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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Radiomics Analysis for Predicting Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas: Comparison of CT and MRI
Shenhao Cheng, Hongyuan Shi, Ming Lu, et al.
Academic Radiology (2021) Vol. 29, Iss. 3, pp. 367-375
Closed Access | Times Cited: 24

Showing 24 citing articles:

Artificial intelligence in pancreatic cancer
Bowen Huang, Haoran Huang, Shuting Zhang, et al.
Theranostics (2022) Vol. 12, Iss. 16, pp. 6931-6954
Open Access | Times Cited: 72
Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications
Kiersten Preuss, Nate Thach, Xiaoying Liang, et al.
Cancers (2022) Vol. 14, Iss. 7, pp. 1654-1654
Open Access | Times Cited: 47
A primer on artificial intelligence in pancreatic imaging
Taha M. Ahmed, Satomi Kawamoto, Ralph H. Hruban, et al.
Diagnostic and Interventional Imaging (2023) Vol. 104, Iss. 9, pp. 435-447
Open Access | Times Cited: 25
Association of high-risk stigmata and worrisome features with advanced neoplasia in intraductal papillary mucinous neoplasms (IPMN): A systematic review
Eizaburo Ohno, Alberto Balduzzi, Susumu Hijioka, et al.
Pancreatology (2023) Vol. 24, Iss. 1, pp. 48-61
Closed Access | Times Cited: 23
A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging
Lanhong Yao, Zheyuan Zhang, Elif Keleş, et al.
Current Opinion in Gastroenterology (2023) Vol. 39, Iss. 5, pp. 436-447
Closed Access | Times Cited: 17
Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
Hari Mohan, Joon Yoo, Abdul Razaque
Expert Systems with Applications (2024) Vol. 255, pp. 124838-124838
Closed Access | Times Cited: 7
Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?
Marina J Corines, Abdalla Ibrahim, Akshay Baheti, et al.
European Journal of Radiology (2025) Vol. 184, pp. 111986-111986
Closed Access
The stability of oncologic MRI radiomic features and the potential role of deep learning: a review
Elisa Scalco, Giovanna Rizzo, Alfonso Mastropietro
Physics in Medicine and Biology (2022) Vol. 67, Iss. 9, pp. 09TR03-09TR03
Closed Access | Times Cited: 16
A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma
Jia Lu, Nannan Jiang, Yuqing Zhang, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 7
Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions
Shiva Rangwani, Devarshi R. Ardeshna, Brandon Rodgers, et al.
Biomimetics (2022) Vol. 7, Iss. 2, pp. 79-79
Open Access | Times Cited: 11
A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images
Lixin Du, Jianpeng Yuan, Meng Gan, et al.
BMC Medical Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 10
Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets
Hari Mohan, Joon Yoo, Syed Atif Moqurrab, et al.
Measurement (2023) Vol. 225, pp. 114059-114059
Closed Access | Times Cited: 6
Intraductal Papillary Mucinous Neoplasm of the Pancreas: A Challenging Diagnosis
Charikleia Triantopoulou, Nicholas Gourtsoyiannis, Dimitriοs Karakaxas, et al.
Diagnostics (2023) Vol. 13, Iss. 12, pp. 2015-2015
Open Access | Times Cited: 4
Radiomics model versus 2017 revised international consensus guidelines for predicting malignant intraductal papillary mucinous neoplasms
Doo Young Lee, Jaeseung Shin, Sung‐Won Kim, et al.
European Radiology (2023) Vol. 34, Iss. 2, pp. 1222-1231
Closed Access | Times Cited: 4
Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations
Deniz Seyithanoglu, Görkem Durak, Elif Keleş, et al.
Cancers (2024) Vol. 16, Iss. 24, pp. 4268-4268
Open Access | Times Cited: 1
Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review
Alberto Balduzzi, Boris V. Janssen, Matteo De Pastena, et al.
British journal of surgery (2023) Vol. 110, Iss. 12, pp. 1623-1627
Open Access | Times Cited: 3
Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma
Piaoe Zeng, Chao Qu, Jianfang Liu, et al.
Acta Radiologica (2022) Vol. 64, Iss. 7, pp. 2221-2228
Closed Access | Times Cited: 4
Early detection of pancreatic cancer in the era of precision medicine
Taha M. Ahmed, Satomi Kawamoto, Felipe Lopez-Ramirez, et al.
Abdominal Radiology (2024) Vol. 49, Iss. 10, pp. 3559-3573
Closed Access
Research Progress in the Application of Radiomics in Pancreatic Diseases
青莉 张
Advances in Clinical Medicine (2024) Vol. 14, Iss. 05, pp. 2142-2149
Closed Access
Application of artificial intelligence and machine learning in pancreatic cancer immunotherapy
Nadia Ahmed, Rebecca P. Gorrie, Teymour Meguid, et al.
Elsevier eBooks (2024), pp. 143-158
Closed Access
Artificial Intelligence Applications in Pancreatic Cancer Imaging
Taha M. Ahmed, Felipe Lopez-Ramirez, Elliot K. Fishman, et al.
Advances in Clinical Radiology (2024) Vol. 6, Iss. 1, pp. 41-54
Closed Access
Advancements and Challenges in Artificial Intelligence-Driven Diagnosis and Management of Pancreatic Cancer
Veeral M. Oza, Nitish Mittal, Ajith Antony, et al.
AI in Precision Oncology (2024) Vol. 1, Iss. 4, pp. 207-215
Closed Access
Clinical Feasibility of 5.0 T MRI/MRCP in Characterizing Pancreatic Cystic Lesions: Comparison with 3.0 T and MDCT
Huijia Zhao, Qiang Xu, Ruichen Gao, et al.
Diagnostics (2024) Vol. 14, Iss. 21, pp. 2457-2457
Open Access
Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis
Palash Rawlani, Nalini Kanta Ghosh, Ashok Kumar
Artificial Intelligence in Gastroenterology (2023) Vol. 4, Iss. 3, pp. 48-63
Open Access
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