Progress in early detection and screening methods for pancreatic cancer

By Vijay Kumar MalesuReviewed by Lily Ramsey, LLMNov 11 2024

New screening technologies aim to improve survival rates and quality of life through earlier diagnosis of pancreatic cancer.

Study: Advances in Screening and Early Diagnosis of Pancreatic Cancer. Image Credit: mi_viri/Shutterstock.com

In a recent study published in the Cancer Screening and Prevention, a group of researchers provided a comprehensive overview of recent advancements in screening and early diagnostic strategies for pancreatic cancer (PC).

Background

PC often progresses rapidly and presents with subtle symptoms, leading to late-stage diagnoses and poor survival rates despite advancements in oncology. In 2024, it ranked as the 4th leading cause of cancer death in the United States (U.S.), with an estimated 66,440 new cases and 51,750 deaths.

China's incidence rates highlight the immense medical and socioeconomic burdens of PC, with significant healthcare costs and hospitalizations, especially in regions with aging populations.

Early detection improves survival outcomes significantly, yet challenges persist due to a lack of reliable biomarkers, specific screening protocols, and trained personnel. Further research is needed to enhance early screening effectiveness.

Epidemiological burden and risk factors

Ethnic and regional factors influence PC prevalence, with studies indicating increased susceptibility in Asian populations, particularly among individuals with a history of gallstones or Crohn’s disease.

China's significant incidence rate and the associated healthcare expenses highlight PC’s substantial socioeconomic impact. Patients with PC require extensive medical resources, resulting in high hospitalization rates and costs compared to other cancers.

Given that early detection is linked to better outcomes, a focus on identifying at-risk populations and implementing preventive strategies is essential.

Challenges in early detection

The lack of specific symptoms and risk factors complicates the early detection of PC. Many biomarkers lack sensitivity, while advanced imaging techniques like endoscopic ultrasonography (EUS) require specialized operators, often leading to long wait times.

The biomarker carbohydrate antigen 19-9 (CA19-9), though commonly used in clinics, has limited specificity for early-stage PC detection. Consequently, more reliable biomarkers and accessible diagnostic methods are needed to address these gaps and enable effective large-scale screening.

Advances in imaging techniques

Recent advancements in radiology, such as high-resolution computed tomography (CT) and magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI), have improved early lesion detection in the pancreas. EUS, especially contrast-enhanced EUS, allows for effective imaging and differential diagnosis, enhancing sensitivity and specificity.

Despite these improvements, challenges remain, including a shortage of trained personnel and limited accessibility, which hinder widespread clinical implementation. These imaging techniques are instrumental in providing clinicians with valuable information to identify early-stage PC.

Emerging biomarkers and liquid biopsy

Biomarker research has grown significantly, with a focus on identifying non-invasive markers like circulating tumor deoxyribonucleic acid (ctDNA), tumor ribonucleic acid (RNA), and exosomes.

Liquid biopsy is a promising method, allowing for the detection of tumor-derived molecules in blood and other body fluids, aiding both early diagnosis and treatment monitoring.

Studies suggest that combining CA19-9 with additional biomarkers, such as methylated DNA markers, may increase diagnostic sensitivity for early-stage PC, supporting efforts to refine and expand biomarker panels for more reliable screening protocols.

Role of artificial intelligence (AI) in early diagnosis

The integration of AI has revolutionized early diagnosis in PC by improving the accuracy of imaging and biomarker analysis. AI algorithms can analyze complex datasets from imaging studies, detecting patterns and subtle morphological changes that may elude human observers.

Machine learning models, trained with large datasets of CT and MRI scans, demonstrate higher sensitivity for detecting early lesions compared to traditional methods.

AI has also shown promise in analyzing liquid biopsy data, identifying real-time biomarkers, and predicting patient risk, thus enhancing the potential for early intervention.

EUS and AI assistance

EUS remains a powerful tool for visualizing pancreatic lesions, but challenges persist due to the similarity in appearance between benign and malignant lesions. AI-assisted EUS has demonstrated high accuracy in detecting PC, with algorithms helping to differentiate between lesion types.

For small lesions, AI can enhance EUS interpretation, reducing the likelihood of misdiagnosis. As AI models become more refined, they could support endoscopists by providing a secondary analysis, thus bridging the experience gap among operators and improving diagnostic consistency.

Screening high-risk individuals

Screening the general population for PC is impractical due to the disease’s low prevalence and high costs. Instead, targeting high-risk groups, such as individuals with a family history of PC or genetic predispositions, is more feasible.

Genetic mutations in Breast cancer (BRCA)1, BRCA2, and other genes have been linked to increased PC risk, and incorporating genetic testing could identify individuals who would benefit from routine monitoring.

Environmental and lifestyle factors, including smoking, obesity, and chronic pancreatitis, also play a role in PC risk, underscoring the importance of a comprehensive risk assessment strategy to identify those at increased risk.

Conclusions

To summarize, advancements in imaging, biomarker discovery, and AI are transforming PC screening and diagnosis. Though challenges in cost, accessibility, and ethics remain, ongoing research offers hope for improved early detection and patient outcomes.

Interdisciplinary collaboration and technology integration are vital to bringing these innovations into clinical practice effectively.