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Parker RA, Zhou Y, Puttock EJ, Chen W, Lustigova E, Wu BU. Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study. Abdom Radiol (NY) 2024; 49:1489-1501. [PMID: 38580790 DOI: 10.1007/s00261-024-04271-2] [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] [Revised: 02/25/2024] [Accepted: 02/25/2024] [Indexed: 04/07/2024]
Abstract
PURPOSE Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging. METHODS We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis). RESULTS We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001). CONCLUSION In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.
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Affiliation(s)
- Rex A Parker
- Department of Radiology, University of Kansas Medical Center, 3901 Rainbow Blvd., Mail Stop 4032, Kansas City, KS, 66160, USA.
| | - Yichen Zhou
- Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 2Nd Floor, Pasadena, CA, 91101, USA
| | - Eric J Puttock
- Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 2Nd Floor, Pasadena, CA, 91101, USA
| | - Wansu Chen
- Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 2Nd Floor, Pasadena, CA, 91101, USA
| | - Eva Lustigova
- Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, 2Nd Floor, Pasadena, CA, 91101, USA
| | - Bechien U Wu
- Center for Pancreatic Care, Department of Gastroenterology, Los Angeles Medical Center, Southern California Permanente Medical Group, Los Angeles, CA, USA
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Bogdanski AM, van Hooft JE, Boekestijn B, Bonsing BA, Wasser MNJM, Klatte DCF, van Leerdam ME. Aspects and outcomes of surveillance for individuals at high-risk of pancreatic cancer. Fam Cancer 2024:10.1007/s10689-024-00368-1. [PMID: 38619782 DOI: 10.1007/s10689-024-00368-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/24/2024] [Indexed: 04/16/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths and is associated with a poor prognosis. The majority of these cancers are detected at a late stage, contributing to the bad prognosis. This underscores the need for novel, enhanced early detection strategies to improve the outcomes. While population-based screening is not recommended due to the relatively low incidence of PDAC, surveillance is recommended for individuals at high risk for PDAC due to their increased incidence of the disease. However, the outcomes of pancreatic cancer surveillance in high-risk individuals are not sorted out yet. In this review, we will address the identification of individuals at high risk for PDAC, discuss the objectives and targets of surveillance, outline how surveillance programs are organized, summarize the outcomes of high-risk individuals undergoing pancreatic cancer surveillance, and conclude with a future perspective on pancreatic cancer surveillance and novel developments.
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Affiliation(s)
- Aleksander M Bogdanski
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Bas Boekestijn
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bert A Bonsing
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Martin N J M Wasser
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Derk C F Klatte
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Monique E van Leerdam
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
- Department of Gastrointestinal Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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Abi Nader C, Vetil R, Wood LK, Rohe MM, Bône A, Karteszi H, Vullierme MP. Automatic Detection of Pancreatic Lesions and Main Pancreatic Duct Dilatation on Portal Venous CT Scans Using Deep Learning. Invest Radiol 2023; 58:791-798. [PMID: 37289274 DOI: 10.1097/rli.0000000000000992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
OBJECTIVES This study proposes and evaluates a deep learning method to detect pancreatic neoplasms and to identify main pancreatic duct (MPD) dilatation on portal venous computed tomography scans. MATERIALS AND METHODS A total of 2890 portal venous computed tomography scans from 9 institutions were acquired, among which 2185 had a pancreatic neoplasm and 705 were healthy controls. Each scan was reviewed by one in a group of 9 radiologists. Physicians contoured the pancreas, pancreatic lesions if present, and the MPD if visible. They also assessed tumor type and MPD dilatation. Data were split into a training and independent testing set of 2134 and 756 cases, respectively.A method to detect pancreatic lesions and MPD dilatation was built in 3 steps. First, a segmentation network was trained in a 5-fold cross-validation manner. Second, outputs of this network were postprocessed to extract imaging features: a normalized lesion risk, the predicted lesion diameter, and the MPD diameter in the head, body, and tail of the pancreas. Third, 2 logistic regression models were calibrated to predict lesion presence and MPD dilatation, respectively. Performance was assessed on the independent test cohort using receiver operating characteristic analysis. The method was also evaluated on subgroups defined based on lesion types and characteristics. RESULTS The area under the curve of the model detecting lesion presence in a patient was 0.98 (95% confidence interval [CI], 0.97-0.99). A sensitivity of 0.94 (469 of 493; 95% CI, 0.92-0.97) was reported. Similar values were obtained in patients with small (less than 2 cm) and isodense lesions with a sensitivity of 0.94 (115 of 123; 95% CI, 0.87-0.98) and 0.95 (53 of 56, 95% CI, 0.87-1.0), respectively. The model sensitivity was also comparable across lesion types with values of 0.94 (95% CI, 0.91-0.97), 1.0 (95% CI, 0.98-1.0), 0.96 (95% CI, 0.97-1.0) for pancreatic ductal adenocarcinoma, neuroendocrine tumor, and intraductal papillary neoplasm, respectively. Regarding MPD dilatation detection, the model had an area under the curve of 0.97 (95% CI, 0.96-0.98). CONCLUSIONS The proposed approach showed high quantitative performance to identify patients with pancreatic neoplasms and to detect MPD dilatation on an independent test cohort. Performance was robust across subgroups of patients with different lesion characteristics and types. Results confirmed the interest to combine a direct lesion detection approach with secondary features such as the MPD diameter, thus indicating a promising avenue for the detection of pancreatic cancer at early stages.
