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1
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Meziani J, Sprij MLJA, Fuhler GM, Bruno MJ, Marchegiani G, Cahen DL. Small cyst size and lack of growth as negative predictors of malignant transformation in low-risk intraductal papillary mucinous neoplasms of the pancreas: A systematic review and meta-analysis. United European Gastroenterol J 2025; 13:7-20. [PMID: 39370669 PMCID: PMC11866309 DOI: 10.1002/ueg2.12666] [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: 06/21/2024] [Accepted: 07/30/2024] [Indexed: 10/08/2024] Open
Abstract
BACKGROUND AND AIM For branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) without worrisome features (WFs) or high-risk stigmata (HRS), current guidelines recommend surveillance. However, these intraductal papillary mucinous neoplasm (IPMNs), especially the small and stable-sized ones, carry a low risk of malignant transformation. Our aim was to assess whether small cyst size and absence of rapid growth provide reassurance against the development of WFs/HRS and malignancy (high-grade dysplasia (HGD) or pancreatic cancer (PC)). METHODS PubMed/Medline, Embase, the Cochrane Library and the Web of Science Core Collection were systematically searched from inception to May 2023 to identify studies investigating surveillance outcomes of low-risk BD-IPMNs. Studies assessing baseline cyst size and/or growth in relation to WFs/HRS and/or HGD/PC were included. The Newcastle-Ottawa scale tool was used to assess study quality. RESULTS Of the 1937 identified manuscripts, 21 studies were eligible for inclusion. The quality of these studies was considered reasonable. A negative association between cyst size and WFs/HRS development was found in 11 out of 13 relevant studies, but only one out of nine studies reported a negative association between size and malignancy. Regarding cyst growth, four out of six studies described a negative association with the development of WFs/HRS, and all six reported a negative association with malignancy. The pooled relative risk (RR) of developing WFs/HRS or malignancy for cysts ≤15 mm was 0.37 (95% CI 0.25-0.57) and the RR of developing malignancy for cyst growth <2-2.5 mm/year was 0.04 (95% CI 0.02-0.09)). CONCLUSION This systematic review and meta-analysis shows that small and stable-sized low-risk BD-IPMNs are associated with a markedly low progression rate, with stable cyst size being the most reassuring feature. Because of substantial heterogeneity in definitions and reported outcome measures, prospective studies are needed to confirm that surveillance of small and stable sized cyst can be de-intensified or even discontinued.
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Affiliation(s)
- Jihane Meziani
- Department of Gastroenterology & HepatologyUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Marloes L. J. A. Sprij
- Department of Gastroenterology & HepatologyUniversity Medical Center RotterdamRotterdamThe Netherlands
- Department of Public HealthUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Gwenny M. Fuhler
- Department of Gastroenterology & HepatologyUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Marco J. Bruno
- Department of Gastroenterology & HepatologyUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Giovanni Marchegiani
- Department of Surgery, Oncology and Gastroenterology (DiSCOG)Hepato Pancreato Biliary and Liver Transplant SurgeryUniversity of PaduaPaduaItaly
| | - Djuna L. Cahen
- Department of Gastroenterology & HepatologyUniversity Medical Center RotterdamRotterdamThe Netherlands
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Koopmann BDM, Dunnewind N, van Duuren LA, Lansdorp-Vogelaar I, Naber SK, Cahen DL, Bruno MJ, de Kok IMCM. The Natural Disease Course of Pancreatic Cyst-Associated Neoplasia, Dysplasia, and Ductal Adenocarcinoma: Results of a Microsimulation Model. Gastroenterology 2023; 165:1522-1532. [PMID: 37633497 DOI: 10.1053/j.gastro.2023.08.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/19/2023] [Accepted: 08/11/2023] [Indexed: 08/28/2023]
Abstract
BACKGROUND & AIMS Estimates on the progression of precursor lesions to pancreatic cancer (PC) are scarce. We used microsimulation modeling to gain insight into the natural disease course of PC and its precursors. This information is pivotal to explore the efficacy of PC screening. METHODS A Microsimulation Screening Analysis model was developed in which pancreatic intraepithelial neoplasms and cysts can evolve from low-grade dysplasia (LGD) to high-grade dysplasia (HGD) to PC. The model was calibrated to Dutch PC incidence data and Japanese precursor prevalence data (autopsy cases without PC) and provides estimates of PC progression (precursor lesion onset and stage duration). RESULTS Mean LGD state durations of cysts and pancreatic intraepithelial neoplasms were 15.8 years and 17.1 years, respectively. Mean HGD state duration was 5.8 years. For lesions that progress to PC, the mean duration was 4.8-4.9 years for LGD lesions and 4.0-4.1 years for HGD lesions. In 13.7% of individuals who developed PC, the HGD state lasted less than 1 year. The probability that an individual at age 50 years developed PC in the next 20 years was estimated to be 1.8% in the presence of any cyst and 6.1% in case of an LGD mucinous cyst. This 20-year PC risk was estimated to be 5.1% for individuals with an LGD pancreatic intraepithelial neoplasm. CONCLUSIONS Mean duration of HGD lesions before development of PC was estimated to be 4.0 years. This implies a window of opportunity for screening, presuming the availability of a reliable diagnostic test. The probability that an LGD cyst will progress to cancer was predicted to be low.
