Establishment and Verification of a Scoring Model for the Differential Diagnosis of Pancreatic Cancer and Chronic Pancreatitis

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

Diagnostic Differentiation between Pancreatitis and Pancreatic Cancer: A Scoping Review

Pancreatitis, encompassing acute and chronic forms, and pancreatic cancer pose significant challenges to the exocrine tissue of the pancreas. Recurrence rates and complications following acute pancreatitis episodes can lead to long-term risks, including diabetes mellitus. Chronic pancreatitis can develop in approximately 15% of cases, regardless of the initial episode's severity. Alcohol-induced pancreatitis, idiopathic causes, cigarette smoking, and hereditary pancreatitis contribute to the progression to chronic pancreatitis. Chronic pancreatitis is associated with an increased risk of pancreatic cancer, with older age at onset and smoking identified as risk factors. This scoping review aims to synthesise recent publications (2017-2022) on the diagnostic differentiation between pancreatitis and pancreatic cancer while identifying knowledge gaps in the field. The review focuses on biomarkers and imaging techniques in individuals with pancreatitis and pancreatic cancer. Promising biomarkers such as faecal elastase-1 and specific chemokines offer non-invasive ways to assess pancreatic insufficiency and detect early biomarkers for chronic pancreatitis. Imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), and positron emission tomography (PET), aid in differentiating between chronic pancreatitis and pancreatic cancer. However, accurately distinguishing between the two conditions remains a challenge, particularly when a mass is present in the head of the pancreas. Several knowledge gaps persist despite advancements in understanding the association between pancreatitis and pancreatic cancer, including the correlation between histopathological grading systems, non-invasive imaging techniques, and biomarkers in chronic pancreatitis to determine the risk of progression to pancreatic cancer, as well as differentiating between the two conditions. Further research is necessary to enhance our understanding of these aspects, which can ultimately improve the diagnosis and management of pancreatitis and pancreatic cancer.

Pituitary adenomas in patients with multiple endocrine neoplasia type 1: a single-center experience in China

PURPOSE

To explore the clinical characteristics of pituitary adenomas in patients with MEN1 and to summarize treatment strategies for MEN1 in a Chinese population.

METHODS

We retrospectively analyzed 54 MEN1 patients with pituitary adenomas diagnosed at Peking Union Medical College Hospital from March 2003 to January 2017. Clinical data, laboratory testing results, treatments of involved glands and treatment responses were collected and analyzed.

RESULTS

The mean age at pituitary adenoma diagnosis was 53.9 ± 17.8. The patients initially consulted the Endocrinology, General Surgery and Neurosurgery departments, in descending frequency. The nonfunctioning adenoma, prolactinoma, GH-secreting adenoma, cosecreting adenoma, and ACTH-secreting adenoma subtypes accounted for 48.1%, 27.8%, 9.3%, 9.3% and 5.6% of the cases, respectively. The remission rate for prolactinomas was 46.2% (6/13) treated with bromocriptine. And the remission rates were 87.5% (7/8) and 100% (3/3) for GH-secreting adenomas and ACTH-secreting adenomas respectively achieved by transsphenoidal surgery. Nineteen (35.2%) patients with asymptomatic nonfunctioning pituitary adenomas showed no progression after a 35-month follow-up with close observation. Regarding treatment priority, patients with thymic carcinoid tumors received first-line surgery, 54% of the patients with enteropancreatic tumors had these tumors treated first, and 26% of all patients had their pituitary adenomas treated first. In acromegalic patients, pituitary lesions tended to be treated first (75%, p = 0.002). PHPT and adrenocortical adenomas can be managed with elective surgery.

CONCLUSIONS

The treatment of MEN1 requires cooperation between multidisciplinary teams. Individualized treatment according to the severity of glandular involvement is needed. GH-secreting and ACTH-secreting pituitary adenomas require active treatment, while nonfunctioning pituitary adenomas can be observed closely.