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Wang L, Grimshaw AA, Mezzacappa C, Larki NR, Yang YX, Justice AC. Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review. Cancer Epidemiol Biomarkers Prev 2023; 32:1490-1497. [PMID: 37610426 PMCID: PMC10873036 DOI: 10.1158/1055-9965.epi-23-0468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/21/2023] [Accepted: 08/21/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data. METHODS We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors. RESULTS Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1). CONCLUSIONS While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination. IMPACT For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.
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Affiliation(s)
- Louise Wang
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Catherine Mezzacappa
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Navid Rahimi Larki
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Yu-Xiao Yang
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA USA
| | - Amy C. Justice
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- School of Public Health, Yale University, New Haven, CT, USA
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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: 1.0] [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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Matchaba S, Fellague-Chebra R, Purushottam P, Johns A. Early Diagnosis of Pancreatic Cancer via Machine Learning Analysis of a National Electronic Medical Record Database. JCO Clin Cancer Inform 2023; 7:e2300076. [PMID: 37816199 DOI: 10.1200/cci.23.00076] [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: 05/02/2023] [Revised: 07/24/2023] [Accepted: 08/22/2023] [Indexed: 10/12/2023] Open
Abstract
PURPOSE Pancreatic cancer (PaC) is often diagnosed at advanced stages, resulting in one of the lowest survival rates among patients with cancer. The purpose of this study was to investigate whether machine learning (ML) models can predict with high sensitivity and specificity an increased risk for PaC ahead of clinical diagnosis. METHODS Optum deidentified electronic health record (EHR) data set was used to extract 1-year data for each patient and to sample for PaC diagnosis, the number of interactions with the health care system, and unique demographic and clinical features. Data for patients with PaC diagnosis were collected between 1 and 2 years before the diagnosis. Standard binary classification ML models were used on training and testing data sets. Data analyses were performed using the scikit-learn package version 1.0.1. RESULTS The data set consisted of 18,987 patient EHRs collected between December 31, 2007, and December 31, 2017. EHRs with 10 unique features and at least three health care interactions were used for model training (N = 15,189; n = 8,438 [56%] with PaC) and testing (N = 3,798; n = 2,127 [56%] with PaC). The ensemble model achieved an AUC of 0.89, a sensitivity of 85.61%, and a specificity of 76.18% on the testing data set and produced superior results compared with other binary classifiers. Increasing unique health care interactions to nine failed to improve the AUC score. When the testing data set was enlarged to 5,696 patients, the ensemble model achieved an AUC of 0.92 and a specificity of 93.21%, but the sensitivity was compromised. CONCLUSION The ensemble model exceeded the state-of-the-art level of performance for prediction of PaC ahead of clinical diagnosis with a minimal clinically guided input, providing a potential strategy for selection of high-risk patients for further screening.
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Affiliation(s)
- Siyabonga Matchaba
- Health Economics and Evidence Development, Novartis Oncology, East Hanover, NJ
- Mendel, San Jose, CA
| | | | | | - Adam Johns
- Health Economics and Evidence Development, Novartis Oncology, East Hanover, NJ
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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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Tovar DR, Rosenthal MH, Maitra A, Koay EJ. Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer. ARTIFICIAL INTELLIGENCE SURGERY 2023; 3:14-26. [PMID: 37124705 PMCID: PMC10141523 DOI: 10.20517/ais.2022.38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.
