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Mishra AK, Chong B, Arunachalam SP, Oberg AL, Majumder S. Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment. Am J Gastroenterol 2024; 119:1466-1482. [PMID: 38752654 PMCID: PMC11296923 DOI: 10.14309/ajg.0000000000002870] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/06/2024] [Indexed: 06/20/2024]
Abstract
INTRODUCTION Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.
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Affiliation(s)
- Anup Kumar Mishra
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Bradford Chong
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | | | - Ann L. Oberg
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Shounak Majumder
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
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Ali S, Coory M, Donovan P, Na R, Pandeya N, Pearson SA, Spilsbury K, Tuesley K, Jordan SJ, Neale RE. Predicting the risk of pancreatic cancer in women with new-onset diabetes mellitus. J Gastroenterol Hepatol 2024; 39:1057-1064. [PMID: 38373821 DOI: 10.1111/jgh.16503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/20/2023] [Accepted: 01/17/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND AND AIM People with new-onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new-onset diabetes. METHODS We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti-diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer. RESULTS Among 99 687 women aged ≥ 50 years with new-onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta-blockers, acid disorder drugs, and lipid-modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%. CONCLUSIONS Our model doubled the PPV of pancreatic cancer in women with new-onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.
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Affiliation(s)
- Sitwat Ali
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Michael Coory
- Centre of Research Excellence in Stillbirth, Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Peter Donovan
- Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Renhua Na
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nirmala Pandeya
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Katrina Spilsbury
- Centre Institute for Health Research, University of Notre Dame Australia, Fremantle, Western Australia, Australia
| | - Karen Tuesley
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Susan J Jordan
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Rachel E Neale
- School of Public Health, University of Queensland, Brisbane, Queensland, Australia
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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Zhao G, Chen X, Zhu M, Liu Y, Wang Y. Exploring the application and future outlook of Artificial intelligence in pancreatic cancer. Front Oncol 2024; 14:1345810. [PMID: 38450187 PMCID: PMC10915754 DOI: 10.3389/fonc.2024.1345810] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.
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Affiliation(s)
- Guohua Zhao
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
| | - Xi Chen
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Mengying Zhu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
- Department of Clinical integration of traditional Chinese and Western medicine, Liaoning University of Traditional Chinese Medicine, Liaoning, China
| | - Yang Liu
- Department of Ophthalmology, First Hospital of China Medical University, Liaoning, China
| | - Yue Wang
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning, China
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Jia K, Kundrot S, Palchuk MB, Warnick J, Haapala K, Kaplan ID, Rinard M, Appelbaum L. A pancreatic cancer risk prediction model (Prism) developed and validated on large-scale US clinical data. EBioMedicine 2023; 98:104888. [PMID: 38007948 PMCID: PMC10755107 DOI: 10.1016/j.ebiom.2023.104888] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND Pancreatic Duct Adenocarcinoma (PDAC) screening can enable early-stage disease detection and long-term survival. Current guidelines use inherited predisposition, with about 10% of PDAC cases eligible for screening. Using Electronic Health Record (EHR) data from a multi-institutional federated network, we developed and validated a PDAC RISk Model (Prism) for the general US population to extend early PDAC detection. METHODS Neural Network (PrismNN) and Logistic Regression (PrismLR) were developed using EHR data from 55 US Health Care Organisations (HCOs) to predict PDAC risk 6-18 months before diagnosis for patients 40 years or older. Model performance was assessed using Area Under the Curve (AUC) and calibration plots. Models were internal-externally validated by geographic location, race, and time. Simulated model deployment evaluated Standardised Incidence Ratio (SIR) and other metrics. FINDINGS With 35,387 PDAC cases, 1,500,081 controls, and 87 features per patient, PrismNN obtained a test AUC of 0.826 (95% CI: 0.824-0.828) (PrismLR: 0.800 (95% CI: 0.798-0.802)). PrismNN's average internal-external validation AUCs were 0.740 for locations, 0.828 for races, and 0.789 (95% CI: 0.762-0.816) for time. At SIR = 5.10 (exceeding the current screening inclusion threshold) in simulated model deployment, PrismNN sensitivity was 35.9% (specificity 95.3%). INTERPRETATION Prism models demonstrated good accuracy and generalizability across diverse populations. PrismNN could find 3.5 times more cases at comparable risk than current screening guidelines. The small number of features provided a basis for model interpretation. Integration with the federated network provided data from a large, heterogeneous patient population and a pathway to future clinical deployment. FUNDING Prevent Cancer Foundation, TriNetX, Boeing, DARPA, NSF, and Aarno Labs.
