Special Issue "Leveraging Radiomics for Computational Inference Advances in Oncology"

A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Cancer Imaging".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 349

Special Issue Editors

Institute of Data Science and Computing, University of Miami, Miami, FL 33146, USA
Interests: radiotherapy; MRI; cancer imaging
Special Issues, Collections and Topics in MDPI journals
Paul Scherrer Institute, 111 523 Villigen, Switzerland
Interests: radiotherapy; MRI; cancer imaging

Special Issue Information

Dear Colleagues,

A wide spectrum of Artificial Intelligence and Machine Learning solutions is currently available to provide access to image-driven data critical to fields such as oncology. These developments have fueled the radiomics field, providing new complementary information for cancer diagnosis therapy, prognosis. The use of predictive learning models has greatly facilitated the combination of radiomic detections with other informative markers to the benefit of clinical decision making. 

Despite the promising results emerging from radiomics, some limitations remain to be addressed in order to optimize the inference processes in oncology. The selected features are required to satisfy criteria such as i) repeatability with regard to acquisitions and used parameters, within the same imaging modality, ii) reproducibility of results across changing parameters, depending on the measurement system of choice, iii) transferability of evidence over patient groups, and iv) generalizability of findings across cancer types. 

Advancing in all such directions is crucial for allowing better standardization in radiomics studies. The Image Biomarker Standardization Initiative (IBSI) provides guidelines centered on data and aimed at establishing benchmarks for the processing of image features. Feature-based approaches require that model generation ultimately prove its reliability in clinical practice, although the computational workflow may be complex and hardly interpretable in the outcomes. Stepping from black-box approaches to graph-based ones or other types of representation frameworks needs to be further developed. This Special Issue invites proposals addressing new solutions in view of their potential integrability within inference approaches supporting medical decisions.

Dr. Enrico Capobianco
Dr. Marco Diego Dominietto
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. Tomography 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 1800 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.


  • oncology
  • radiomics
  • inference
  • computational approaches
  • clinical decision processes

Published Papers

There is no accepted submissions to this special issue at this moment.
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