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Affiliation(s)
- Daniela R. Tovar
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Anirban Maitra
- Department of Radiology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
| | - Eugene J. Koay
- Department of Gastrointestinal Radiation Oncology, The University of Texas, Anderson Cancer Center, Houston, TX 77030, USA
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Chen W, Butler RK, Lustigova E, Chari ST, Maitra A, Ann Rinaudo J, Wu BU. Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach. J Clin Gastroenterol 2023; 57:103-110. [PMID: 35470312 PMCID: PMC9585151 DOI: 10.1097/mcg.0000000000001710] [Citation(s) in RCA: 3] [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: 11/03/2021] [Accepted: 03/16/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND New-onset diabetes (NOD) has been suggested as an early indicator of pancreatic cancer. However, the definition of NOD by the American Diabetes Association requires 2 simultaneous or consecutive elevated glycemic measures. We aimed to apply a machine-learning approach using electronic health records to predict the risk in patients with recent-onset hyperglycemia. MATERIALS AND METHODS In this retrospective cohort study, health plan enrollees 50 to 84 years of age who had an elevated (6.5%+) glycated hemoglobin (HbA1c) tested in January 2010 to September 2018 with recent-onset hyperglycemia were identified. A total of 102 potential predictors were extracted. Ten imputation datasets were generated to handle missing data. The random survival forests approach was used to develop and validate risk models. Performance was evaluated by c -index, calibration plot, sensitivity, specificity, and positive predictive value. RESULTS The cohort consisted of 109,266 patients (mean age: 63.6 y). The 3-year incidence rate was 1.4 (95% confidence interval: 1.3-1.6)/1000 person-years of follow-up. The 3 models containing age, weight change in 1 year, HbA1c, and 1 of the 3 variables (HbA1c change in 1 y, HbA1c in the prior 6 mo, or HbA1c in the prior 18 mo) appeared most often out of the 50 training samples. The c -indexes were in the range of 0.81 to 0.82. The sensitivity, specificity, and positive predictive value in patients who had the top 20% of the predicted risks were 56% to 60%, 80%, and 2.5% to 2.6%, respectively. CONCLUSION Targeting evaluation at the point of recent hyperglycemia based on elevated HbA1c could offer an opportunity to identify pancreatic cancer early and possibly impact survival in cancer patients.
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Affiliation(s)
- Wansu Chen
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Rebecca K. Butler
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Eva Lustigova
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA
| | - Suresh T. Chari
- Department of Gastroenterology, Hepatology and Nutrition, University of Texas MD Anderson Cancer Center
| | - Anirban Maitra
- Sheikh Ahmed Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center
| | - Jo Ann Rinaudo
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Bechien U. Wu
- Center for Pancreatic Care, Department of Gastroenterology, Los Angeles Medical Center, Southern California Permanente Medical Group, Los Angeles, CA
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Schweber AB, Agarunov E, Brooks C, Hur C, Gonda TA. New-Onset Diabetes Is a Potential Marker for the Malignant Transformation of Pancreatic Cysts: A Real-World Population Cohort Study. Pancreas 2022; 51:1186-1193. [PMID: 37078944 DOI: 10.1097/mpa.0000000000002161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
OBJECTIVES New-onset diabetes mellitus has been shown to be associated with pancreatic cancer (PC) in the general population. Our objective was to leverage real-world data to assess the association of new-onset diabetes (NODM) with malignant transformation in a large longitudinal cohort of pancreatic cyst patients. METHODS A retrospective longitudinal cohort study was conducted using IBM's MarketScan claims databases from 2009 to 2017. From 200 million database subjects, we selected patients with newly diagnosed cysts without prior pancreatic pathology. RESULTS Of the 137,970 patients with a pancreatic cyst, 14,279 had a new diagnosis. Median follow-up was 41.6 months. Patients with NODM progressed to PC at nearly 3 times the rate of patients without a diabetes history (hazard ratio, 2.80; 95% confidence interval, 2.05-3.83) and at a significantly higher rate than patients with preexisting diabetes (hazard ratio, 1.59; 95% confidence interval, 1.14-2.21). The mean interval between NODM and cancer diagnosis was 7.5 months. CONCLUSIONS Cyst patients who developed NODM progressed to PC at 3 times the rate of nondiabetics and at a greater rate than preexisting diabetics. The diagnosis of NODM preceded cancer detection by several months. These results support the inclusion of diabetes mellitus screening in cyst surveillance algorithms.