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Affiliation(s)
- Kai Jia
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | | | | | | | | | - Irving D Kaplan
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
| | - Martin Rinard
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Limor Appelbaum
- Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
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Ke TM, Lophatananon A, Muir KR. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank. Biomedicines 2023; 11:3206. [PMID: 38137427 PMCID: PMC10740416 DOI: 10.3390/biomedicines11123206] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/24/2023] Open
Abstract
Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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Chen S, Phuc PT, Nguyen P, Burton W, Lin S, Lin W, Lu CY, Hsu M, Cheng C, Hsu JC. A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning. Cancer Med 2023; 12:19987-19999. [PMID: 37737056 PMCID: PMC10587954 DOI: 10.1002/cam4.6547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/14/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023] Open
Abstract
INTRODUCTION Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms. METHODS This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. RESULTS The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid-modifying drug use. CONCLUSION This study successfully developed a highly accurate 4-year risk model for pancreatic cancer in patients with diabetes using real-world clinical data and multiple machine-learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.
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Affiliation(s)
- Shih‐Min Chen
- School of PharmacyTaipei Medical UniversityTaipeiTaiwan
| | - Phan Thanh Phuc
- International Ph.D. Program in Biotech and Healthcare Management, College of ManagementTaipei Medical UniversityTaipeiTaiwan
| | - Phung‐Anh Nguyen
- Clinical Data Center, Office of Data ScienceTaipei Medical UniversityTaipeiTaiwan
- Clinical Big Data Research CenterTaipei Medical University Hospital, Taipei Medical UniversityTaipeiTaiwan
- Research Center of Health Care Industry Data Science, College of ManagementTaipei Medical UniversityTaipeiTaiwan
| | - Whitney Burton
- International Ph.D. Program in Biotech and Healthcare Management, College of ManagementTaipei Medical UniversityTaipeiTaiwan
| | | | - Weei‐Chin Lin
- Section of Hematology/Oncology, Department of Medicine and Department of Molecular and Cellular BiologyBaylor College of MedicineHoustonTexasUSA
| | - Christine Y. Lu
- Department of Population MedicineHarvard Medical School and Harvard Pilgrim Health Care InstituteBostonMassachusettsUSA
- Kolling Institute, Faculty of Medicine and HealthThe University of Sydney and the Northern Sydney Local Health DistrictSydneyNew South WalesAustralia
- School of Pharmacy, Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
| | - Min‐Huei Hsu
- Clinical Data Center, Office of Data ScienceTaipei Medical UniversityTaipeiTaiwan
- Graduate Institute of Data Science, College of ManagementTaipei Medical UniversityTaipeiTaiwan
| | - Chi‐Tsun Cheng
- Research Center of Health Care Industry Data Science, College of ManagementTaipei Medical UniversityTaipeiTaiwan
| | - Jason C. Hsu
- International Ph.D. Program in Biotech and Healthcare Management, College of ManagementTaipei Medical UniversityTaipeiTaiwan
- Clinical Data Center, Office of Data ScienceTaipei Medical UniversityTaipeiTaiwan
- Clinical Big Data Research CenterTaipei Medical University Hospital, Taipei Medical UniversityTaipeiTaiwan
- Research Center of Health Care Industry Data Science, College of ManagementTaipei Medical UniversityTaipeiTaiwan
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Bojesen AB, Mortensen FV, Kirkegård J. Real-Time Identification of Pancreatic Cancer Cases Using Artificial Intelligence Developed on Danish Nationwide Registry Data. JCO Clin Cancer Inform 2023; 7:e2300084. [PMID: 37812754 DOI: 10.1200/cci.23.00084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/18/2023] [Accepted: 08/29/2023] [Indexed: 10/11/2023] Open
Abstract
PURPOSE Pancreatic cancer is expected to be the second leading cause of cancer-related deaths worldwide within few years. Most patients are not diagnosed in time for curative-intent treatment. Accelerating the time of diagnosis is a key component of reducing pancreatic cancer mortality. We developed and tested a dynamic algorithm aiming at proactively identifying patients with a substantially elevated risk of having undiagnosed pancreatic cancer. METHODS Machine learning methodology was applied to a live stream of nationwide Danish registry data. A hybrid case-control and prospective cohort design relying on incidence density sampling was used. Three models with minimal tuning were tested. All performance evaluation metrics were based on out-of-sample, out-of-time data in a monthly walk-forward strategy to avoid any temporal biases or inflation of performance metrics. Outcome was a diagnosis of pancreatic cancer. RESULTS Subgroups identified had a 10.1% risk of being diagnosed with pancreatic cancer within 1 year, corresponding to a number needed to screen of 9.9. When considering competing, potentially computed tomography-detectable GI cancers, this number is reduced to 5.7. The time of diagnosis can be accelerated by up to 142 days. CONCLUSION Currently available nationwide live data and computational resources are sufficient for real-time identification of individuals with at least 10.1% risk of having undiagnosed pancreatic cancer and 17.7% risk of any GI cancer in the Danish population. For prospective identification of high-risk patients, the area under the curve is not a useful indication of the positive predictive values achieved. Viable design solutions are demonstrated, which address the main shortfalls of the existing cancer prediction efforts in relation to temporal biases, leaks, and performance metric inflation. Efficacy evaluations with resection rates and mortality as end points are needed.
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Affiliation(s)
- Anders Bo Bojesen
- Department of Surgery, HPB Section, Aarhus University Hospital, Aarhus, Denmark
| | - Frank Viborg Mortensen
- Department of Surgery, HPB Section, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jakob Kirkegård
- Department of Surgery, HPB Section, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Zhang Y, Wang QL, Yuan C, Lee AA, Babic A, Ng K, Perez K, Nowak JA, Lagergren J, Stampfer MJ, Giovannucci EL, Sander C, Rosenthal MH, Kraft P, Wolpin BM. Pancreatic cancer is associated with medication changes prior to clinical diagnosis. Nat Commun 2023; 14:2437. [PMID: 37117188 PMCID: PMC10147931 DOI: 10.1038/s41467-023-38088-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 04/11/2023] [Indexed: 04/30/2023] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) commonly develop symptoms and signs in the 1-2 years before diagnosis that can result in changes to medications. We investigate recent medication changes and PDAC diagnosis in Nurses' Health Study (NHS; females) and Health Professionals Follow-up Study (HPFS; males), including up to 148,973 U.S. participants followed for 2,994,057 person-years and 991 incident PDAC cases. Here we show recent initiation of antidiabetic (NHS) or anticoagulant (NHS, HFS) medications and cessation of antihypertensive medications (NHS, HPFS) are associated with pancreatic cancer diagnosis in the next 2 years. Two-year PDAC risk increases as number of relevant medication changes increases (P-trend <1 × 10-5), with participants who recently start antidiabetic and stop antihypertensive medications having multivariable-adjusted hazard ratio of 4.86 (95%CI, 1.74-13.6). These changes are not associated with diagnosis of other digestive system cancers. Recent medication changes should be considered as candidate features in multi-factor risk models for PDAC, though they are not causally implicated in development of PDAC.
