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Diagnostics
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Advances in the Diagnosis of Pancreatic Cancer
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Advances in the Diagnosis of Pancreatic Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 14718

Special Issue Editor

Dr. Christos Agalianos

E-Mail Website
Guest Editor
Department of Surgery, Athens Naval and Veterans Hospital, Deinokratous 70, 11521 Athens, Greece
Interests: pancreatic surgery; liver surgery

Special Issue Information

Dear Colleagues,

Pancreatic cancer (PC) remains one of the most aggressive cancers despite improvements in its prevention, diagnosis, and management. Early detection, especially in high-risk patients, along with the improvement of traditional imaging technologies such as computer tomography (CT) and magnetic resonance imaging (MRI) are nowadays the best tools available to physicians when dealing with PC. Furthermore, the implementation of radiomics in diagnosis and staging can aid in predicting treatment selection and accuracy in PC patients. Finally, molecular analysis and liquid biopsy can further stratify these patients, in order to receive optimal individualised therapy, either in terms of chemo-radiotherapy or surgical management. The aim of this Special Issue is to gather the newest data regarding the diagnosis of PC and the implementation of different modalities in both the pre- and post-operative period.

Submissions on, but not limited to, the following topics are welcome:

  • Pancreatic cancer imaging
  • Radiomics in pancreatic cancer
  • Rare pancreatic neoplasms
  • Pancreatic cystic tumors imaging
  • Molecular-analysis-driven therapies
  • Liquid biopsy
  • High-risk-patient identification
  • Management of high-risk patients
  • Biomarkers in PC

Dr. Christos Agalianos
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. Diagnostics 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 2600 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

  • prognostic biomarkers
  • diagnostic biomarkers
  • radiomics
  • imaging modalities
  • cystic neoplasms
  • liquid biopsy
  • genetic predisposition
  • high-risk patients
  • gene therapy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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10 pages, 779 KiB  
Article
Clinical Value of Mean Platelet Volume to Platelet Ratio (MPR) in Distinguishing Mass-Forming Chronic Pancreatitis and Pancreatic Cancer
by Han-Xuan Wang, Yu-Lin Li, Jin-Can Huang, You-Wei Ma, Ren Lang and Shao-Cheng Lyu
Diagnostics 2023, 13(19), 3126; https://doi.org/10.3390/diagnostics13193126 - 4 Oct 2023
Cited by 1 | Viewed by 1755
Abstract
Background: Correctly distinguishing mass-forming chronic pancreatitis (MFCP) from pancreatic cancer (PC) is of clinical significance to determine optimal therapy and improve the prognosis of patients. According to research, inflammation status in PC is different from that in MFCP. Mean platelet volume/platelet ratio (MPR) [...] Read more.
Background: Correctly distinguishing mass-forming chronic pancreatitis (MFCP) from pancreatic cancer (PC) is of clinical significance to determine optimal therapy and improve the prognosis of patients. According to research, inflammation status in PC is different from that in MFCP. Mean platelet volume/platelet ratio (MPR) is a platelet-related inflammation index which has been proven to be valuable in the diagnosis and prognosis of various malignant cancers due to the change in mean platelet volume and platelet count under abnormal inflammatory conditions caused by tumors. Thus, we conducted this study to investigate the clinical value of MPR in distinguishing MFCP from PC. Methods: We retrospectively analyzed the data of 422 patients who were suspected to have PC during imaging examination at our department from January 2012 to December 2021. Included patients were divided into the PC (n = 383) and MFCP groups (n = 39), according to their pathological diagnosis. Clinical data including MPR were compared within these two groups and the diagnostic value was explored using logistic regression. The ROC curve between MPR and PC occurrence was drawn and an optimal cut-off value was obtained. Propensity score matching was applied to match MFCP patients with PC patients according to their age and carbohydrate antigen 19-9 (CA19-9). Differences in MPR between groups were compared to verify our findings. Results: The area under the ROC curve between MPR and PC occurrence was 0.728 (95%CI: 0.652–0.805) and the optimal cut-off value was 0.045 with a 69.2% sensitivity and 68.0% accuracy. For all the included patients, MPRs in the MFCP and PC groups were 0.04 (0.04, 0.06) and 0.06 (0.04, 0.07), respectively (p = 0.005). In patients with matching propensity scores, MPRs in the MFCP and PC groups were 0.04 (0.03, 0.06) and 0.06 (0.05, 0.08), respectively (p = 0.005). Multiple logistic regression in all included patients and matched patients confirmed MPR and CA19-9 as independent risk markers in distinguishing PC. Combining CA19-9 with MPR can increase the sensitivity and accuracy in diagnosing PC to 93.2% and 89.5%, respectively. Conclusion: MPR in PC patients is significantly higher than that in MFCP patients and may be adopted as a potential indicator to distinguish MFCP and PC. Its differential diagnosis capacity can be improved if combined with CA19-9. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Pancreatic Cancer)
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Review

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19 pages, 1404 KiB  
Review
From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer
by Satvik Tripathi, Azadeh Tabari, Arian Mansur, Harika Dabbara, Christopher P. Bridge and Dania Daye
Diagnostics 2024, 14(2), 174; https://doi.org/10.3390/diagnostics14020174 - 12 Jan 2024
Cited by 16 | Viewed by 11207
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room [...] Read more.
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Pancreatic Cancer)
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Other

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9 pages, 1113 KiB  
Brief Report
Analysis of IVIM Perfusion Fraction Improves Detection of Pancreatic Ductal Adenocarcinoma
by Katarzyna Nadolska, Agnieszka Białecka, Elżbieta Zawada, Wojciech Kazimierczak and Zbigniew Serafin
Diagnostics 2024, 14(6), 571; https://doi.org/10.3390/diagnostics14060571 - 7 Mar 2024
Viewed by 1235
Abstract
The purpose of this study was to evaluate whether intravoxel incoherent motion (IVIM) parameters can enhance the diagnostic performance of MRI in differentiating normal pancreatic parenchyma from solid pancreatic adenocarcinomas. This study included 113 participants: 66 patients diagnosed with pancreatic adenocarcinoma and 47 [...] Read more.
The purpose of this study was to evaluate whether intravoxel incoherent motion (IVIM) parameters can enhance the diagnostic performance of MRI in differentiating normal pancreatic parenchyma from solid pancreatic adenocarcinomas. This study included 113 participants: 66 patients diagnosed with pancreatic adenocarcinoma and 47 healthy volunteers. An MRI was conducted at 1.5 T MR unit, using nine b-values. Postprocessing involved analyzing both conventional monoexponential apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient D-pure molecular diffusion coefficient, perfusion-dependent diffusion coefficient D*-pseudodiffusion coeffitient, and perfusion fraction coefficient (f)) across four different b-value selections. Significantly higher parameters were found in the control group when using high b-values for the pure diffusion analysis and all b-values for the monoexponential analysis. Conversely, in the study group, the parameters were affected by low b-values. Most parameters could differentiate between normal and cancerous tissue, with D* showing the highest diagnostic performance (AUC 98–100%). A marked decrease in perfusion in the patients with pancreatic cancer, indicated by the significant differences in the D* medians between groups, was found. In conclusion, standard ADC maps alone may not suffice for a definitive pancreatic cancer diagnosis, and incorporating IVIM into MRI protocols is recommended, as the reduced tissue perfusion detected by the IVIM parameters is a promising marker for pancreatic adenocarcinoma. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Pancreatic Cancer)
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