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Cancers
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Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival
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Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 25058

Special Issue Editor

Prof. Dr. Atsushi Masamune

E-Mail Website
Guest Editor
Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan
Interests: pancreatic cancer; pancreatitis; early diagnosis; tumor microenvironment; fibrosis; microRNA; inflammation; epidemiology

Special Issue Information

Dear Colleagues,

Pancreatic ductal adenocarcinoma (PDAC), which accounts for the majority of pancreatic cancers, is one of the most lethal human malignancies, with its 5-year survival rate less than 10%. Recently, we have seen advances in the research field, diagnosis, and management of PDAC. For example, genomic alterations during the carcinogenesis of intraductal papillary mucinous neoplasm have been dissected. Dense stroma plays a pivotal role in the progression of PDAC and might serve as a novel therapeutic target. Development of biomarkers, stratification of high-risk individuals, and new imaging modalities may contribute to early diagnosis of PDAC. Several clinical trials are ongoing.

This Special Issue aims to consolidate bench-to-bedside information that addresses the challenges of PDAC. We welcome submissions dealing with the wide aspects of PDAC including pathogenesis, early diagnosis, and potential therapeutic applications, which would contribute to the improved prognosis of this intractable disease.

Prof. Dr. Atsushi Masamune
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomarker
  • clinical trial
  • genomics
  • microRNA
  • pancreatic ductal adenocarcinoma
  • pancreatic stellate cells
  • precision medicine
  • stroma

Published Papers (6 papers)

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Research

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20 pages, 3775 KiB  
Article
Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma
by Raphael M. Kronberg, Lena Haeberle, Melanie Pfaus, Haifeng C. Xu, Karina S. Krings, Martin Schlensog, Tilman Rau, Aleksandra A. Pandyra, Karl S. Lang, Irene Esposito and Philipp A. Lang
Cancers 2022, 14(8), 1964; https://doi.org/10.3390/cancers14081964 - 13 Apr 2022
Cited by 4 | Viewed by 2022
Abstract
Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always [...] Read more.
Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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10 pages, 1825 KiB  
Article
Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography
by Natália Alves, Megan Schuurmans, Geke Litjens, Joeran S. Bosma, John Hermans and Henkjan Huisman
Cancers 2022, 14(2), 376; https://doi.org/10.3390/cancers14020376 - 13 Jan 2022
Cited by 35 | Viewed by 5522
Abstract
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. [...] Read more.
Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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15 pages, 2111 KiB  
Article
Clinical Utility and Limitation of Diagnostic Ability for Different Degrees of Dysplasia of Intraductal Papillary Mucinous Neoplasms of the Pancreas Using 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography
by Yuto Hozaka, Hiroshi Kurahara, Hideyuki Oi, Tetsuya Idichi, Yoichi Yamasaki, Yota Kawasaki, Kiyonori Tanoue, Megumi Jinguji, Masatoyo Nakajo, Atsushi Tani, Akihiro Nakajo, Yuko Mataki, Yoshihiko Fukukura, Hirotsugu Noguchi, Michiyo Higashi, Takashi Yoshiura, Akihide Tanimoto and Takao Ohtsuka
Cancers 2021, 13(18), 4633; https://doi.org/10.3390/cancers13184633 - 15 Sep 2021
Cited by 4 | Viewed by 2403
Abstract
The diagnostic value of 18F-fluorodeoxyglucose (FDG) uptake in the management of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas remains unclear. This study aimed to assess the role of FDG uptake in the diagnosis of different degrees of dysplasia of IPMNs. We [...] Read more.
The diagnostic value of 18F-fluorodeoxyglucose (FDG) uptake in the management of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas remains unclear. This study aimed to assess the role of FDG uptake in the diagnosis of different degrees of dysplasia of IPMNs. We retrospectively analyzed the following three points in 84 patients with IPMNs: (1) risk factors to predict high-grade dysplasia (HGD) and invasive carcinoma (INV); (2) the relationship between FDG uptake and glucose transporter 1 (GLUT-1) expression; and (3) the relationship between FDG uptake and the presence of mural nodules. The histopathological diagnosis was low-grade dysplasia (LGD) in 43 patients, HGD in 16, and INV in 25. The maximum standardized uptake value (SUV-max) was significantly higher in INV than in LGD/HGD (p < 0.0001, p = 0.0136). The sensitivity and specificity to discriminate INV from LGD/HGD were 80.0% and 86.2%, respectively, using the receiver operator characteristic curve, when the optimal cutoff score of SUV-max was set at 4.03. Those values were not different between HGD and LGD. More than half of HGD patients had low GLUT-1 expression. Taken together, FDG-PET/CT is useful in distinguishing between non-invasive and invasive IPMN. Our results offer critical information that may determine surgical treatment strategies. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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Review