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Affiliation(s)
- Yin Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Qiao-Li Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Department of Clinical Science, Intervention and Technology, Karolinka Institutet, Stockholm, Sweden
| | - Chen Yuan
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Alice A Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Kimberly Perez
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jesper Lagergren
- Upper Gastrointestinal Surgery, Department of Molecular Medicine and Surgery, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Meir J Stampfer
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Chris Sander
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael H Rosenthal
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
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Mazer BL, Lee JW, Roberts NJ, Chu LC, Lennon AM, Klein AP, Eshleman JR, Fishman EK, Canto MI, Goggins MG, Hruban RH. Screening for pancreatic cancer has the potential to save lives, but is it practical? Expert Rev Gastroenterol Hepatol 2023; 17:555-574. [PMID: 37212770 PMCID: PMC10424088 DOI: 10.1080/17474124.2023.2217354] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/21/2023] [Accepted: 05/19/2023] [Indexed: 05/23/2023]
Abstract
INTRODUCTION Most patients with pancreatic cancer present with advanced stage, incurable disease. However, patients with high-grade precancerous lesions and many patients with low-stage disease can be cured with surgery, suggesting that early detection has the potential to improve survival. While serum CA19.9 has been a long-standing biomarker used for pancreatic cancer disease monitoring, its low sensitivity and poor specificity have driven investigators to hunt for better diagnostic markers. AREAS COVERED This review will cover recent advances in genetics, proteomics, imaging, and artificial intelligence, which offer opportunities for the early detection of curable pancreatic neoplasms. EXPERT OPINION From exosomes, to circulating tumor DNA, to subtle changes on imaging, we know much more now about the biology and clinical manifestations of early pancreatic neoplasia than we did just five years ago. The overriding challenge, however, remains the development of a practical approach to screen for a relatively rare, but deadly, disease that is often treated with complex surgery. It is our hope that future advances will bring us closer to an effective and financially sound approach for the early detection of pancreatic cancer and its precursors.
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Affiliation(s)
- Benjamin L. Mazer
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jae W. Lee
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
| | - Nicholas J. Roberts
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Linda C. Chu
- Department of Radiology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anne Marie Lennon
- Department of Medicine, Division of Gastroenterology and Hepatology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alison P. Klein
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James R. Eshleman
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elliot K. Fishman
- Department of Radiology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marcia Irene Canto
- Department of Medicine, Division of Gastroenterology and Hepatology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael G. Goggins
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ralph H. Hruban
- The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, the Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Santos R, Coleman HG, Cairnduff V, Kunzmann AT. Clinical Prediction Models for Pancreatic Cancer in General and At-Risk Populations: A Systematic Review. Am J Gastroenterol 2023; 118:26-40. [PMID: 36148840 DOI: 10.14309/ajg.0000000000002022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/16/2022] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk. METHODS MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias. RESULTS In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n = 14), patients with diabetes (n = 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n = 11). The commonly used clinical risk factors in the model were cigarette smoking (n = 27), age (n = 25), diabetes history (n = 22), chronic pancreatitis (n = 18), and body mass index (n = 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST. DISCUSSION Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.
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Affiliation(s)
- Ralph Santos
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Helen G Coleman
- Centre for Public Health, Queen's University Belfast, Belfast, UK
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
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11
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Ruze R, Chen Y, Xu R, Song J, Yin X, Wang C, Xu Q. Obesity, diabetes mellitus, and pancreatic carcinogenesis: Correlations, prevention, and diagnostic implications. Biochim Biophys Acta Rev Cancer 2023; 1878:188844. [PMID: 36464199 DOI: 10.1016/j.bbcan.2022.188844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/13/2022] [Accepted: 11/26/2022] [Indexed: 12/03/2022]
Abstract
The prevalence of obesity, diabetes mellitus (DM), and pancreatic cancer (PC) has been consistently increasing in the last two decades worldwide. Sharing various influential risk factors in genetics and environmental inducers in pathogenesis, the close correlations of these three diseases have been demonstrated in plenty of clinical studies using multiple parameters among different populations. On the contrary, most measures aimed to manage and treat obesity and DM effectively reduce the risk and prevent PC occurrence, yet certain drugs can inversely promote pancreatic carcinogenesis instead. Most importantly, an elevation of blood glucose with or without a reduction in body weight, along with other potential tools, may provide valuable clues for detecting PC at an early stage in patients with obesity and DM, favoring a timely intervention and prolonging survival. Herein, the epidemiological and etiological correlations among these three diseases and the supporting clinical evidence of their connections are first summarized to favor a better and more thorough understanding of obesity- and DM-related pancreatic carcinogenesis. After comparing the distinct impacts of different weight-lowering and anti-diabetic treatments on the risk of PC, the possible diagnostic implications of hyperglycemia and weight loss in PC screening are also addressed in detail.
