Innovative Techniques and Imaging Endpoints Transforming Pancreatic Cancer in Clinical Trials
Imaging plays a crucial role in pancreatic cancer clinical trials, particularly in the early diagnosis, monitoring, and treatment response evaluation. Often referred to as the “Silent Killer,” it is a highly aggressive malignancy that often presents asymptomatically in its early stages, leading to delayed diagnoses. CT, MRI, endoscopic ultrasound, and ERCP for biopsy aid in assessment, while CA19-9 remains the primary biomarker despite limitations in specificity and sensitivity.
Pancreatic cancer (PC) is the fourth leading cause of cancer-related deaths globally, with Pancreatic ductal adenocarcinoma (PDAC) being the most common and aggressive type. The 5-year survival rate for pancreatic cancer is less than 5%, with PDAC leading to a staggering 98% life expectancy loss. PC, like other solid tumors, arises from the accumulation of oncogenic driver mutations, notably KRAS (G12D) and TP53, which drive progression from pancreatic intraepithelial neoplasia to invasive adenocarcinoma. The disease’s complex tumor microenvironment, comprised of cancer-associated fibroblasts, macrophages, and regulatory T cells, facilitates tumor growth and metastasis, creating barriers to effective treatment.
Imaging Endpoints (IE) is at the forefront of enhancing imaging modalities for pancreatic cancer detection, crucial for improving clinical trial outcomes. While Contrast-Enhanced Computed Tomography (CECT) is the standard for initial evaluations, it often misses small lesions (<2 cm). In these cases, contrast-enhanced MRI has proven to be a valuable alternative, offering superior sensitivity and diagnostic accuracy. Techniques like diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) significantly enhance lesion identification, with DWI demonstrating 96% sensitivity and 99% specificity for pancreatic ductal adenocarcinoma (PDAC).
Imaging Endpoints is also dedicated to advancing radiomic technologies for early detection of PC as well as the detection of pre-cancerous areas of risk that are not yet visible on imaging but may be detectable by utilizing advanced radiomic analysis. Convolutional neural networks and other deep learning architectures are increasingly used to extract hierarchical “deep” features. Models that fuse multiple imaging modalities have shown promise in distinguishing early-stage PDAC from pancreatitis or benign cystic lesions. And radiomics models are being developed to identify early pathologic changes in intraductal papillary mucinous neoplasms and other precursor lesions before they progress to invasive cancer. Future directions are combining radiomics with circulating tumor DNA, proteomics, or other -omics data for comprehensive, noninvasive assessments of tumor biology.
Immunotherapy strategies are emerging to improve patient responses by targeting specific interactions in tumor microenvironment, with drugs like Dostarlimab and pembrolizumab now approved for certain advanced PDAC cases. However, the challenge of “pseudoprogression” complicates treatment response assessments, as traditional RECIST criteria may not suffice. To address this, iRECIST guidelines allow for new lesions to be categorized as unconfirmed progression with strict follow-up. Recent studies highlight that metabolic metrics from PET/MRI, and morphological metrics from CT, can effectively evaluate treatment responses and predict survival outcomes in PDAC patients. IE’s commitment to clinical trial endpoints ensures that such trials are equipped with the best tools to yield meaningful results and improve patient care.
Imaging Endpoints is the global leader in designing the imaging requirements for clinical trial protocols in Pancreatic cancer and has an impeccable inspection record and an exceptional 95% marketing authorization success rate across all clinical trials we have supported. For more information,
