State of the Art Diagnosis of Pancreatic Ductal Adenocarcinoma

Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer, are challenging to diagnose or detect early, and the stages have a fast progression rate. This paper presents the state-of-the-art techniques used in cancer survival prediction, suggesting how these techniques can be implemented in predicting the overall survival of pancreatic ductal adenocarcinoma cancer (pdac) patients. Because of bewildering and high volumes of data, the recent studies highlight the importance of machine learning (ML) algorithms like support vector machines and convolutional neural networks. Studies predict pancreatic ductal adenocarcinoma cancer (pdac) survival is within the limits of 41.7% at one year, 8.7% at three years, and 1.9% at five years. There is no significant correlation found between the disease stages and the overall survival rate. The implementation of ML algorithms can improve our understanding of cancer progression. ML methods need an appropriate level of validation to be considered in everyday clinical practice. The objective of these techniques is to perform classification, prediction, and estimation. Accurate predictions give pathologists information on the patient’s state, surgical treatment to be done, optimal use of resources, individualized therapy, drugs to prescribe, and better patient management.

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Epithelial to Mesenchymal Transition in Patients with Pancreatic Ductal Adenocarcinoma: State-of-the-Art and Therapeutic Opportunities

Pharmaceuticals ◽

10.3390/ph14080740 ◽

2021 ◽

Vol 14 (8) ◽

pp. 740

Author(s):

Julie Dardare ◽

Andréa Witz ◽

Jean-Louis Merlin ◽

Agathe Bochnakian ◽

Paul Toussaint ◽

...

Keyword(s):

Molecular Markers ◽

Pancreatic Ductal Adenocarcinoma ◽

Cancer Progression ◽

Epithelial To Mesenchymal Transition ◽

State Of The Art ◽

Ductal Adenocarcinoma ◽

Mesenchymal Transition ◽

Late Stages

Pancreatic ductal adenocarcinoma (PDAC) is one of the malignancies with the worst prognosis despite a decade of efforts. Up to eighty percent of patients are managed at late stages with metastatic disease, in part due to a lack of diagnosis. The effectiveness of PDAC therapies is challenged by the early and widespread metastasis. Epithelial to mesenchymal transition (EMT) is a major driver of cancer progression and metastasis. This process allows cancer cells to gain invasive properties by switching their phenotype from epithelial to mesenchymal. The importance of EMT has been largely described in PDAC, and its importance is notably highlighted by the two major subtypes found in PDAC: the classical epithelial and the quasi-mesenchymal subtypes. Quasi-mesenchymal subtypes have been associated with a poorer prognosis. EMT has also been associated with resistance to treatments such as chemotherapy and immunotherapy. EMT is associated with several key molecular markers both epithelial and mesenchymal. Those markers might be helpful as a biomarker in PDAC diagnosis. EMT might becoming a key new target of interest for the treatment PDAC. In this review, we describe the role of EMT in PDAC, its contribution in diagnosis, in the orientation and treatment follow-up. We also discuss the putative role of EMT as a new therapeutic target in the management of PDAC.

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Pancreatic ductal adenocarcinoma: State-of-the-art 2017 and new therapeutic strategies

Cancer Treatment Reviews ◽

10.1016/j.ctrv.2017.08.007 ◽

2017 ◽

Vol 60 ◽

pp. 32-43 ◽

Cited By ~ 66

Author(s):

Marta Chiaravalli ◽

Michele Reni ◽

Eileen M. O'Reilly

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

State Of The Art ◽

Therapeutic Strategies ◽

Ductal Adenocarcinoma

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The State of the Art on Blood MicroRNAs in Pancreatic Ductal Adenocarcinoma

Analytical Cellular Pathology ◽

10.1155/2019/9419072 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Zhuqing Gao ◽

Wei Jiang ◽

Shutian Zhang ◽

Peng Li

Keyword(s):

Tumor Microenvironment ◽

Pancreatic Ductal Adenocarcinoma ◽

State Of The Art ◽

Therapeutic Interventions ◽

Ductal Adenocarcinoma ◽

Curative Surgery ◽

Clinical Utilization ◽

Highly Sensitive ◽

Made In ◽

Wide Usage

Despite enormous advances being made in diagnosis and therapeutic interventions, pancreatic ductal adenocarcinoma (PDAC) is still recognized as one of the most lethal malignancies. Early diagnosis and timely curative surgery can markedly improve the prognosis; hence, there is an unmet necessity to explore efficient biomarkers for patients’ benefit. Recently, blood miRNAs (miRNAs) have been reported to be a novel biomarker in human cancers. Part of it is selectively packaged by plasma exosomes released from cells via exocytosis and is highly sensitive to changes in the tumor microenvironment. Furthermore, due to less invasiveness and technical availability, miRNA-based liquid biopsy holds promise for further wide usage. Therefore, this review is aimed at presenting an update on the association between blood miRNAs and the biology of PDAC, then discussing its clinical utilization further.

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Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

10.20944/preprints202112.0001.v1 ◽

2021 ◽

Author(s):

Natália Alves ◽

Megan Schuurmans ◽

Geke Litjens ◽

Joeran S. Bosma ◽

John Hermans ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Pancreatic Ductal Adenocarcinoma ◽

State Of The Art ◽

Characteristic Curve ◽

Lesion Detection ◽

Ductal Adenocarcinoma ◽

Test Set ◽

Pancreas Tumor ◽

The Impact

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 (&lt;2cm) 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 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), (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 &lt;2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

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Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Cancers ◽

10.3390/cancers14020376 ◽

2022 ◽

Vol 14 (2) ◽

pp. 376

Author(s):

Natália Alves ◽

Megan Schuurmans ◽

Geke Litjens ◽

Joeran S. Bosma ◽

John Hermans ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Pancreatic Ductal Adenocarcinoma ◽

State Of The Art ◽

Characteristic Curve ◽

Lesion Detection ◽

Ductal Adenocarcinoma ◽

Test Set ◽

Pancreas Tumor ◽

The Impact

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.

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