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Applied Sciences
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Machine Learning in Oncology
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Machine Learning in Oncology

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 2791

Special Issue Editors

Dr. Noemi Scarpato

E-Mail Website
Guest Editor
Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, Via Val Cannuta 247, 00166 Rome, Italy
Interests: precision medicine; data mining; machine learning; big data; IoT; semantic web; linked data visualization; NLP
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Pallotti

E-Mail Website
Co-Guest Editor
Department of Management and Law, Tor Vergata University of Rome, 00133 Rome, Italy
Interests: wearable sensors; machine learning; personalized medicine; health technology assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Pieroni

E-Mail Website
Co-Guest Editor
Department of Innovation and Information Engineering, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, Italy
Interests: digital&social innovation; blockchain technology; IoT; data lake and big data architecture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the availability of large collections of data, often related to each other, has allowed the widespread dissemination of data-based applications.

Specifically, in the medical environment, the spread of the digitalization of medical data has made it possible to design machine learning applications able to support medical decisions.

The design of medical decision support systems and their application in oncology have been a very hot topic in recent oncology research.

The purpose of this Special Issue is to analyze the applications of artificial intelligence in oncology. In particular, we will pay attention to the machine learning applications able to provide decision support systems.

Submitted papers must include new, significant research-based technical contributions in the scope of the journal or a complete review of the state of the art in the following Special Issue topics:

  • Machine learning applications in oncology;
  • Deep learning applications in oncology;
  • Neural networks applications in oncology;
  • Computer vision for oncology applications;
  • Predictive medicine applications in oncology;
  • Precision medicine applications in oncology;
  • Medical decision support systems in oncology;
  • Explainability of machine learning applications in oncology.

Prof. Noemi Scarpato
Dr. Antonio Pallotti
Prof. Alessandra Pieroni
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. Applied Sciences 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 2400 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

  • machine learning
  • oncology
  • data mining
  • precision medicine
  • personalized medicine
  • explainability
  • neural networks
  • computer vision

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Published Papers (1 paper)

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Research

14 pages, 516 KiB  
Article
Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data
by Valeri Gitis, Alexander Derendyaev, Konstantin Petrov, Eugene Yurkov, Sergey Pirogov, Natalia Sergeeva, Boris Alekseev and Andrey Kaprin
Appl. Sci. 2021, 11(9), 3836; https://doi.org/10.3390/app11093836 - 23 Apr 2021
Cited by 1 | Viewed by 2083
Abstract
Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with [...] Read more.
Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease. Full article
(This article belongs to the Special Issue Machine Learning in Oncology)
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