Determining age and sex-specific distribution of pancreatic whole-gland CT attenuation using artificial intelligence aided image segmentation: Associations with body composition and pancreatic cancer risk

Janssens, Laurens; Weston, Alexander D.; Singh, Dhruv; Spears, Grant M.; Harmsen, William S.; Takahashi, Naoki; Philbrick, Kenneth A.; Erickson, Bradley J.; Dayyeh, Barham K. Abu; Chari, Suresh T.; Chandrasekhara, Vinay; Gleeson, Ferga C.; Levy, Michael J.; Pearson, Randall K.; Petersen, Bret T.; Vege, Santhi Swaroop; Majumder, Shounak

doi:10.1016/j.pan.2021.08.004

Cited by 10 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multimedia Appendix 4 shows the detailed characteristics of the included studies. 7) Japan [17] 1 (3) Indonesia [32] 1 (3) South Korea [16] 1 (3) South Africa [23] 1 (3) Saudi Arabia [29] 1 (3) Germany [37] 1 (3) Spain [43] 1 (3) United Kingdom [45] 1 (3) Brazil [44] 1 (3) Turkey [19,[22][23][24][25]29,[31][32][33][34][35][36][37][39][40][41][42]44] 55.6 (8.3) Age of participants (years), mean (SD) Age of the participants (years), n (%) [22,36,44] 3 (…”

Section: Main Characteristics Of the Included Articlesmentioning

confidence: 99%

“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

“…Validation approach a [18,[20][21][22]28,31,33,37,38,40] 10 (33) External validation [16,17,19,[23][24][25]28,34,35,37,39] 10 (33) K-fold cross-validation [19,22,39,41,43] 5 (17) Hold-out cross-validation [31,35] 2 (7) Leave-one-out cross-validation [29] 1 (3) Shuffle-split cross-validation [26,32,36,42] 4 (13) Not reported…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

“…Confusion matrix [18,20,35,40,45] 5 (17) Reported [16,17,19,21,[23][24][25][26][27][28][29][30][31][32][33][34][36][37][38]41,42,44]…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

show abstract

Section: Main Characteristics Of the Included Articlesmentioning

confidence: 99%

“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

“…Confusion matrix [18,20,35,40,45] 5 (17) Reported [16,17,19,21,[23][24][25][26][27][28][29][30][31][32][33][34][36][37][38]41,42,44]…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…They utilized 370 CT scans from subjects with pancreatic cancer and 320 controls and found that the developed algorithm had a sensitivity of 98%, which was significantly higher than the 93% among experienced radiologists. 79 In 2021, Janssens et al 80 trained a CNN on 469 CT scans to identify intrapancreatic fat and pancreatic ductal adenocarcinoma and were able to identify markers that could potentially be used to identify pancreatic adenocarcinoma in the prediagnostic phase.…”

Section: Pancreasmentioning

confidence: 99%