Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.6
2.6
Journals
Current Oncology
Special Issues
Diagnosis of Solid and Hematological Tumours in the Era of...
share announcement

Diagnosis of Solid and Hematological Tumours in the Era of Artificial Intelligence (AI)

A special issue of Current Oncology (ISSN 1718-7729).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1816

Special Issue Editors

Dr. Gerardo Cazzato

E-Mail Website
Guest Editor
Section of Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari "Aldo Moro", 70124 Bari, Italy
Interests: skin diseases; dermatology; dermatopathology; diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Massaro

E-Mail Website
Guest Editor
LUM Enterprise, LUM University, S.S. 100-km 18, Casamassima, 70010 Bari, Italy
Interests: e-health; artificial neural network; multilayer perceptron; smart health; homecare assistance management; telemedicine architecture; patient health status prediction; KNIME

Special Issue Information

Dear Colleagues,

In recent years the diagnosis of solid and hematological neoplasms has begun to be invested by a renewed enthusiasm and desire for research towards the use of technology in a field that is difficult in itself, such as human neoplasms. The spread of ever more powerful instruments has made it possible to transform large amounts of data into information flows that could be the basis for the development and training of Artificial Intelligence algorithms. In this Special Issue we are confronted on a very current terrain and we invite colleagues from different disciplines to present their contribution. 

Dr. Gerardo Cazzato
Dr. Alessandro Massaro
Guest Editors

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. Current Oncology is an international peer-reviewed open access monthly 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 2200 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

  • artificial intelligence (AI)
  • algorithm
  • digital pathology
  • digital oncology
  • diagnosis
  • neoplasms

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

13 pages, 4362 KiB  
Article
Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
by Gerardo Cazzato, Alessandro Massaro, Anna Colagrande, Irma Trilli, Giuseppe Ingravallo, Nadia Casatta, Carmelo Lupo, Andrea Ronchi, Renato Franco, Eugenio Maiorano and Angelo Vacca
Curr. Oncol. 2023, 30(7), 6066-6078; https://doi.org/10.3390/curroncol30070452 - 23 Jun 2023
Cited by 1 | Viewed by 1250
Abstract
Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, [...] Read more.
Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process. Full article
(This article belongs to the Special Issue Diagnosis of Solid and Hematological Tumours in the Era of Artificial Intelligence (AI))
►▼ 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