Special issue on Artificial Intelligence in Radiation Therapy

15. juli 2020 - Heidi S. Rønde

IEEE Transactions on Radiation & Plasma Medical Sciences
                               Special issue on
              Artificial Intelligence in Radiation Therapy

              Call for papers: submission deadline 31/10/2020
                Guest Editors: Xun Jia, Lei Ren, Leonard Wee

Artificial intelligence (AI) has attracted a lot of attention in radiation therapy recently. With
advancements in deep learning techniques enabled by multi-layered neural networks, AI has
demonstrated its successes in solving a number of challenging clinical problems with substantial
improvements over conventional methods. We are seeing a strong rise of AI in various
radiotherapy applications - medical imaging reconstruction, tumor and organ-at-risk
segmentations, automated treatment planning, and automatic machine or treatment plan quality
assurance - to name but a few. AI has also opened up new horizons to tackle some problems that
have proven too challenging for conventional machine learning techniques. For instance, AI may
be used to build models with intelligence to solve problems in a human-like fashion. The
penetration of AI through clinical radiation therapy is expected to generate valuable impacts on
treatment accuracy, efficiency, and safety, which we hope will eventually translate into benefits
for patient care.

Meanwhile, it has also been brought to the fore that adoption of AI technology in the clinic faces
numerous challenges, such as the lack of interpretability of many deep learning models, and the
concerns about their robustness and generalizability. Studies are actively conducted to overcome
the challenges to bring the promises of AI to clinical practice.

We organize this special issue and invite authors to submit AI-related papers on topics that include,
but are not limited to, the following:

• Dose calculation and treatment planning
• Machine and patient-specific treatment plan quality assurance
• AI-based autonomous decision systems and its applications in radiotherapy
• Adaptive radiation therapy
• Medical image acquisition and processing (e.g. registration, segmentation, and
synthesis) as applied to radiotherapy
• Radiomics, radiogenomics, and treatment outcome prediction and assessment
• Challenges of adopting AI-based tools in radiotherapy clinics, such as “interpretability” or
“generalizability”, and their corresponding solutions
• Practical aspects for implementing AI in the clinical practice
• Evaluation of clinical impacts of AI techniques in radiotherapy

Authors must submit papers digitally to https://mc.manuscriptcentral.com/trpms, using standard
IEEE Transactions format, indicating in their cover letter that the submission is aimed for this
special issue. Authors are encouraged to contact the guest editors to determine suitability of their
submission for this special issue.

Guest Editors:
Xun Jia, Ph.D.
University of Texas
Southwestern Medical Center,
2280 Inwood Rd., MC9303,
Dallas, TX 75235, USA
Phone: 1-214-648-5032

Lei Ren, Ph.D.
Duke University,
Duke University Medical
Center, Durham, NC 27710,
Phone: 1-919-668-0489

Leonard Wee, Ph.D.
Maastricht University,
Dr. Tanslann 12, 6229 ET
Maastricht, Netherlands
Phone: +31 (0)6 58 73 01 82

Provisional schedule:
Submission of manuscripts: Oct 31, 2020
Acceptance/rejection notification: Dec 31, 2020
Revised manuscripts due: Jan 31, 2021
Publication: Mar 2021

Download info her eller se mere på hjemmesiden.

IEEE Article

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