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16 pages, 1293 KiB  
Review
HIF-1 and NRF2; Key Molecules for Malignant Phenotypes of Pancreatic Cancer
by Shin Hamada, Ryotaro Matsumoto and Atsushi Masamune
Cancers 2022, 14(2), 411; https://doi.org/10.3390/cancers14020411 - 14 Jan 2022
Cited by 11 | Viewed by 5181
Abstract
Pancreatic cancer is intractable due to early progression and resistance to conventional therapy. Dense fibrotic stroma, known as desmoplasia, is a characteristic feature of pancreatic cancer, and develops through the interactions between pancreatic cancer cells and stromal cells, including pancreatic stellate cells. Dense [...] Read more.
Pancreatic cancer is intractable due to early progression and resistance to conventional therapy. Dense fibrotic stroma, known as desmoplasia, is a characteristic feature of pancreatic cancer, and develops through the interactions between pancreatic cancer cells and stromal cells, including pancreatic stellate cells. Dense stroma forms harsh tumor microenvironments characterized by hypoxia, few nutrients, and oxidative stress. Pancreatic cancer cells as well as pancreatic stellate cells survive in the harsh microenvironments through the altered expression of signaling molecules, transporters, and metabolic enzymes governed by various stress response mechanisms. Hypoxia inducible factor-1 and KEAP1-NRF2, stress response mechanisms for hypoxia and oxidative stress, respectively, contribute to the aggressive behaviors of pancreatic cancer. These key molecules for stress response mechanisms are activated, both in pancreatic cancer cells and in pancreatic stellate cells. Both factors are involved in the mutual activation of cancer cells and stellate cells, by inducing cancer-promoting signals and their mediators. Therapeutic interventions targeting these pathways are promising approaches for novel therapies. In this review, we summarize the roles of stress response mechanisms, focusing on hypoxia inducible factor-1 and KEAP1-NRF2, in pancreatic cancer. In addition, we discuss the potential of targeting these molecules for the treatment of pancreatic cancer. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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20 pages, 2677 KiB  
Review
Genetic Mutations of Pancreatic Cancer and Genetically Engineered Mouse Models
by Yuriko Saiki, Can Jiang, Masaki Ohmuraya and Toru Furukawa
Cancers 2022, 14(1), 71; https://doi.org/10.3390/cancers14010071 - 24 Dec 2021
Cited by 10 | Viewed by 5492
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, and the seventh leading cause of cancer-related deaths worldwide. An improved understanding of tumor biology and novel therapeutic discoveries are needed to improve overall survival. Recent multi-gene analysis approaches such as next-generation sequencing have [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, and the seventh leading cause of cancer-related deaths worldwide. An improved understanding of tumor biology and novel therapeutic discoveries are needed to improve overall survival. Recent multi-gene analysis approaches such as next-generation sequencing have provided useful information on the molecular characterization of pancreatic tumors. Different types of pancreatic cancer and precursor lesions are characterized by specific molecular alterations. Genetically engineered mouse models (GEMMs) of PDAC are useful to understand the roles of altered genes. Most GEMMs are driven by oncogenic Kras, and can recapitulate the histological and molecular hallmarks of human PDAC and comparable precursor lesions. Advanced GEMMs permit the temporally and spatially controlled manipulation of multiple target genes using a dual-recombinase system or CRISPR/Cas9 gene editing. GEMMs that express fluorescent proteins allow cell lineage tracing to follow tumor growth and metastasis to understand the contribution of different cell types in cancer progression. GEMMs are widely used for therapeutic optimization. In this review, we summarize the main molecular alterations found in pancreatic neoplasms, developed GEMMs, and the contribution of GEMMs to the current understanding of PDAC pathobiology. Furthermore, we attempted to modify the categorization of altered driver genes according to the most updated findings. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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17 pages, 2618 KiB  
Review
Role of Pancreatic Stellate Cell-Derived Exosomes in Pancreatic Cancer-Related Diabetes: A Novel Hypothesis
by Chamini J. Perera, Marco Falasca, Suresh T. Chari, Jerry R. Greenfield, Zhihong Xu, Romano C. Pirola, Jeremy S. Wilson and Minoti V. Apte
Cancers 2021, 13(20), 5224; https://doi.org/10.3390/cancers13205224 - 18 Oct 2021
Cited by 12 | Viewed by 3400
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a devastating condition characterised by vague symptomatology and delayed diagnosis. About 30% of PDAC patients report a history of new onset diabetes, usually diagnosed within 3 years prior to the diagnosis of cancer. Thus, new onset diabetes, which [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC) is a devastating condition characterised by vague symptomatology and delayed diagnosis. About 30% of PDAC patients report a history of new onset diabetes, usually diagnosed within 3 years prior to the diagnosis of cancer. Thus, new onset diabetes, which is also known as pancreatic cancer-related diabetes (PCRD), could be a harbinger of PDAC. Diabetes is driven by progressive β cell loss/dysfunction and insulin resistance, two key features that are also found in PCRD. Experimental studies suggest that PDAC cell-derived exosomes carry factors that are detrimental to β cell function and insulin sensitivity. However, the role of stromal cells, particularly pancreatic stellate cells (PSCs), in the pathogenesis of PCRD is not known. PSCs are present around the earliest neoplastic lesions and around islets. Given that PSCs interact closely with cancer cells to drive cancer progression, it is possible that exosomal cargo from both cancer cells and PSCs plays a role in modulating β cell function and peripheral insulin resistance. Identification of such mediators may help elucidate the mechanisms of PCRD and aid early detection of PDAC. This paper discusses the concept of a novel role of PSCs in the pathogenesis of PCRD. Full article
(This article belongs to the Special Issue Pancreatic Cancer: Pathogenesis, Early Diagnosis, and Management for Improved Survival)
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