Artificial Intelligence in Orthopedic Oncology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 756

Special Issue Editor


E-Mail Website
Guest Editor
Department of Orthopedic Surgery, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
Interests: bone metastatic disease

Special Issue Information

Dear Colleagues,

The Special Issue aims to allow researchers to share their findings, perspectives, and experiences in the fields of artificial intelligence (AI) and orthopedic oncology. We invite original research articles, reviews, and perspectives. The topics we will cover include machine learning, predictive modelling, medical imaging, cancer diagnosis, clinical decision support systems, and precision medicine.

The use of AI in orthopedic oncology research is an emerging field that has attracted the attention of international researchers due to its potential to revolutionize diagnosis and treatment. The non-invasive and low-cost nature of AI technologies make them an attractive tool to support clinical decision making. AI can analyze vast amounts of data and identify subtle patterns that may be missed by human observers.

In summary, the Special Issue on "Artificial Intelligence in Orthopedic Oncology" aims to provide a comprehensive and up-to-date overview of this field, from fundamentally developing models to validations and applications. We aim to facilitate the translation of this emerging technology into clinical practice, and ultimately, improve diagnosis and treatment.

Dr. Olivier Q. Groot
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. 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.

Keywords

  • orthopedic oncology
  • artificial intelligence
  • machine learning
  • predictive modelling
  • pathological fractures
  • cancer diagnosis
  • medical imaging

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 3279 KiB  
Article
Opportunistic CT for Prediction of Adverse Postoperative Events in Patients with Spinal Metastases
by Neal D. Kapoor, Olivier Q. Groot, Colleen G. Buckless, Peter K. Twining, Michiel E. R. Bongers, Stein J. Janssen, Joseph H. Schwab, Martin Torriani and Miriam A. Bredella
Diagnostics 2024, 14(8), 844; https://doi.org/10.3390/diagnostics14080844 - 19 Apr 2024
Viewed by 286
Abstract
The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 [...] Read more.
The purpose of this study was to assess the value of body composition measures obtained from opportunistic abdominal computed tomography (CT) in order to predict hospital length of stay (LOS), 30-day postoperative complications, and reoperations in patients undergoing surgery for spinal metastases. 196 patients underwent CT of the abdomen within three months of surgery for spinal metastases. Automated body composition segmentation and quantifications of the cross-sectional areas (CSA) of abdominal visceral and subcutaneous adipose tissue and abdominal skeletal muscle was performed. From this, 31% (61) of patients had postoperative complications within 30 days, and 16% (31) of patients underwent reoperation. Lower muscle CSA was associated with increased postoperative complications within 30 days (OR [95% CI] = 0.99 [0.98–0.99], p = 0.03). Through multivariate analysis, it was found that lower muscle CSA was also associated with an increased postoperative complication rate after controlling for the albumin, ASIA score, previous systemic therapy, and thoracic metastases (OR [95% CI] = 0.99 [0.98–0.99], p = 0.047). LOS and reoperations were not associated with any body composition measures. Low muscle mass may serve as a biomarker for the prediction of complications in patients with spinal metastases. The routine assessment of muscle mass on opportunistic CTs may help to predict outcomes in these patients. Full article
(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Oncology)
Show Figures

Figure 1

Back to TopTop