Pancreaticoduodenal Groove: Spectrum of Disease and Imaging                     Features

Bello, Hernan R; Sekhar, Aarti; Filice, Ross W.; Radmard, Amir Reza; Davarpanah, Amir H.

doi:10.1148/rg.210168

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…It is predicted that PC will become the third biggest cause of cancer mortalities in European countries by 2025 [ 4 ]. Because the pancreas is located in the retroperitoneum, its location is deep, the early symptoms of PC are not obvious, the opportunity for early diagnosis and treatment is often missed, and the five-year survival rate is below 10% [ 5 , 6 , 7 ]. Surgery is currently the only curative method available, but only about 20% of patients are suitable for radical surgery, with a five-year survival rate of about 21% following surgery; in the two years following surgery, over 80% of patients will have a recurrence or metastasis [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Shi

Zhou

et al. 2023

JPM

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The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

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Section: Introductionmentioning

confidence: 99%

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Shi

Zhou

et al. 2023

JPM

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Groove pancreatitis: From enigma to future directions—A comprehensive review

Dahiya,

Shah,

Canakis

et al. 2024

J of Gastro and Hepatol

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Groove pancreatitis (GP) is a rare and clinically distinct form of chronic pancreatitis affecting the pancreaticoduodenal groove comprising the head of the pancreas, duodenum, and the common bile duct. It is more prevalent in individuals in their 4–5th decade of life and disproportionately affects men compared with women. Excessive alcohol consumption, tobacco smoking, pancreatic ductal stones, pancreatic divisum, annular pancreas, ectopic pancreas, duodenal wall thickening, and peptic ulcers are significant risk factors implicated in the development of GP. The usual presenting symptoms include severe abdominal pain, nausea, vomiting, diarrhea, weight loss, and jaundice. Establishing a diagnosis of GP is often challenging due to significant clinical and radiological overlap with numerous benign and malignant conditions affecting the same anatomical location. This can lead to a delay in initiation of treatment leading to increasing morbidity, mortality, and complication rates. Promising research in artificial intelligence (AI) has garnered immense interest in recent years. Due to its widespread application in diagnostic imaging with a high degree of sensitivity and specificity, AI has the potential of becoming a vital tool in differentiating GP from pancreatic malignancies, thereby preventing a missed or delayed diagnosis. In this article, we provide a comprehensive review of GP, covering the etiology, pathogenesis, clinical presentation, radiological and endoscopic evaluation, management strategies, and future directions. This article also aims to increase awareness about this lesser known and often‐misdiagnosed clinical entity amongst clinicians to ultimately improve patient outcomes.

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