Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65–75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

The combination of stereotactic body radiation therapy (SBRT) plus immune checkpoint inhibitors (ICI) must be explored to treat advanced primary liver tumors such as hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Limited retrospective reviews and case reports/series suggest this combination can be effective and safe in both cancer types. With ICIs moving into the first line (IMbrave 150, HIMALAYA, and TOPAZ-1) to manage these cancers, identifying a suitable population for this approach is challenging. Patients with macrovascular invasion (MVI)-positive HCC (especially if larger veins are involved) or recurrent HCCs post-locoregional therapies (such as transarterial radioembolization (TARE), transarterial chemoembolization (TACE), or ablation), as well as those ineligible for bevacizumab or tyrosine kinase inhibitors (TKIs), should be the focus of exploring this combination in HCC. Unresectable or oligometastatic CCA patients who cannot tolerate gemcitabine/cisplatin (GC) or those who progressed on GC without durvalumab and do not have targetable mutations could also be considered for this approach. In both HCC and CCA disease groups, SBRT plus ICI can be examined post-ICI as these two modalities act synergistically to enhance anti-tumor activity (based on pre-clinical studies). Large-scale randomized trials are needed to identify the subsets of primary liver cancers suitable for this approach and to clearly define its clinical benefit.

Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.

INTRODUCTION: Pathogenic variants (PVs) in the cystic fibrosis transmembrane conductance regulator (CFTR) gene are commonly reported across the spectrum of pancreatitis, including acute (AP), recurrent acute (RAP), and chronic pancreatitis (CP). We aimed to define the pooled prevalence of CFTR PVs according to pancreatitis phenotype. METHODS: A systematic search using synonyms for CFTR and pancreatitis was performed in Embase and Pubmed databases. The primary outcome was the frequency of subjects with at least one CFTR PV among those who underwent germline CFTR testing. Subgroup analyses included age, pancreatitis etiology, and genetic testing strategy. Confidence intervals (CIs) were obtained using the exact binomial method (Clopper-Pearson), and a Sidik-Jonkman random-effects model was used to calculate pooled prevalence. RESULTS: In total, 138 studies were included in the final analysis; 17 (n = 1,873) reported populations with AP, 21 (n = 1,172) with RAP, 86 (n = 13,428) with CP, and 36 (n = 4,521) with unspecified pancreatitis type. The pooled prevalence of at least one CFTR PV was 8.0% (95% CI: 4.3%–14.4%) of AP, 16.4% (95% CI: 10.2%–25.4%) of RAP, 15.3% (95% CI: 12.2%–19.0%) of CP, and 25.0% (95% CI: 17.5%–34.3%) of unspecified pancreatitis. Heterogeneity was high in each phenotype (I² value range 88.3%–96.7%). DISCUSSION: These findings underscore the complex landscape of CFTR PVs in pancreatitis, emphasizing the importance of tailored approaches in addressing this genetic component across diverse patient groups and phenotypic presentations. In addition, these data are useful for pretest genetic counseling and provide a justification for developing CFTR-directed interventions.