Eur J Radiol 122:108747. https://doi.org/10.1016/j.ejrad.2019.108747
Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers R, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Löck S (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 295 (2):328–338. https://doi.org/10.1148/radiol.2020191145
Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg 102 (3):148–158. https://doi.org/10.1002/bjs.9736
Hayes DF (2015) Biomarker validation and testing. Molecular Oncology 9 (5):960–966. https://doi.org/https://doi.org/10.1016/j.molonc.2014.10.004
Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, Danesi R (2018) Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights into Imaging 9 (6):915–924. https://doi.org/10.1007/s13244-018-0657-7
Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, Hoekstra CJ, Boellaard R, Dekker AL, Gillies RJ, Aerts HJ, Lambin P (2013) Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52 (7):1391–1397. https://doi.org/10.3109/0284186x.2013.812798
Zhang MM, Yang H, Jin ZD, Yu JG, Cai ZY, Li ZS (2010) Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images. Gastrointest Endosc 72(5):978–985. https://doi.org/10.1016/j.gie.2010.06.042
Attiyeh MA, Chakraborty J, McIntyre CA, Kappagantula R, Chou Y, Askan G, Seier K, Gonen M, Basturk O, Balachandran VP, Kingham TP, D'Angelica MI, Drebin JA, Jarnagin WR, Allen PJ, Iacobuzio-Donahue CA, Simpson AL, Do RK (2019) CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 44(9):3148–3157. https://doi.org/10.1007/s00261-019-02112-1
Lim CH, Cho YS, Choi JY, Lee KH, Lee JK, Min JH, Hyun SH (2020) Imaging phenotype using (18)F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging 47(9):2113–2122. https://doi.org/10.1007/s00259-020-04698-x
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Virarkar, M., Wong, V.K., Morani, A.C. et al. Update on quantitative radiomics of pancreatic tumors. Abdom Radiol 47, 3118–3160 (2022). https://doi.org/10.1007/s00261-021-03216-3
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DOI: https://doi.org/10.1007/s00261-021-03216-3