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Affiliation(s)
- Adam B Schweber
- From the Department of Medicine, Division of Digestive and Liver Diseases, Columbia University Irving Medical Center
| | - Emil Agarunov
- Division of Gastroenterology and Hepatology, New York University
| | | | - Chin Hur
- From the Department of Medicine, Division of Digestive and Liver Diseases, Columbia University Irving Medical Center
| | - Tamas A Gonda
- Division of Gastroenterology and Hepatology, New York University
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Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022; 12:973999. [PMID: 35982967 PMCID: PMC9380440 DOI: 10.3389/fonc.2022.973999] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Purpose We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. Methods Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. Results A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field. Conclusion The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.
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Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Feixiong Zhang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiangkun Meng
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Yu Miao
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | | | - Li Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
| | - Zhaoshen Li
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
- Clinical Medical College, Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
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Scherübl H. Krebsrisiko bei Prädiabetes und Typ-2-Diabetes mellitus. DIABETOL STOFFWECHS 2022. [DOI: 10.1055/a-1837-2035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
ZusammenfassungKrebs ist nun die führende Todesursache bei Typ-2-Diabetes mellitus (T2D). Prädiabetes und T2D erhöhen das Risiko für bestimmte Tumoren. Zu den Prädiabetes- bzw. T2D-assoziierten Malignomen zählen gastrointestinale, gynäkologische, urologische und endokrine Karzinome aber auch Leukämien. Prädiabetes und T2D bedingen eine 1,2- bis 2,7-fach erhöhte Krebssterblichkeit. Zugrundeliegende Mechanismen der Assoziation zwischen T2D und Krebs beinhalten die chronische Hyperglykämie, einen chronischen systemischen Entzündungszustand, oxidativen Stress, Dyslipidämie, die Insulinresistenz sowie chronisch erhöhte Spiegel von insulin-like growth factor 1 (IGF-1) und von Insulin. Eine dauerhafte Gewichtsreduktion kann das Krebsrisiko adipöser T2D-Patienten signifikant senken. Ein gesunder Lebensstil und die regelmäßige Teilnahme an Vorsorgeuntersuchungen sind wichtig und können die Krebsmortalität von Diabetespatienten erheblich verringern.
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Affiliation(s)
- Hans Scherübl
- Klinik für Innere Medizin, Gastroenterologie, GI Onkologie, Diabetologie und Infektiologie, Vivantes Netzwerk für Gesundheit GmbH, Berlin, Germany
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Chatterjee A, Chahal P. Letter to the Editor Regarding Prospective Assessment for Prediabetes and New-Onset Diabetes in High-Risk Individuals Undergoing Pancreatic Cancer Screening. Gastroenterology 2022; 162:1778-1779. [PMID: 34582896 DOI: 10.1053/j.gastro.2021.09.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 09/22/2021] [Indexed: 12/02/2022]
Affiliation(s)
- Arjun Chatterjee
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Prabhleen Chahal
- Department of Gastroenterology and Hepatology, Digestive Disease Institute, Cleveland Clinic, Cleveland, Ohio
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Appelbaum L, Kaplan ID, Palchuk MB, Kundrot S, Winer-Jones JP, Rinard M. Development and Experience with Cancer Risk Prediction Models Using Federated Databases and Electronic Health Records. Digit Health 2022. [DOI: 10.36255/exon-publications-digital-health-federated-databases] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021; 27:7480-7496. [PMID: 34887644 PMCID: PMC8613738 DOI: 10.3748/wjg.v27.i43.7480] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/02/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.
