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. 2022 Mar 21;11(3):156–169. doi: 10.4103/EUS-D-21-00063

Table 1.

Study characteristics

Study, year Design, time period, center, country Study aim Image type Machine learning model Total images
Carrara, 2018 Prospective, December 2015-February 2017, Single-center, Italy Characterization of solitary pancreatic lesions EUS elastography Fractal-based quantitative analysis NR
Das, 2008 Retrospective, Single center, USA Differentiate pancreatic adenocarcinoma from nonneoplastic tissue EUS images Neural network 11,099
Marya, 2020 Retrospective, Single center, USA Data on pancreatic adenocarcinoma EUS images/videos Neural network 1,174,461 (EUS images), 955 (EUS frames per second) (video data)
Norton, 2001 Retrospective, single center, USA Differentiate malignancy from pancreatitis EUS images Neural network NR
Ozkan, 2016 Retrospecitve, January 2013-September 2014, Single center, Turkey Diagnosing pancreatic cancer EUS images Neural network 332 (202 cancer and 130 noncancer)
Saftoiu, 2008 Prospective, cross-sectional, multicenter, August 2005-November 2006 (Denmark), December 2006-September 2007 (Romania) Differentiate malignancy from pancreatitis EUS elastography Neural network NR
Saftoiu, 2012 Prospective, blinded, multicenter (13), Romania, Denmark, Germany, Spain, Italy, France, Norway, and United Kingdom Diagnosis of focal pancreatic lesions EUS elastography Neural network 774
Saftoiu, 2015 Prospective, observational trial, multicenter (5), Romania, Denmark, Germany, and Spain Diagnosis of focal pancreatic masses CEH-EUS Neural network NR
Tonozuka, 2020 Prospective, April 2016-August 2019, Single center, Japan Diagnosing pancreatic cancer EUS images Neural network 920 (endosonographic images), 470 (images were independently tested)
Zhang, 2010 Retrospective, Controlled, March 2005 and December 2007, Single center, China Diagnosing pancreatic cancer EUS images SVM NR
Zhu, 2013 Retrospective, May 2002-August 2011, Single center, China Differentiate malignancy from pancreatitis EUS images SVM NR

Study, year Total patients Accuracy Sensitivity Specificity PPV NPV

Carrara, 2018 100 85.3 (95% CI, 78.4-92.2) (pSR)/84.3 (95% CI, 76.5-91.2) (wSR)/84.31 (95% CI, 76.47-90.20) (both) 88.4 (95% CI, 79.7-95.7) (pSR)/91.3 (95% CI, 84.2-97.1) (wSR)/86.96 (95% CI, 78.26-94.20) (both) 78.8 (95% CI, 63.6-91.0) (pSR)/69.7 (95% CI, 54.6-84.9) (wSR)/78.79 (95% CI, 63.64-90.91) (both) 89.7 (95% CI, 83.5-95.5) (pSR)/86.5 (95% CI, 80.3-92.8) (wSR)/89.71 (95% CI, 83.10-95.38) (both) 76.9 (95% CI, 65.0-88.9) (pSR)/80.0 (95% CI, 66.7-92.6) (wSR)/74.29 (95% CI, 62.86-86.67) (both)
Das, 2008 56 (22 n; Group I [normal pancreas], 12 n; Group II [Chronic pancreatitis], 22 n; Group III [pancreatic adenocarcinoma]) 100% 93% (95% CI, 89%-97%) 92% (95% CI, 88%-96%) 87% (95% CI, 82%-92%) 96% (95% CI, 93%-99%)
Marya, 2020 583 NR 0.95 (0.91-0.98) 0.91 (0.86-0.94) 0.87 (0.82-0.91) 0.97 (0.93-0.98)
Norton, 2001 35 (14 n [chronic pancreatitis], 21 n [pancreatic adenocarcinoma]) 80% 100% 50% 75% 100%
Ozkan, 2016 172 87.50% 83.30% 93.30% NR NR
Saftoiu, 2008 68 (22 n=Normal pancrease), (11 n=Chronic pancreatitis), (32 n=Pancreatic adenocarcinoma), and (3 n=Pancreatic neuroendocrine tumors) 89.70% 91.40% 87.90% 88.90% 90.60%
Saftoiu, 2012 258 84.27% (95% CI, 83.09%-85.44%) 87.59% 82.94% 96.25% 57.22%
Saftoiu, 2015 167 (112 n=Pancreatic carcinoma and 55 n=Chronic pancreatitis) NR 94.64% (95% CI, 88.22%-97.80%) 94.44% (95% CI, 83.93%-98.58%) 97.24% (95% CI, 91.57%-99.28%) 89.47% (95% CI, 78.165-95.72%)
Tonozuka, 2020 139 (76 n=Pancreatic ductal carcinoma, 34 n=Chronic pancreatitis, and 29 n=Normal pancreas) NR 92.40% 84.10% 86.80% 90.70%
Zhang, 2010 216 (153 n pancreatic cancer and 63 n [20 n normal pancreas and/or 43 n chronic pancreatitis] noncancer patients) 97.98% (1.23%) 94.32% (0.03%) 99.45% (0.01%) 98.65% (0.02%) 97.77% (0.01%)
Zhu, 2013 388 (262 n=Pancreatic carcinoma and 126 n=Chronic pancreatitis) 94.20% (0.1749%) 96.25% (0.4460%) 93.38% (0.2076%) 92.21% (0.4249%) 96.68% (0.1471%)

CEH: Contrast enhanced harmonic; SVM: Support vector machine; NR: Not reported; pSR: Parenchymal strain ratio; wSR: Wall strain ratio; PPV: Positive predictive value; NPV: Negative predictive value