Radiomics and Machine Learning in Disease Diagnosis

Dear Colleagues,

Interest in the potential of machine learning and radiomics has steadily grown over the past decade. These techniques have opened the door for innovative applications in the prediction of patient prognosis and in lesion characterization, expanding the amount of information obtainable from routine radiological exams. Machine learning also holds the potential to streamline radiologists’ clinical workflow by automating repetitive, menial tasks as well as increase overall efficiency, for example by improving image acquisition speed without quality loss. However, even as commercial solutions based on these tools are continually being launched, the need for research in this domain is greater than ever. In particular, validation of radiomics and machine learning software to demonstrate their clinical usefulness should be required by potential end users.

This Special Issue will be open to collect original research and review papers centered on radiomics and machine learning. The aim is to provide an avenue for preliminary and feasibility studies exploring new avenues for radiomics and machine learning in radiology, validation studies for previously developed models, and an overview of the current state of the art.

Dr. Renato Cuocolo
Dr. Gaia Spadarella
Dr. Lorenzo Ugga
Guest Editors

Manuscript Submission Information

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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 2600 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.