Magnetic resonance (MR) for mural nodule detection studying Intraductal papillary mucinous neoplasms (IPMN) of pancreas: Imaging-pathologic correlation

Imaging of the Liver and Pancreas: The Added Value of MRI

MR is a powerful diagnostic tool in the diagnosis and management of most hepatic and pancreatic diseases. Thanks to its multiple sequences, the use of dedicated contrast media and special techniques, it allows a multiparametric approach able to provide both morphological and functional information for many pathological conditions. The knowledge of correct technique is fundamental in order to obtain a correct diagnosis. In this paper, different MR sequences will be illustrated in the evaluation of liver and pancreatic diseases, especially those sequences which provide information not otherwise obtainable with other imaging techniques. Practical MR protocols with the most common indications of MR in the study of the liver and pancreas are provided.

Towards a Simplified and Cost-Effective Diagnostic Algorithm for the Surveillance of Intraductal Papillary Mucinous Neoplasms (IPMNs): Can We Save Contrast for Later?

The increased detection of pancreatic cysts in recent years has triggered extensive diagnostic investigations to clarify their potential risk of malignancy, resulting in a large number of patients undergoing numerous imaging follow-up studies for many years. Therefore, there is a growing need for optimization of the current surveillance protocol to reduce both healthcare costs and waiting lists, while still maintaining appropriate sensibility and specificity. Imaging is an essential tool for evaluating patients with intraductal papillary mucinous neoplasms (IPMNs) since it can assess several predictors for malignancy and thus guide further management recommendations. Although contrast-enhanced magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP) has been widely recommended by most international guidelines, recent results support the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool in patients with IPMN. In fact, A-MRI has shown high diagnostic performance in malignant detection, with high sensitivity and specificity as well as excellent interobserver agreement. The aim of this paper is, therefore, to discuss the current available evidence on whether the implementation of an abbreviated-MRI (A-MRI) protocol for cystic pancreatic lesion surveillance could improve healthcare economics and reduce waiting lists in clinical practice without significantly reducing diagnostic accuracy.

Risk Models for Pancreatic Cyst Diagnosis

The overall prevalence of pancreatic cysts (PCs) is high in the general population. In clinical practice PCs are often incidentally discovered and are classified into benign, premalignant, and malignant lesions according to the World Health Organization. For this reason, in the absence of reliable biomarkers, to date clinical decision-making relies mostly on risk models based on morphological features. The aim of this narrative review is to present the current knowledge regarding PC's morphologic features with related estimated risk of malignancy and discuss available diagnostic tools to minimize clinically relevant diagnostic errors.

MRI: Evaluating the Application of FOCUS-MUSE Diffusion-Weighted Imaging in the Pancreas in Comparison With FOCUS, MUSE, and Single-Shot DWIs

BACKGROUND

Diffusion-weighted imaging (DWI) is a useful technique to detect pancreatic lesion. In DWIs, field-of-view optimized and constrained undistorted single-shot (FOCUS) can improve the spatial resolution and multiplexed sensitivity-encoding (MUSE) can gain a high signal-to-noise ratio (SNR). Based on the advantage of FOCUS and MUSE, a new DWI sequence-named FOCUS-MUSE DWI (FOCUS combined with MUSE)-was developed to delineate the pancreas.

PURPOSE

To investigate the reliability of FOCUS-MUSE DWI compared to FOCUS, MUSE and single-shot (SS) DWI via the systematical evaluation of the apparent diffusion coefficient (ADC) measurements, SNR and image quality.

STUDY TYPE

Prospective.

SUBJECTS

A total of 33 healthy volunteers and 9 patients with pancreatic lesion.

FIELD STRENGTH/SEQUENCE

A 3.0 T scanner. FOCUS-MUSE DWI, FOCUS DWI, MUSE DWI, SS DWI.

ASSESSMENT

For volunteers, ADC and SNR were measured by two readers in the pancreatic head, body, and tail. For all subjects, the diagnostic image quality score was assessed by three other readers on above four DWIs.

STATISTICAL TESTS

Paired-sample T-test, intraclass correlation (ICC), Bland-Altman method, Friedman test, Dunn-Bonferroni post hoc test and kappa coefficient. A significance level of 0.05 was used.

RESULTS

FOCUS-MUSE DWI had the best intersession repeatability of ADC measurements (head: 59.53, body: 101.64, tail: 42.30) among the four DWIs, and also maintained the significantly highest SNR (reader 1 [head: 19.68 ± 3.23, body: 23.42 ± 5.00, tail: 28.85 ± 4.96], reader 2 [head: 19.93 ± 3.52, body: 23.02 ± 5.69, tail: 29.77 ± 6.33]) except for MUSE DWI. Furthermore, it significantly achieved better image quality in volunteers (median value: 4 score) and 9 patients (most in 4 score).

DATA CONCLUSION

FOCUS-MUSE DWI improved the reliability of pancreatic images with the most stable ADC measurement, best image quality score and sufficient SNR among four DWIs.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

Simple Summary

With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine.

Abstract

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

Pathology of intraductal papillary mucinous neoplasms

BACKGROUND

Intraductal papillary mucinous neoplasms (IPMNs) represent a unique opportunity to treat and prevent a curable neoplasm before it has the chance to progress to incurable cancer. This prospect, however, has to be balanced with the real risk of over treating patients with lesions that would, in fact, never progress during the life of the patient.

PURPOSE

Informed clinical decisions in the treatment of IPMNs are first and foremost based on a deep understanding of the pathology of these lesions.

CONCLUSIONS

Here we review the pathology of IPMNs, with an emphasis on the clinical relevance of the important features that characterize these lesions.