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Affiliation(s)
- Brechtje D M Koopmann
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Niels Dunnewind
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Luuk A van Duuren
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Steffie K Naber
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Djuna L Cahen
- Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marco J Bruno
- Department of Gastroenterology and Hepatology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Inge M C M de Kok
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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3
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Park J, Lim F, Prest M, Ferris JS, Aziz Z, Agyekum A, Wagner S, Gulati R, Hur C. Quantifying the potential benefits of early detection for pancreatic cancer through a counterfactual simulation modeling analysis. Sci Rep 2023; 13:20028. [PMID: 37973858 PMCID: PMC10654404 DOI: 10.1038/s41598-023-46751-3] [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: 08/28/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023] Open
Abstract
The benefits of cancer early detection depend on various factors, including cancer type, screening method performance, stage at diagnosis, and subsequent treatment. Although numerous studies have evaluated the effectiveness of screening interventions for identifying cancer at earlier stages, there is no quantitative analysis that studies the optimal early detection time interval that results in the greatest mortality benefit; such data could serve as a target and benchmark for cancer early detection strategies. In this study, we focus on pancreatic ductal adenocarcinoma (PDAC), a cancer known for its lack of early symptoms. Consequently, it is most often detected at late stages when the 5-year survival rate is only 3%. We developed a PDAC population model that simulates an individual patient's age and stage at diagnosis, while replicating overall US cancer incidence and mortality rates. The model includes "cancer sojourn time," serving as a proxy for the speed of cancer progression, with shorter times indicating rapid progression and longer times indicating slower progression. In our PDAC model, our hypothesis was that earlier cancer detection, potentially through a hypothetical screening intervention in the counterfactual analysis, would yield reduced mortality as compared to a no-screening group. We found that the benefits of early detection, such as increased life-years gained, are greater when the sojourn time is shorter, reaching their maximum when identification is made 4-6 years prior to clinical diagnosis (e.g., when a symptomatic diagnosis is made). However, when early detection occurs even earlier, for example 6-10 years prior to clinical diagnosis, the benefits significantly diminish for shorter sojourn time cancers, and level off for longer sojourn time cancers. Our study clarifies the potential benefits of PDAC early detection that explicitly incorporates individual patient heterogeneity in cancer progression and identifies quantitative benchmarks for future interventions.
