Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Advances in Gastric Cancer Imaging
share announcement

Advances in Gastric Cancer Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2381

Special Issue Editor

Prof. Dr. Maria Antonietta Mazzei

E-Mail Website
Guest Editor
Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
Interests: oncologic imaging; MRI; CT; DECT; perfusion CT; MR functional imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The treatment of gastric cancer (GC) has considerably changed in the last two decades. Even if surgery remains the only curative treatment, the introduction of multimodal therapy has dramatically improved the chance of survival, especially for those patients with locally advanced or peritoneal disease. Imaging is facing the challenge of understanding the efficacy of these treatments and of defining prognostic factors that can influence patients’ management. Imaging innovations such as radiomics, artificial intelligence (AI), dual energy CT, advanced MRI, and new discoveries about tumor histology, biology, and genetics could be combined for identifying the best diagnostic and therapeutic route for each patient, making precision medicine a reality.

The forthcoming Special Issue focuses on imaging innovations that could have a real impact on patients’ management and prognosis.

Invited topics may include:

  • New diagnostic and prognostic imaging capabilities in GC;
  • Applications of AI and radiomics to GC;
  • Imaging to evaluate the efficacy of innovative intraperitoneal procedures (HIPEC, PIPAC).

Prof. Dr. Maria Antonietta Mazzei
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

  • gastric cancer
  • prognosis
  • diagnosis
  • Artificial Intelligence
  • hyperthermic intraperitoneal chemotherapy (HIPEC)
  • pressurized intraperitoneal aerosol chemotherapy (PIPAC)
  • dual energy CT
  • MRI
  • positron emission tomography (PET-CT)
  • endoscopy

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 4164 KiB  
Article
Detection and Characterization of Gastric Cancer Using Cascade Deep Learning Model in Endoscopic Images
by Atsushi Teramoto, Tomoyuki Shibata, Hyuga Yamada, Yoshiki Hirooka, Kuniaki Saito and Hiroshi Fujita
Diagnostics 2022, 12(8), 1996; https://doi.org/10.3390/diagnostics12081996 - 18 Aug 2022
Cited by 6 | Viewed by 2094
Abstract
Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, [...] Read more.
Endoscopy is widely applied in the examination of gastric cancer. However, extensive knowledge and experience are required, owing to the need to examine the lesion while manipulating the endoscope. Various diagnostic support techniques have been reported for this examination. In our previous study, segmentation of invasive areas of gastric cancer was performed directly from endoscopic images and the detection sensitivity per case was 0.98. This method has challenges of false positives and computational costs because segmentation was applied to all healthy images that were captured during the examination. In this study, we propose a cascaded deep learning model to perform categorization of endoscopic images and identification of the invasive region to solve the above challenges. Endoscopic images are first classified as normal, showing early gastric cancer and showing advanced gastric cancer using a convolutional neural network. Segmentation on the extent of gastric cancer invasion is performed for the images classified as showing cancer using two separate U-Net models. In an experiment, 1208 endoscopic images collected from healthy subjects, 533 images collected from patients with early stage gastric cancer, and 637 images from patients with advanced gastric cancer were used for evaluation. The sensitivity and specificity of the proposed approach in the detection of gastric cancer via image classification were 97.0% and 99.4%, respectively. Furthermore, both detection sensitivity and specificity reached 100% in a case-based evaluation. The extent of invasion was also identified at an acceptable level, suggesting that the proposed method may be considered useful for the classification of endoscopic images and identification of the extent of cancer invasion. Full article
(This article belongs to the Special Issue Advances in Gastric Cancer Imaging)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop