Bridging the Gap: Integrating AI into Clinical Practice for Oncological PET/CT Imaging
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".
Deadline for manuscript submissions: 1 April 2025 | Viewed by 66
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue aims to bridge the gap between AI algorithms developed in academia and the practical needs of clinical oncological PET/CT imaging. It addresses challenges such as model generalizability, economic feasibility, and regulatory constraints that limit the adoption of AI-based solutions in clinical settings. Additionally, it highlights the struggle of AI-based techniques developed in academic research labs to gain clinical trust compared to industrial solutions, which benefit from extensive datasets and clinical approvals.
AI promises significant advancements in diagnosis, treatment assessment, and planning, particularly in oncologic PET/CT imaging. However, challenges such as model generalizability, economic feasibility, and regulatory hurdles significantly hinder the widespread adoption of AI-based solutions in clinical settings. Techniques introduced by research labs often struggle to gain clinical trust compared to industrial solutions, which benefit from extensive datasets and clinical approvals. Moreover, the lack of agreed-upon problem statements and effective collaboration tools has impeded the integration of academic advancements into clinical practice.
This Special Issue seeks contributions from technical and clinical researchers to share their solutions and results within this framework. We are particularly interested in foundational and generalist medical AI models capable of performing diverse tasks using minimally labeled data through self-supervised and active learning. These models should be capable of interpreting various medical modalities, including imaging, electronic health records, lab results, genomics, graphs, and medical text, by leveraging large, diverse datasets. Specialists such as oncologists, nuclear medicine experts, and medical physicists will guide these models using specific prompts, ensuring that responses are informed by their expertise.
Dr. Fereshteh Yousefirizi
Guest Editor
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. 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.
Keywords
- physician-in-the-loop
- PET/CT
- AI
- foundation model
- generalist model
- tumor segmentation
- outcome prediction
- whole-body PET
- cancers
