Pancreatic Cancer Imaging: A New Look at an Old Problem

Early detection of pancreatic cancer in the era of precision medicine

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Confined growth of Ag nanogap shells emitting stable Raman label signals for SERS liquid biopsy of pancreatic cancer

To develop a reliable surface-enhanced Raman scattering (SERS) immunoassay as a new liquid biopsy modality, SERS nanoprobes emitting strong and stable signals are necessary. However, Ag nanoparticles used as SERS nanoprobes are prone to rapid fading of SERS signals by oxidation. This has driven the development of a new strategy for Ag-based SERS nanoprobes emitting stable and strong SERS signals over time. Herein, Ag nanogap shells entrapping Raman labels are created in the confined pores of mesoporous silica nanoparticles (AgNSM) through a rapid single-step reaction for SERS liquid biopsy. Each AgNSM nanoprobe possesses multiple nanogaps of 1.58 nm to entrap Raman labels, allowing superior long-term SERS signal stability and large enhancement of 1.5 × 106. AgNSM nanoprobes conjugated with an antibody specific for carbohydrate antigen (CA)19-9 are employed in the SERS sandwich immunoassay including antibody-conjugated magnetic nanoparticles for CA19-9 detection, showing a two orders of magnitude lower limit of detection (0.025 U mL-1) than an enzyme-linked immunosorbent assay (0.3 U mL-1). The AgNSM nanoprobe immunoassay accurately quantifies CA19-9 levels from clinical serum samples of early and advanced pancreatic cancer. AgNSM nanoprobes with stable SERS signals provide a new route to SERS liquid biopsy for effective detection of blood biomarkers.

3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC's challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer

Pancreatic cancer is currently the third leading cause of cancer death in the United States. The clinical hallmarks of this disease include abdominal pain that radiates to the back, the presence of a hypoenhancing intrapancreatic lesion on imaging, and widespread liver metastases. Technologies such as tissue clearing and three-dimensional (3D) reconstruction of digitized serially sectioned hematoxylin and eosin-stained slides can be used to visualize large (up to 2- to 3-centimeter cube) tissues at cellular resolution. When applied to human pancreatic cancers, these 3D visualization techniques have provided novel insights into the basis of a number of the clinical characteristics of this disease. Here, we describe the clinical features of pancreatic cancer, review techniques for clearing and the 3D reconstruction of digitized microscope slides, and provide examples that illustrate how 3D visualization of human pancreatic cancer at the microscopic level has revealed features not apparent in 2D microscopy and, in so doing, has closed the gap between bench and bedside. Compared with animal models and 2D microscopy, studies of human tissues in 3D can reveal the difference between what can happen and what does happen in human cancers.

Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.

Clinical Study of Anti-PD-1 Immunotherapy Combined with Gemcitabine Chemotherapy in Multiline Treatment of Advanced Pancreatic Cancer

Objective

This study aimed to investigate the efficacy and safety of anti-PD-1 immunotherapy combined with gemcitabine chemotherapy in multiline treatment of advanced pancreatic cancer.

Methods

A retrospective analysis was performed on the clinical data of 32 patients with advanced pancreatic cancer treated with sintilimab regimen from January 2019 to December 2021 in our hospital. All patients were followed up until death or April 2022, in the form of outpatient, in-hospital review, or telephone follow-up. Follow-up content included routine blood, liver and kidney functions, tumor markers, plain or enhanced abdominal CT, and abdominal MRI examinations. Clinical efficacy was evaluated according to mRECIST criteria, and the severity of adverse effects was evaluated according to American Institute for Cancer Research (AICR) Standard Term for Adverse Events, Version 5.0.

Results

During treatment, the dosage of sintilimab was halved in 2 patients due to adverse reactions. All patients were treated with sintilimab for 1~10 times, with an average of 6 ± 4 times. The total response rate (ORR) and disease control rate (DCR) were 6.25% and 12.50% and 25.00% and 37.50%, respectively, after 1 and 3 months of treatment. The mean follow-up time of 32 patients was 1-12 months, and the median follow-up time was 4 ± 3 months. By the end point of follow-up, a total of 25 patients died, and the median progression-free survival (PFS) was 3.8 (95% CI (1.85-5.63)) months. The median overall survival (OS) was 5.1 months (95% CI (3.63~7.68). After treatment, the levels of tumor markers CA125, CEA and CA199 were partly decreased compared with those before treatment (all P < 0.001). After treatment, the blood routine indexes d-dimer, CRP (C-reactive protein), NLR (neutral granulocyte to lymphocyte ratio), and MLR (monocyte to lymphocyte ratio) decreased compared with those before treatment. In 32 patients with advanced pancreatic cancer, the adverse reactions with an incidence more than 10% included fatigue, rash, hypothyroidism, hyperuricemia, and renal insufficiency. Only 2 patients showed grade 3 fatigue symptom, and all the others showed no adverse reactions of grades 3~5. In this study, all patients' adverse reactions were relieved after symptomatic treatment.

Conclusion

Gemcitabine chemotherapy in multiline treatment of advanced pancreatic cancer with sintilimab can achieve certain clinical benefits without serious adverse reactions.

Update on quantitative radiomics of pancreatic tumors

Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

Current trends in the diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a dreaded malignancy with a dismal 5-year survival rate despite maximal efforts on optimizing treatment strategies. Currently, early detection is considered to be the most effective way to improve survival as radical resection is the only potential cure. PDAC is often divided into four categories based on the extent of disease: resectable, borderline resectable, locally advanced, and metastatic. Unfortunately, the majority of patients are diagnosed with locally advanced or metastatic disease, which renders them ineligible for curative resection. This is mainly due to the lack of or vague symptoms while the disease is still localized, although appropriate utilization and prompt availability of adequate diagnostic tools is also critical given the aggressive nature of the disease. A cost-effective biomarker with high specificity and sensitivity allowing early detection of PDAC without the need for advanced or invasive methods is still not available. This leaves the diagnosis dependent on radiodiagnostic methods or endoscopic ultrasound. Here we summarize the latest epidemiological data, risk factors, clinical manifestation, and current diagnostic trends and implications of PDAC focusing on serum biomarkers and imaging modalities. Additionally, up-to-date management and therapeutic algorithms are outlined.

Pancreatic cancer grading in pathological images using deep learning convolutional neural networks

Background: Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. Methods: A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. Results: From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. Conclusions: To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.

CT and MRI of pancreatic tumors: an update in the era of radiomics

Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.