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Affiliation(s)
- Hiromitsu Hayashi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Norio Uemura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Kazuki Matsumura
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Liu Zhao
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hiroki Sato
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yuta Shiraishi
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Yo-ichi Yamashita
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan
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Ke J, Lin T, Liu X, Wu K, Ruan X, Ding Y, Liu W, Qiu H, Tan X, Wang X, Chen X, Li Z, Cao G. Glucose Intolerance and Cancer Risk: A Community-Based Prospective Cohort Study in Shanghai, China. Front Oncol 2021; 11:726672. [PMID: 34527591 PMCID: PMC8435720 DOI: 10.3389/fonc.2021.726672] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/11/2021] [Indexed: 01/24/2023] Open
Abstract
Background Cancer becomes the leading cause of premature death in China. Primary objective of this study was to determine the major risk factors especially glucose intolerance for cancer prophylaxis. Methods A cluster sampling method was applied to enroll 10,657 community-based adults aged 15-92 years in Shanghai, China in 2013. A structured questionnaire and physical examination were applied in baseline survey. Prediabetes was diagnosed using 75-g oral glucose tolerance test. After excluding 1433 subjects including 224 diagnosed with cancer before and 1 year after baseline survey, the remaining 9,224 subjects were followed-up to December 31, 2020. Results A total of 502 new cancer cases were diagnosed. The cancer incidence was 10.29, 9.20, and 5.95/1,000 person-years in diabetes patients, those with prediabetes, and healthy participants, respectively (p<0.001). The multivariate Cox regression analysis indicated that age, prediabetes and diabetes, were associated with an increased risk of cancer in those <65 years, the hazard ratios (95% confidence interval) for prediabetes and diabetes were, 1.49(1.09-2.02) and 1.51(1.12-2.02), respectively. Glucose intolerance (prediabetes and diabetes) were associated with increased risks of stomach cancer, colorectal cancer, and kidney cancer in those <65 years. Anti-diabetic medications reduced the risk of cancer caused by diabetes. The multivariate Cox analysis showed that age, male, <9 years of education, and current smoking were associated with increased risks of cancer in those ≥65 years independently. Conclusions Glucose intolerance is the prominent cancer risk factor in adults <65 years. Lifestyle intervention and medications to treat glucose intolerance help prevent cancer in this population.
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Affiliation(s)
- Juzhong Ke
- Department of Epidemiology, Second Military Medical University, Shanghai, China.,Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Tao Lin
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Xiaolin Liu
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Kang Wu
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Xiaonan Ruan
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Yibo Ding
- Department of Epidemiology, Second Military Medical University, Shanghai, China
| | - Wenbin Liu
- Department of Epidemiology, Second Military Medical University, Shanghai, China
| | - Hua Qiu
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Xiaojie Tan
- Department of Epidemiology, Second Military Medical University, Shanghai, China
| | - Xiaonan Wang
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Xi Chen
- Department of Epidemiology, Second Military Medical University, Shanghai, China
| | - Zhitao Li
- Pudong New Area Center for Disease Control and Prevention, Pudong Institute of Preventive Medicine, Fudan University, Shanghai, China
| | - Guangwen Cao
- Department of Epidemiology, Second Military Medical University, Shanghai, China
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14
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Gallo M, Adinolfi V, Morviducci L, Acquati S, Tuveri E, Ferrari P, Zatelli MC, Faggiano A, Argentiero A, Natalicchio A, D'Oronzo S, Danesi R, Gori S, Russo A, Montagnani M, Beretta GD, Di Bartolo P, Silvestris N, Giorgino F. Early prediction of pancreatic cancer from new-onset diabetes: an Associazione Italiana Oncologia Medica (AIOM)/Associazione Medici Diabetologi (AMD)/Società Italiana Endocrinologia (SIE)/Società Italiana Farmacologia (SIF) multidisciplinary consensus position paper. ESMO Open 2021; 6:100155. [PMID: 34020401 PMCID: PMC8144346 DOI: 10.1016/j.esmoop.2021.100155] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023] Open
