Advances in Oncological Imaging (2nd Edition)

Dear Colleagues,

This Special Issue is the second edition of the Special Issue “Advances in Oncological Imaging”, available at https://www.mdpi.com/journal/cancers/special_issues/L31K1N2AT2.

With cancer being one of the leading causes of death worldwide, there is an urgent need for novel and more effective imaging methods that can detect cancer at an early stage, accurately predict patient outcomes, and aid in personalized treatment planning. This Special Issue brings together a collection of original research articles, reviews, and perspectives that showcase cutting-edge imaging technologies and innovative approaches in cancer imaging, including molecular imaging, radiomics, artificial intelligence, and multimodal imaging. Our goal is to provide a comprehensive overview of the current state of cancer imaging and to promote the translation of these advances into clinical practice to improve patient outcomes.

Recent advancements in oncological imaging technologies have greatly improved the detection and treatment of malignancy.

Developments in MRI and CT equipment have allowed significant improvements in image quality and reduction in acquisition times. Moreover, CT has also benefited from deep learning algorithms to reconstruct high-quality images from low-dose data, with a reduction in radiation exposure by up to 80% compared to standard CT scans.

Multimodal imaging technologies allow for a more comprehensive view of the patient’s cancer, which can improve diagnosis and treatment planning.

Artificial intelligence (AI) is also being increasingly used in oncological imaging to aid in the detection, diagnosis, and treatment of cancer. AI applications include automated tumor detection and segmentation to help radiologists in rapid and accurate tumor detection and characterization.

Radiomics feature extraction combined with AI predictive models aid cancer diagnosis, prognosis, and treatment response prediction to provide a customized approach and ultimately improve patient outcomes.

This Special Issue will highlight the novelties in oncological imaging in the field of image acquisition and reconstruction technologies, as well as AI applications for the automatic detection, segmentation, and feature extraction of data, to achieve increasingly personalized medicine.

Dr. Michaela Cellina
Dr. Maurizio Cè
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 communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Cancers 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 2900 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.