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Affiliation(s)
- Jiheum Park
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Francesca Lim
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Matthew Prest
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Jennifer S Ferris
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Zainab Aziz
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Alice Agyekum
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Sophie Wagner
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Roman Gulati
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Chin Hur
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
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4
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Koopmann BDM, Omidvari AH, Lansdorp-Vogelaar I, Cahen DL, Bruno MJ, de Kok IMCM. The impact of pancreatic cancer screening on life expectancy: A systematic review of modeling studies. Int J Cancer 2023; 152:1570-1580. [PMID: 36444505 PMCID: PMC10107819 DOI: 10.1002/ijc.34379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 10/09/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Evidence supporting the effectiveness of pancreatic cancer (PC) screening is scant. Most clinical studies concern small populations with short follow-up durations. Mathematical models are useful to estimate long-term effects of PC screening using short-term indicators. This systematic review aims to evaluate the impact of PC screening on life expectancy (LE) in model-based studies. Therefore, we searched four databases (Embase, Medline, Web-of-science, Cochrane) until 30 May 2022 to identify model-based studies evaluating the impact of PC screening on LE in different risk populations. Two authors independently screened identified papers, extracted data and assessed the methodological quality of studies. A descriptive analysis was performed and the impact of screening strategies on LE of different risk groups was reported. Our search resulted in 419 studies, of which eight met the eligibility criteria (mathematical model, PC screening, LE). Reported relative risks (RR) for PC varied from 1 to 70. In higher risk individuals (RR > 5), annual screening (by imaging with 56% sensitivity for HGD/early stage PC) predicted to increase LE of screened individuals by 20 to 260 days. In the general population, one-time PC screening was estimated to decrease LE (2-110 days), depending on the test characteristics and treatment mortality risk. In conclusion, although the models use different and sometimes outdated or unrealistic assumptions, it seems that PC screening in high-risk populations improves LE, and that this gain increases with a higher PC risk. Updated model studies, with data from large clinical trials are necessary to predict the long-term effect of PC screening more accurately.
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Affiliation(s)
- Brechtje D M Koopmann
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands.,Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Amir-Houshang Omidvari
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Iris Lansdorp-Vogelaar
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Djuna L Cahen
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Marco J Bruno
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Inge M C M de Kok
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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5
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Owens L, Gulati R, Etzioni R. Stage Shift as an Endpoint in Cancer Screening Trials: Implications for Evaluating Multicancer Early Detection Tests. Cancer Epidemiol Biomarkers Prev 2022; 31:1298-1304. [PMID: 35477176 PMCID: PMC9250620 DOI: 10.1158/1055-9965.epi-22-0024] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/30/2022] [Accepted: 04/14/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Disease-specific mortality is a consensus endpoint in cancer screening trials. New liquid biopsy-based screening tests, including multi-cancer early detection (MCED) tests, are creating a need to reduce the typically lengthy screening trial process. Endpoints based on the reduction in late-stage disease (stage shift) have been proposed but it is unclear how well they predict the impact of screening on disease-specific mortality across a variety of cancers potentially detectable by MCED tests. METHODS We develop a mathematical formulation relating the reduction in late-stage cancer to the expected reduction in disease-specific mortality if cases diagnosed early via screening receive a corresponding shift in mortality. We investigate the similarity between the expected mortality reduction and the observed mortality reduction in published trials of screening for breast, lung, ovarian, and prostate cancer. RESULTS The expected mortality reduction for a given stage shift varies significantly depending on cancer- and stage-specific survival distributions, with some cancer types showing little possibility for mortality improvement even under substantial stage shift. The expected mortality reduction fails to consistently match the mortality outcomes of published trials. CONCLUSIONS In MCED, any mortality benefit is likely to vary substantially across target cancers. Stage shift does not appear to be a reliable basis for inference about mortality reduction across cancers potentially detectable by MCED tests. IMPACT Stage shift may be an appealing endpoint for evaluation of cancer screening tests but it appears to be an unreliable predictor of mortality benefit; furthermore, the same stage shift can mean different things for different cancers.