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Affiliation(s)
- Rexiati Ruze
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, No. 9 Dongdan Santiao, Beijing, China
| | - Yuan Chen
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, No. 9 Dongdan Santiao, Beijing, China
| | - Ruiyuan Xu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, No. 9 Dongdan Santiao, Beijing, China
| | - Jianlu Song
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, No. 9 Dongdan Santiao, Beijing, China
| | - Xinpeng Yin
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China; Chinese Academy of Medical Sciences and Peking Union Medical College, No. 9 Dongdan Santiao, Beijing, China
| | - Chengcheng Wang
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China.
| | - Qiang Xu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing Street, Beijing, China.
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12
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Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD. Cancers (Basel) 2022; 14:cancers14040882. [PMID: 35205630 PMCID: PMC8870511 DOI: 10.3390/cancers14040882] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three year in Taiwan. It is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. This is a population-based study from NHIRD. A higher performance pancreatic cancer prediction model has been established. This predictive model can improve the awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated. Abstract (1) Background: Cancer has been the leading cause of death in Taiwan for 39 years, and among them, pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three years. While the incidence rate of pancreatic cancer is ranked at the bottom of the top 10 cancers, the survival rate is very low. Pancreatic cancer is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. Early screening is important for the treatment of pancreatic cancer. Only a few studies have designed predictive models for pancreatic cancer. (2) Methods: The Taiwan Health Insurance Database was used in this study, covering over 99% of the population in Taiwan. The subset sample was not significantly different from the original NHIRD sample. A machine learning approach was used to develop a predictive model for pancreatic cancer disease. Four models, including logistic regression, deep neural networks, ensemble learning, and voting ensemble were used in this study. The ROC curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. (3) Results: The AUC of the LR model was higher than the other three models in the external testing set for all three of the factor combinations. Sensitivity was best measured by the stacking model for the first factor combinations, and specificity was best measured by the DNN model for the second factor combination. The result of the model that used only nine factors (third factor combinations) was equal to the other two factor combinations. The AUC of the previous models for the early assessment of pancreatic cancer ranged from approximately 0.57 to 0.71. The AUC of this study was higher than that of previous studies and ranged from 0.71 to 0.76, which provides higher accuracy. (4) Conclusions: This study compared the performances of LR, DNN, stacking, and voting models for pancreatic cancer prediction and constructed a pancreatic cancer prediction model with accuracy higher than that of previous studies. This predictive model will improve awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated.
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13
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Jeon CY, Kim S, Lin YC, Risch HA, Goodarzi MO, Nuckols TK, Freedland SJ, Pandol SJ, Pisegna JR. Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control. Cancer Epidemiol Biomarkers Prev 2022; 31:242-253. [PMID: 34728468 PMCID: PMC8759109 DOI: 10.1158/1055-9965.epi-21-0712] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/25/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed prediction models for PDAC among those with worsening glycemic control after diabetes diagnosis. METHODS In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) ≥8% with ≥Δ1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clinical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap. RESULTS Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were ≥1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%. CONCLUSIONS Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes. IMPACT Our models can identify diabetes patients who would benefit from PDAC screening.
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Affiliation(s)
- Christie Y Jeon
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California.
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, California
| | - Sungjin Kim
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California
| | - Yu-Chen Lin
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California
| | - Harvey A Risch
- Department of Epidemiology, Yale School of Public Health, Los Angeles, California
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, California
| | - Teryl K Nuckols
- Division of General Internal Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephen J Freedland
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, California
- Section of Urology, Durham VA Medical Center, Durham, North Carolina
| | - Stephen J Pandol
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joseph R Pisegna
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California
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14
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Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas 2021; 50:251-279. [PMID: 33835956 PMCID: PMC8041569 DOI: 10.1097/mpa.0000000000001762] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
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Affiliation(s)
| | - Suresh T. Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Stephen J. Pandol
- Basic and Translational Pancreas Research Program, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anil K. Rustgi
- Division of Digestive and Liver Diseases, Department of Medicine, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | | | - Adam Yala
- Department of Electrical Engineering and Computer Science
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Noura Abul-Husn
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, Mount Sinai, New York, NY
| | - Dana K. Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | | | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Marcia Irene Canto
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Yonina C. Eldar
- Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Elliot K. Fishman
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD
| | | | - Vay Liang W. Go
- UCLA Center for Excellence in Pancreatic Diseases, University of California, Los Angeles, Los Angeles, CA
| | | | - Bruce Field
- From the Kenner Family Research Fund, New York, NY
| | - Ann Goldberg
- From the Kenner Family Research Fund, New York, NY
| | | | - Christine Iacobuzio-Donahue
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | | | | | - Lawrence H. Schwartz
- Department of Radiology, NewYork-Presbyterian Hospital/Columbia University Irving Medical Center, New York, NY
| | - Uri Shalit
- Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Brian Wolpin
- Gastrointestinal Cancer Center, Dana-Farber Cancer Institute, Boston, MA
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15
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Appelbaum L, Cambronero JP, Stevens JP, Horng S, Pollick K, Silva G, Haneuse S, Piatkowski G, Benhaga N, Duey S, Stevenson MA, Mamon H, Kaplan ID, Rinard MC. Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study. Eur J Cancer 2021; 143:19-30. [PMID: 33278770 DOI: 10.1016/j.ejca.2020.10.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 10/15/2020] [Accepted: 10/28/2020] [Indexed: 02/07/2023]
Abstract
AIM Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data. METHODS Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered. RESULTS The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set. CONCLUSION A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.
