ArticleJournal of biological engineering2023
Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks.
Article in Journal of biological engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- From radiomics to transformers in pancreatic cancer detection and prognosis.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Decoding the microbial blueprint of pancreatic cancer.Frontiers in medicine · 2026Review
- Hybrid Convolutional-Gated Recurrent Neural Network for Robust Mobile Health Activities Classification.International journal of telemedicine and applications · 2026Article
- Detection and Classification of Pancreatic Cancer Nodules on CT Images using U-Net and Ensemble Models.Current medical imaging · 2026Article
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- A machine learning framework using urinary biomarkers for pancreatic ductal adenocarcinoma prediction with post hoc validation via single-cell transcriptomics.Briefings in bioinformatics · 2025Article
- DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.Abdominal radiology (New York) · 2025Article
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Advancing clinical biochemistry: addressing gaps and driving future innovations.Frontiers in medicine · 2025Review
- The Past, Present, and Future of Biomarkers for the Early Diagnosis of Pancreatic Cancer.Biomedicines · 2024Review
- Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.BMC gastroenterology · 2024Article
- Data privacy-aware machine learning approach in pancreatic cancer diagnosis.BMC medical informatics and decision making · 2024Article
- Review
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically.
resultsExperiments and evaluations have been successfully done on a public dataset of 590 urine samples of three classes, which are 183 healthy pancreas samples, 208 benign hepatobiliary disease samples, and 199 PDAC samples. The results demonstrated that our proposed 1-D CNN + LSTM model achieved the best accuracy score of 97% and the area under curve (AUC) of 98% versus the state-of-the-art models to diagnose pancreatic cancers using urine biomarkers.
conclusionA new efficient 1D CNN-LSTM model has been successfully developed for early PDAC diagnosis using four proteomic urine biomarkers of creatinine, LYVE1, REG1B, and TFF1. This developed model showed superior performance on other machine learning classifiers in previous studies. The main prospect of this study is the laboratory realization of our proposed deep classifier on urinary biomarker panels for assisting diagnostic procedures of pancreatic cancer patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.