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Diagnostics
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Artificial Intelligence in Orthopedic Oncology
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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: closed (30 June 2024) | Viewed by 3232

Special Issue Editor

Dr. Olivier Q. Groot

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

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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 1006
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)
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Review

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14 pages, 518 KiB  
Review
Predictive Modeling for Spinal Metastatic Disease
by Akash A. Shah and Joseph H. Schwab
Diagnostics 2024, 14(9), 962; https://doi.org/10.3390/diagnostics14090962 - 5 May 2024
Cited by 1 | Viewed by 1514
Abstract
Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver [...] Read more.
Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians’ ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow. Full article
(This article belongs to the Special Issue Artificial Intelligence in Orthopedic Oncology)
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