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Special Issue "Radiomics and Machine Learning Models for Oncological Clinical Applications"
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 (20 May 2023) | Viewed by 6526
Special Issue Editor
Interests: genitourinary imaging, hepatobiliary imaging, radiomics, machine learning; US; CT; MRI
Special Issue Information
In this new era of technological medical advances, machine learning (ML) has emerged as a subset of artificial intelligence focused on algorithms that can make predictions or decision tasks without prior explicit programmed rules. ML algorithms use iterative statistics learning methods from “training” data to progressively improve the model performance over time, enabling the recognition patterns in large datasets and classification of instances.
The application of ML models coupled to radiomics analysis has been embraced in oncological imaging to assess predictive image-based phenotypes for precision medicine.
Radiomics is a multistep process that converts medical images into mineable data through mathematical extraction of quantitative parameters that reflect image tumor heterogeneity, thus empowering precision diagnosis and staging in cancer imaging. Indeed, such a vast amount of data can be more easily handled by ML algorithms than traditional statistical methods.
The development of ML radiomics-based models represents an excellent opportunity to extract additional value and information from medical imaging, thus improving clinical and radiological workup for oncological patients.
This Special Issue aims to present novel applications of ML and radiomics in diagnostic oncological imaging, covering insights from optimising the clinical-radiological workflow (patient screening, image acquisition) to the more specific image-based tasks (cancer detection, characterisation, and treatment monitoring).
Dr. Francesco Verde
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 2000 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.
- oncological imaging
- texture analysis
- computed tomography
- magnetic resonance imaging
- genitourinary cancer
- breast cancer
- hepatobiliary cancer
- gastrointestinal cancer