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Affiliation(s)
- Limor Appelbaum
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - José P Cambronero
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA, 02139, USA.
| | - Jennifer P Stevens
- Beth Israel Deaconess Medical Center, Center for Healthcare Delivery Science, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Steven Horng
- Beth Israel Deaconess Medical Center, Division of Emergency Medicine Informatics, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Karla Pollick
- Beth Israel Deaconess Medical Center, Center for Healthcare Delivery Science, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - George Silva
- Beth Israel Deaconess Medical Center, Center for Healthcare Delivery Science, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Sebastien Haneuse
- Harvard University, T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
| | - Gail Piatkowski
- Beth Israel Deaconess Medical Center, Center for Healthcare Delivery Science, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Nordine Benhaga
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Stacey Duey
- Brigham and Women's Hospital, Partners Research IS and Computing, Information Systems Department, 75 Francis Street, Boston, MA, 02115, USA.
| | - Mary A Stevenson
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Harvey Mamon
- Dana Farber Cancer Institute/Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
| | - Irving D Kaplan
- Beth Israel Deaconess Medical Center, Department of Radiation Oncology, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Martin C Rinard
- Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA, 02139, USA.
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16
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Abstract
PURPOSE OF REVIEW Pancreatic cancer is the third leading cause of cancer death and with a dismal 5-year survival of 10%. Poor survival of pancreatic cancer is mostly due to its presentation and diagnosis at a late stage. The present article aims to update clinicians with recent progress in the field of early detection of pancreatic cancer. RECENT FINDINGS Pancreatic cancer screening is not recommended in the general population due to its low prevalence. In this review, we discuss high-risk groups for pancreatic cancer, including inherited predisposition to pancreatic cancer, new-onset diabetes, mucinous pancreatic cyst, and chronic pancreatitis. We discuss methods of enrichment of high-risk groups with clinical models using electronic health records and biomarkers. We also discuss improvements in imaging modalities and emerging role of machine learning and artificial intelligence in the field of imaging and biomarker to aid in early identification of pancreatic cancer. SUMMARY There are still vast challenges in the field of early detection of pancreatic cancer. We need to develop noninvasive prediagnostic validated biomarkers for longitudinal surveillance of high-risk individuals and imaging modalities that can identify pancreatic cancer early.
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17
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Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection. Pancreas 2020; 49:882-886. [PMID: 32675784 DOI: 10.1097/mpa.0000000000001603] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. Based on several models, it is projected to become the second leading cause of cancer-related deaths by 2030. Although the overall survival rate for patients diagnosed with pancreatic cancer is less than 10%, survival rates are increasing in those whose cancers are detected at an early stage, when intervention is possible. There are, however, no reliable biomarkers or imaging technology that can detect early-stage pancreatic cancer or accurately identify precursors that are likely to progress to malignancy. The Alliance of Pancreatic Cancer Consortia, a virtual consortium of researchers, clinicians, and advocacies focused on early diagnosis of pancreatic cancer, was formed in 2016 to provide a platform and resources to discover and validate biomarkers and imaging methods for early detection. The focus of discussion at the most recent alliance meeting was on imaging methods and the use of artificial intelligence for early detection of pancreatic cancer.
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18
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Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol 2020; 5:698-710. [PMID: 32135127 PMCID: PMC7380506 DOI: 10.1016/s2468-1253(19)30416-9] [Citation(s) in RCA: 292] [Impact Index Per Article: 58.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 10/30/2019] [Accepted: 11/05/2019] [Indexed: 02/07/2023]
Abstract
Pancreatic ductal adenocarcinoma is most frequently detected at an advanced stage. Such late detection restricts treatment options and contributes to a dismal 5-year survival rate of 3-15%. Pancreatic ductal adenocarcinoma is relatively uncommon and screening of the asymptomatic adult population is not feasible or recommended with current modalities. However, screening of individuals in high-risk groups is recommended. Here, we review groups at high risk for pancreatic ductal adenocarcinoma, including individuals with inherited predisposition and patients with pancreatic cystic lesions. We discuss studies aimed at finding ways of identifying pancreatic ductal adenocarcinoma in high-risk groups, such as among individuals with new-onset diabetes mellitus and people attending primary and secondary care practices with symptoms that suggest this cancer. We review early detection biomarkers, explore the potential of using social media for detection, appraise prediction models developed using electronic health records and research data, and examine the application of artificial intelligence to medical imaging for the purposes of early detection.
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Affiliation(s)
- Stephen P Pereira
- Institute for Liver and Digestive Health, University College London, London, UK
| | - Lucy Oldfield
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Alexander Ney
- Institute for Liver and Digestive Health, University College London, London, UK
| | - Phil A Hart
- Division of Gastroenterology, Hepatology, and Nutrition, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Margaret G Keane
- Division of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen J Pandol
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William Greenhalf
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Christie Y Jeon
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Eugene J Koay
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Christopher V Almario
- Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Christopher Halloran
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK
| | - Anne Marie Lennon
- Division of Gastroenterology and Hepatology, Johns Hopkins University, Baltimore, MD, USA
| | - Eithne Costello
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, UK.
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19
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Kaleru T, Vankeshwaram VK, Maheshwary A, Mohite D, Khan S. Diabetes Mellitus in the Middle-Aged and Elderly Population (>45 Years) and Its Association With Pancreatic Cancer: An Updated Review. Cureus 2020; 12:e8884. [PMID: 32742851 PMCID: PMC7388804 DOI: 10.7759/cureus.8884] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Diabetes mellitus (DM) and pancreatic cancer (PC) in the elderly are widely considered to be interrelated. New-onset diabetes (NOD) patients are considered a high-risk group for the development of PC within three years of diagnosis. We reviewed the literature to determine the pathophysiological association between DM and PC, which can help in the development of screening tests for early PC diagnosis in the elderly with NOD. We also studied the potential associations between them after pancreaticoduodenectomy (PD) or pancreatic resection. We collected studies published in the last five years in PubMed that are relevant to DM and PC in the elderly. We mainly focused on the pathophysiology and intracellular mechanisms involved between NOD and PC. We illustrated the clinical signs and immunological and metabolic biomarkers that can be used to diagnose early PC in the elderly with NOD. In the 34 studies we reviewed, five showed that long-term diabetes mellitus (LTDM) increases the risk of PC. Six studies showed that NOD in the elderly is an early sign of PC. Fourteen studies proposed that clinical signs and biomarker levels should be used to determine the high-risk risk group for PC among NOD patients. Six studies reported that NOD is associated with the worst outcomes postoperatively, and three studies showed that patients developed DM after pancreatic resection. LTDM is considered an independent risk factor for PC development in the elderly. NOD is a consequence and maybe the only early presenting sign of PC. Screening protocols and tests should be used in clinical practice to determine the proportion of NOD patients who should undergo further testing for early diagnosis of PC. DM and PC are also co-related postoperatively and patients should be monitored for impaired glucose levels, overall survival, and mortality.
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Affiliation(s)
- Thanmai Kaleru
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | | | - Ankush Maheshwary
- Neurology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
- Medicine, Government Medical College, Amritsar, IND
| | - Divya Mohite
- Neurology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
| | - Safeera Khan
- Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA
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