Surgery for Osteosarcoma

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (1 July 2024) | Viewed by 1083

Special Issue Editor


E-Mail Website1 Website2
Guest Editor
1. Department of Pediatric Orthopedic Oncology, Prionces Maxima Center for pediatric Cancer, Heidelberglaan 25, 3584 CS Utrecht, The Netherlands
2. Department of Orthopedic Oncology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
Interests: bone and soft tissue sarcomas; pediatric orthopedics; surgical treatment; limb reconstruction
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Special Issue Information

Dear Colleagues,

We cordially invite researchers, surgeons, and medical professionals from around the world to submit their papers to the upcoming Special Issue of Cancers, entitled "Surgery for Osteosarcoma". This Special Issue aims to collect papers that explore groundbreaking advancements in osteosarcoma surgery.

Osteosarcoma, a highly aggressive bone cancer, poses significant challenges to patients and medical practitioners alike. We believe that the key to improving outcomes lies in the continual exploration and implementation of innovative surgical approaches. By fostering collaboration and knowledge sharing, we aim to accelerate progress in the field and offer renewed hope to those affected by this devastating disease.

We encourage researchers and practitioners to submit papers that delve into a wide range of topics related to surgical innovations for osteosarcoma. Potential areas of interest include, but are not limited to, the following:

Limb-salvage surgery techniques: Novel approaches and advancements in preserving limb functionality while effectively removing the tumor;

Customized implant solutions: Application of 3D printing and advanced imaging technologies to create personalized implants for improved surgical outcomes;

Integration of navigation systems: Utilization of computer-assisted navigation systems to enhance precision, optimize surgical planning, and minimize complications;

Adjuvant therapies: Investigation of the role of adjuvant therapies, such as targeted therapies and immunotherapies, in conjunction with surgical interventions to enhance treatment efficacy.

Rehabilitation and functional outcomes: Evaluation of rehabilitation strategies and long-term functional outcomes following surgical interventions for osteosarcoma.

Prof. Dr. Michiel A. J. van de Sande
Guest Editor

Manuscript Submission Information

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Keywords

  • limb salvage
  • biological reconstruction
  • prothesis
  • non-invasive growing prosthesis
  • amputation
  • shared decision making
  • quality of life
  • PROM
  • complications
  • lung metastasis
  • fluorescence-/navigation-guided surgery
  • 3D printing
  • saw guides

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Published Papers (1 paper)

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17 pages, 12206 KiB  
Article
Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data
by Marta Spreafico, Audinga-Dea Hazewinkel, Michiel A. J. van de Sande, Hans Gelderblom and Marta Fiocco
Cancers 2024, 16(16), 2880; https://doi.org/10.3390/cancers16162880 - 19 Aug 2024
Viewed by 739
Abstract
Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the [...] Read more.
Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data. Full article
(This article belongs to the Special Issue Surgery for Osteosarcoma)
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