Yenjia Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yenjia Chen has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Po-Ting Chen
Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
2 shared publications - Pochuan Wang
Department of Computer Science and Information Engineering, National Taiwan University , Taipei, 106319
2 shared publications - Weichung Wang
Department of Mathematics and Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan
2 shared publications Andre Yanchen Yeh
Department of Medicine, National Taiwan University College of Medicine , Taipei, 100233
1 shared publication- Andre Yanchen Yeh
Department of Education, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100225, Taiwan
1 shared publication - David Yen-Ting Chen
Department of Medical Image, Shuang Ho Hospital, Taipei Medical University, New Taipei City, 235041, Taiwan
1 shared publication
Latest Publications
- Open Access
- 3 March 2026
- Published by Oxford University Press (OUP) in Radiology Advances
- Vol. 3 (2)
Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis
Background: Pancreatic cancer (PC) is frequently missed on unenhanced CT examinations performed for unrelated clinical indications, where the pancreas is included incidentally and clinical suspicion is low. Purpose: To develop and validate a deep learning-based tool for PC diagnosis and risk stratification on unenhanced CT. Materials and Methods: This retrospective study included 3080 unenhanced CT studies of Taiwanese patients with PC, other pancreatic diseases and normal pancreas between 2004 and 2019 from a tertiary hospital, randomly divided into training, validation, and internal test sets. Unenhanced CT studies from United States institutions were used for external testing. A hybrid convolutional neural network–transformer model was trained for PC diagnosis and risk stratification. Performance was evaluated using sensitivity, specificity, and area under the curve (AUC), with comparisons to 2 radiologists by McNemar’s test and exploratory decision curve analysis. Results: The internal dataset included 713 PCs (mean age, 64.6 ± 12.0 years; 384 men), 1661 normal pancreas and 706 other pancreatic diseases. In an exploratory comparison restricted to unenhanced CT (29 PCs, 31 controls), the sensitivity of computer-aided diagnosis (CAD) tool (89.7%, 72.6-97.8) seemed comparable with that of 1 radiologist (86.2%, 68.3-96.1) and higher than another (41.4%, 23.5-61.1); but wide confidence intervals and inter-radiologist variability warrant cautious interpretation. In the internal test set (142 PCs, 474 controls), sensitivity was 90.8% (84.9-95.0) and specificity 93.0% (90.4-95.2) (AUC: 0.98), with sensitivity comparable to radiologist reports based on enhanced and unenhanced CT (95.4%, 90.2-98.3; P = .21). In the external set (42 PCs, 22 controls), sensitivity was 76.2% (60.5-87.9) and specificity 86.4% (65.1-97.1) (AUC: 0.89). The tool stratified cases into 7 risk levels with likelihood ratios ranging from <0.01 to 173.46. Exploratory decision curve analysis suggested potential net benefit across threshold probabilities. Conclusion: This tool may assist in the opportunistic detection and risk stratification of PC on unenhanced CT.
- 3 March 2026
- Published by Oxford University Press (OUP) in Radiology Advances
- Vol. 3 (2)
Open AccessPancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis
Background: Pancreatic cancer (PC) is frequently missed on unenhanced CT examinations performed for unrelated clinical indications, where the pancreas is included incidentally and clinical suspicion is low. Purpose: To develop and validate a deep learning-based tool for PC diagnosis and risk stratification on unenhanced CT. Materials and Methods: This retrospective study included 3080 unenhanced CT studies of Taiwanese patients with PC, other pancreatic diseases and normal pancreas between 2004 and 2019 from a tertiary hospital, randomly divided into training, validation, and internal test sets. Unenhanced CT studies from United States institutions were used for external testing. A hybrid convolutional neural network–transformer model was trained for PC diagnosis and risk stratification. Performance was evaluated using sensitivity, specificity, and area under the curve (AUC), with comparisons to 2 radiologists by McNemar’s test and exploratory decision curve analysis. Results: The internal dataset included 713 PCs (mean age, 64.6 ± 12.0 years; 384 men), 1661 normal pancreas and 706 other pancreatic diseases. In an exploratory comparison restricted to unenhanced CT (29 PCs, 31 controls), the sensitivity of computer-aided diagnosis (CAD) tool (89.7%, 72.6-97.8) seemed comparable with that of 1 radiologist (86.2%, 68.3-96.1) and higher than another (41.4%, 23.5-61.1); but wide confidence intervals and inter-radiologist variability warrant cautious interpretation. In the internal test set (142 PCs, 474 controls), sensitivity was 90.8% (84.9-95.0) and specificity 93.0% (90.4-95.2) (AUC: 0.98), with sensitivity comparable to radiologist reports based on enhanced and unenhanced CT (95.4%, 90.2-98.3; P = .21). In the external set (42 PCs, 22 controls), sensitivity was 76.2% (60.5-87.9) and specificity 86.4% (65.1-97.1) (AUC: 0.89). The tool stratified cases into 7 risk levels with likelihood ratios ranging from <0.01 to 173.46. Exploratory decision curve analysis suggested potential net benefit across threshold probabilities. Conclusion: This tool may assist in the opportunistic detection and risk stratification of PC on unenhanced CT.