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 2502

Special Issue Editors


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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

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Keywords

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

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Published Papers (1 paper)

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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 4 | Viewed by 1783
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
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