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Diagnostics
Special Issues
Application of AI in Diagnosis of Colorectal Cancer
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Application of AI in Diagnosis of Colorectal Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 965

Special Issue Editor

Dr. Arda Isik

E-Mail Website
Guest Editor
General Surgery Department, School of Medicine, Istanbul Medeniyet University, 34720 Istanbul, Turkey
Interests: colorectal diseases; colorectal surgery; endoscopy; minimally invasive surgery; surgical oncology

Special Issue Information

Dear Colleagues,

The incidence of colorectal cancer has increased markedly after COVID-19. Innovation is important in the treatment of this disease. In this publication, we would like to focus on new treatment possibilities and especially the importance of robotic surgery. Robotic surgery has incorporated many improvements such as 3D visualization, articulating instruments assisting complex and precise movements. As a result, robotic colorectal surgery shows less time to oral intake, a smaller conversion rate and shortened hospital length of stay compared to open and laparoscopic surgery. Costs may also be reduced in future. In this Special Edition, we invite valuable papers about this subject.

Dr. Arda Isik
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

  • robotic surgery
  • colon cancer
  • rectum cancer
  • 3D visualization
  • laparoscopic surgery

Published Papers (1 paper)

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Research

13 pages, 1462 KiB  
Article
Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features
by Mohammed S. Alshuhri, Abdulaziz Alduhyyim, Haitham Al-Mubarak, Ahmad A. Alhulail, Othman I. Alomair, Yahia Madkhali, Rakan A. Alghuraybi, Abdullah M. Alotaibi and Abdullalh G. M. Alqahtani
Diagnostics 2023, 13(23), 3541; https://doi.org/10.3390/diagnostics13233541 - 27 Nov 2023
Viewed by 795
Abstract
(1) Background: Colorectal cancer is the third most common type of cancer with a high mortality rate and poor prognosis. The accurate prediction of key genetic mutations, such as the KRAS status, tumor staging, and extramural venous invasion (EMVI), is crucial for guiding [...] Read more.
(1) Background: Colorectal cancer is the third most common type of cancer with a high mortality rate and poor prognosis. The accurate prediction of key genetic mutations, such as the KRAS status, tumor staging, and extramural venous invasion (EMVI), is crucial for guiding personalized treatment decisions and improving patients’ outcomes. MRI radiomics was assessed to predict the KRAS status and tumor staging in colorectal cancer patients across different imaging platforms to improve the personalized treatment decisions and outcomes. (2) Methods: Sixty colorectal cancer patients (35M/25F; avg. age 56.3 ± 12.9 years) were treated at an oncology unit. The MRI scans included T2-weighted (T2W) and diffusion-weighted imaging (DWI) or the apparent diffusion coefficient (ADC). The manual segmentation of colorectal cancer was conducted on the T2W and DWI/ADC images. The cohort was split into training and validation sets, and machine learning was used to build predictive models. (3) Results: The neural network (NN) model achieved 73% accuracy and an AUC of 0.71 during training for predicting the KRAS mutation status, while during testing, it achieved 62.5% accuracy and an AUC of 0.68. In the case of tumor grading, the support vector machine (SVM) model excelled with a training accuracy of 72.93% and an AUC of 0.7, and during testing, it reached an accuracy of 72% and an AUC of 0.69. (4) Conclusions: ML models using radiomics from ADC maps and T2-weighted images are effective for distinguishing KRAS genes, tumor grading, and EMVI in colorectal cancer. Standardized protocols are essential to improve MRI radiomics’ reliability in clinical practice. Full article
(This article belongs to the Special Issue Application of AI in Diagnosis of Colorectal Cancer)
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