Abstract
Pancreatic cancer (PC) is a common cause of cancer-related death, due to difficulties in detecting early-stage disease, to its aggressive behaviour, and to poor response to systemic therapy. Therefore, developing strategies for early diagnosis of resectable PC is critical for improving survival. Diabetes mellitus is another major public health problem worldwide. Furthermore, diabetes can represent both a risk factor and a consequence of PC: nowadays, the relationship between these two diseases is considered a high priority for research. New-onset diabetes can be an early manifestation of PC, especially in a thin adult without a family history of diabetes. However, even if targeted screening for patients at higher risk of PC could be a promising approach, this is not recommended in asymptomatic adults with new-onset diabetes, due to the much higher incidence of hyperglycaemia than PC and to the lack of a safe and affordable PC screening test. Prompted by a well-established and productive multidisciplinary cooperation, the Italian Association of Medical Oncology (AIOM), the Italian Medical Diabetologists Association (AMD), the Italian Society of Endocrinology (SIE), and the Italian Society of Pharmacology (SIF) here review available evidence on the mechanisms linking diabetes and PC, addressing the feasibility of screening for early PC in patients with diabetes, and sharing a set of update statements with the aim of providing a state-of-the-art overview and a decision aid tool for daily clinical practice. The incidence of PC is increasing and its prognosis is very poor; therefore, early detection is fundamental. New-onset diabetes may be an early manifestation of PC, often disappearing after its resection. Screening for PC is not currently recommended among people with new-onset diabetes, due to its high incidence. Thin subjects >50 years old at the time of diabetes onset, with sudden weight loss and severe hyperglycaemia are at higher risk. Currently some clinical models are promising for stratifying cancer risk in people with new-onset diabetes.
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Affiliation(s)
- M Gallo
- Endocrinology and Metabolic Diseases Unit of AO SS Antonio e Biagio e Cesare Arrigo of Alessandria, Alessandria, Italy.
| | - V Adinolfi
- Endocrinology and Diabetology Unit, ASL Verbano Cusio Ossola, Domodossola, Italy
| | - L Morviducci
- Diabetology and Nutrition Unit, Department of Medical Specialities, ASL Roma 1 - S. Spirito Hospital, Rome, Italy
| | - S Acquati
- Endocrinology Unit, Ospedale Pierantoni-Morgagni, Forlì, Italy
| | - E Tuveri
- Diabetology, Endocrinology and Metabolic Diseases Service, ATS Sardegna - ASSL Carbonia-Iglesias, Italy
| | - P Ferrari
- Palliative Care Unit, Istituti Clinici Scientifici Maugeri SPA SB, IRCCS, Pavia, Italy
| | - M C Zatelli
- Section of Endocrinology & Internal Medicine, Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - A Faggiano
- Endocrinology Unit, Department of Clinical & Molecular Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - A Argentiero
- Medical Oncology Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - A Natalicchio
- Department of Emergency and Organ Transplantation, Section of Internal Medicine, Endocrinology, Andrology and Metabolic Diseases, University of Bari Aldo Moro, Bari, Italy
| | - S D'Oronzo
- Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, Bari, Italy
| | - R Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - S Gori
- Oncologia Medica, IRCCS Ospedale Don Calabria-Sacro Cuore di Negrar, Verona, Italy
| | - A Russo
- Department of Surgical, Oncological and Oral Sciences, Section of Medical Oncology, University of Palermo, Palermo, Italy
| | - M Montagnani
- Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, Bari, Italy
| | - G D Beretta
- Medical Oncology Department, Humanitas Gavazzeni, Bergamo, Italy
| | - P Di Bartolo
- Ravenna Diabetes Center, Romagna Diabetes Managed Clinical Network - Romagna Local Health Authority, Ravenna, Italy
| | - N Silvestris
- Medical Oncology Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy; Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, Bari, Italy
| | - F Giorgino
- Department of Emergency and Organ Transplantation, Section of Internal Medicine, Endocrinology, Andrology and Metabolic Diseases, University of Bari Aldo Moro, Bari, Italy
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