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Affiliation(s)
- Lukas Owens
- Program in Biostatistics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center
| | - Roman Gulati
- Program in Biostatistics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center
| | - Ruth Etzioni
- Program in Biostatistics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center
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6
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Rabiei R, Ayyoubzadeh SM, Sohrabei S, Esmaeili M, Atashi A. Prediction of Breast Cancer using Machine Learning Approaches. J Biomed Phys Eng 2022; 12:297-308. [PMID: 35698545 PMCID: PMC9175124 DOI: 10.31661/jbpe.v0i0.2109-1403] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 03/05/2022] [Indexed: 05/27/2023]
Abstract
BACKGROUND Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. OBJECTIVE This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. MATERIAL AND METHODS In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. RESULTS RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. CONCLUSION Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
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Affiliation(s)
- Reza Rabiei
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran
| | - Solmaz Sohrabei
- MSc, Department Deputy of Development, Management and Resources, Office of Statistic and Information Technology Management, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Marzieh Esmaeili
- PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran
| | - Alireza Atashi
- PhD, Department of E-Health, Virtual School, Tehran University of Medical Sciences, Medical Informatics Research Group, Clinical Research Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
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7
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Petrov MS. Post-pancreatitis diabetes mellitus and excess intra-pancreatic fat deposition as harbingers of pancreatic cancer. World J Gastroenterol 2021; 27:1936-1942. [PMID: 34007131 PMCID: PMC8108030 DOI: 10.3748/wjg.v27.i17.1936] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/02/2021] [Accepted: 04/09/2021] [Indexed: 02/06/2023] Open
Abstract
Progress in the fight against pancreatic cancer has been hampered by many factors. One of them is the inability to detect the disease early in overwhelming majority of patients. The present paper outlines a novel way in which progress could be accelerated. This includes a focus on two harbingers—post-pancreatitis diabetes mellitus and excess intra-pancreatic fat deposition—that converge at affecting the tumor macroenvironment and microenvironment specifically in the pancreas, not other organs. The two entities have the potential to be incorporated into future screening strategies with a view to early detecting of pancreatic cancer.
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Affiliation(s)
- Maxim S Petrov
- School of Medicine, The University of Auckland, Auckland 1142, New Zealand
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8
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Koopmann BDM, Harinck F, Kroep S, Konings ICAW, Naber SK, Lansdorp-Vogelaar I, Fockens P, van Hooft JE, Cahen DL, van Ballegooijen M, Bruno MJ, de Kok IMCM. Identifying key factors for the effectiveness of pancreatic cancer screening: A model-based analysis. Int J Cancer 2021; 149:337-346. [PMID: 33644856 PMCID: PMC8251934 DOI: 10.1002/ijc.33540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/22/2021] [Accepted: 02/12/2021] [Indexed: 12/14/2022]
Abstract
Pancreatic cancer (PC) survival is poor, as detection usually occurs late, when treatment options are limited. Screening of high‐risk individuals may enable early detection and a more favorable prognosis. Knowledge gaps prohibit establishing the effectiveness of screening. We developed a Microsimulation Screening Analysis model to analyze the impact of relevant uncertainties on the effect of PC screening in high‐risk individuals. The model simulates two base cases: one in which lesions always progress to PC and one in which indolent and faster progressive lesions coexist. For each base case, the effect of annual and 5‐yearly screening with endoscopic ultrasonography/magnetic resonance imaging was evaluated. The impact of variance in PC risk, screening test characteristics and surgery‐related mortality was evaluated using sensitivity analyses. Screening resulted in a reduction of PC mortality by at least 16% in all simulated scenarios. This reduction depended strongly on the natural disease course (annual screening: −57% for “Progressive‐only” vs −41% for “Indolent Included”). The number of screen and surveillance tests needed to prevent one cancer death was impacted most by PC risk. A 10% increase in test sensitivity reduced mortality by 1.9% at most. Test specificity is important for the number of surveillance tests. In conclusion, screening reduces PC mortality in all modeled scenarios. The natural disease course and PC risk strongly determines the effectiveness of screening. Test sensitivity seems of lesser influence than specificity. Future research should gain more insight in PC pathobiology to establish the true value of PC screening in high‐risk individuals.
What's new?
About 10 percent of pancreatic cancers occur in individuals with inherited risk factors. While screening such high‐risk individuals can facilitate the detection of precursor lesions and early‐stage cancer, the extent to which benefits outweigh harms, including overdiagnosis, remains unknown. Here, using a microsimulation model, the authors explored uncertainties concerning the early detection of pancreatic cancer and analyzed the impact of these uncertainties on the effect of screening. In all simulated scenarios, screening was associated with reduced pancreatic cancer mortality. The effectiveness of screening was most strongly impacted by characteristics of natural disease course and level of pancreatic cancer risk
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Affiliation(s)
- Brechtje D M Koopmann
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Femme Harinck
- Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sonja Kroep
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ingrid C A W Konings
- Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Steffie K Naber
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Iris Lansdorp-Vogelaar
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Paul Fockens
- Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanin E van Hooft
- Department of Gastroenterology & Hepatology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Djuna L Cahen
- Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marjolein van Ballegooijen
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marco J Bruno
- Department of Gastroenterology & Hepatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Inge M C M de Kok
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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