Application of Magnetic Resonance Imaging in Neoadjuvant Treatment of Pancreatic Ductal Adenocarcinoma

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignant tumors of the human digestive system. Due to its insidious onset, many patients have already lost the opportunity for radical resection upon tumor diagnosis. In recent years, neoadjuvant treatment for patients with borderline resectable PDAC has been recommended by multiple guidelines to increase the resection rate of radical surgery and improve the postoperative survival. However, further developments are required to accurately assess the tumor response to neoadjuvant therapy and to select the population suitable for such treatment. Reductions in drug toxicity and the number of neoadjuvant cycles are also critical. At present, the clinical evaluation of neoadjuvant treatment is mainly based on several serological and imaging indicators; however, the unique characteristics of PDAC and the insufficient sensitivity and specificity of the markers render this system ineffective. The imaging evaluation system, magnetic resonance imaging (MRI), has its own unique imaging advantages compared with computed tomography (CT) and other imaging examinations. One key advantage is the ability to reflect the changes more rapidly in tumor tissue components, such as the degree of fibrosis, microvessel density, and tissue hypoxia. It can also perform multiparameter quantitative analysis of tumor tissue and changes, attributing to its increasingly important role in imaging evaluation, and potentially the evaluation of neoadjuvant treatment of pancreatic cancer, as several current articles have studied. At the same time, owing to the complexity of MRI and some of its limitations, its wider application is limited. Compared with CT imaging, few relevant studies have been conducted. In this review article, we will investigate and summarize the advantages, limitations, and future development of MRI in the evaluation of neoadjuvant treatment of PDAC. Evidence Level 3 Technical Efficacy Stage 2

Preoperative Multiparametric Quantitative Magnetic Resonance Imaging Correlates with Prognosis and Recurrence Patterns in Pancreatic Ductal Adenocarcinoma

“…MRI has advantages over CT in identifying tissue components, and functional magnetic resonance imaging (fMRI) can be quantitatively measured. Quantitative parameters such as the apparent diffusion coefficient (ADC) value measured by single-parameter functional diffusion-weighted magnetic resonance imaging (DWI) have been used in the diagnosis of a variety of tumors [ 7 ], monitoring tumor response to treatment [ 8 , 9 , 10 ], and predicting the degree of malignancy and prognosis [ 11 ]. With the continuous development of quantitative MRI techniques, multiparameter functional intravoxel incoherent motion (IVIM) DWI could provide quantitative parameters which could enable us to better understand the tumor microenvironment and the abundant extracellular matrix components of PDAC.…”

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer

“…MRI is a common and reliable technique for detecting pancreatic cancer without ionizing radiation 9 . Compared to other imaging methods, MP‐MRI has a higher soft tissue resolution and spatial resolution, which allows it to more accurately reflect changes in tissue status and components, such as microvascular formation and hypoxia 10 . However, the use of MRI is currently limited to a preliminary assessment of tumor resectability and it remains challenging to objectively evaluate the pathological indices of tumors based on images.…”

“…9 Compared to other imaging methods, MP-MRI has a higher soft tissue resolution and spatial resolution, which allows it to more accurately reflect changes in tissue status and components, such as microvascular formation and hypoxia. 10 However, the use of MRI is currently limited to a preliminary assessment of tumor resectability and it remains challenging to objectively evaluate the pathological indices of tumors based on images. Currently, radiomics allow to extract numerous crucial features from images, which are difficult to observe with the naked eye.…”