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Affiliation(s)
| | | | | | | | | | | | - Marie-Pierre Vullierme
- Department of Radiology, Hospital of Annecy-Genevois, Université Paris-Cité, Paris, France
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Bolan CW, Stauffer J, LeGout JD, Caserta M, Lockwood A, Bowman AW. A narrative review of imaging for pancreas adenocarcinoma: staging, surgical considerations, and surveillance. J Gastrointest Oncol 2023; 14:2260-2272. [PMID: 37969828 PMCID: PMC10643588 DOI: 10.21037/jgo-22-1044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/30/2023] [Indexed: 11/17/2023] Open
Abstract
Background and Objective Pancreas adenocarcinoma is a disease with dire prognosis. Imaging is pivotal to the diagnosis, staging, reassessment, surgical planning, and surveillance of pancreas cancer. The purpose of this paper is to provide the reader an overview of current imaging practices for pancreas adenocarcinoma. Methods A literature search of original papers and reviews through 2022 was performed using the PubMed database. The most current American College of Radiology Appropriateness Criteria and National Comprehensive Cancer Network guidelines on pancreas cancer imaging were also included. Key Content and Findings Multidisciplinary team care at a high-volume institution is instrumental to optimal patient management and outcomes. It is therefore important for all team members to be aware of imaging modality options, strengths, and challenges. Additionally, a high-level understanding of imaging findings is useful clinically. This manuscript provides a current overview of imaging modalities used in the identification and assessment of pancreas adenocarcinoma, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. Imaging findings, including the expected and unexpected, are reviewed to give the novice imager a better understanding. Conclusions This review provides a current overview of imaging for pancreas adenocarcinoma, including strengths and weakness of various imaging modalities; therefore, providing the reader with a robust resource when considering imaging in the management of this disease.
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Affiliation(s)
| | - John Stauffer
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | | | - Amy Lockwood
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
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Stoffel EM, Brand RE, Goggins M. Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk Assessment, Early Detection, and Prevention. Gastroenterology 2023; 164:752-765. [PMID: 36804602 DOI: 10.1053/j.gastro.2023.02.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/23/2023]
Abstract
Pancreatic cancer usually results in poor survival with limited options for treatment, as most affected individuals present with advanced disease. Early detection of preinvasive pancreatic neoplasia and identifying molecular therapeutic targets provide opportunities for extending survival. Although screening for pancreatic cancer is currently not recommended for the general population, emerging evidence indicates that pancreatic surveillance can improve outcomes for individuals in certain high-risk groups. Changes in the epidemiology of pancreatic cancer, experience from pancreatic surveillance, and discovery of novel biomarkers provide a roadmap for new strategies for pancreatic cancer risk assessment, early detection, and prevention.
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Affiliation(s)
- Elena M Stoffel
- Division of Gastroenterology, University of Michigan Medical School, Ann Arbor, Michigan.
| | - Randall E Brand
- Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Michael Goggins
- Departments of Medicine and Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Xu JX, Hu JB, Yang XY, Feng N, Huang XS, Zheng XZ, Rao QP, Wei YG, Yu RS. A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers. Front Oncol 2023; 12:1106525. [PMID: 36727067 PMCID: PMC9885140 DOI: 10.3389/fonc.2022.1106525] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 12/28/2022] [Indexed: 01/17/2023] Open
Abstract
Objective To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively. Methods A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Results Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. Conclusion We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.
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Affiliation(s)
- Jian-Xia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jin-Bao Hu
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiao-Yan Yang
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Na Feng
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiao-Shan Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiao-Zhong Zheng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Qin-Pan Rao
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yu-Guo Wei
- Precision Health Institution, General Electric (GE) Healthcare, Hangzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China,*Correspondence: Ri-Sheng Yu,
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