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Completed

NCT Number: NCT07215728

Machine Learning Applied to EHRs Data of Patients With Sarcoma

Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma.

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Key information

About this study

This observational, retrospective, multicenter study will be conducted on a group of patients treated at the Rizzoli Orthopedic Institute in Bologna and followed throughout their treatment. The study population includes patients of both sexes and all ages, affected by the two types of bone sarcoma typical of young people, with histologically confirmed diagnoses. The musculoskeletal tumors referred to in the study are osteosarcoma (OS) and Ewing's sarcoma (ES). Both are rare and very aggressive tumors, with a prognosis that remains unsatisfactory. These characteristics limit the possibility of conducting ad hoc studies on large case series that would allow the characterization of patients affected by these conditions in order to identify prognostic predictors. The clinical registries of specialized centers such as the Rizzoli Orthopedic Institute (IOR), which has always been a reference point for the diagnosis and treatment of sarcomas, are a source of very relevant data in this regard, allowing the collection of observational data gathered prospectively over time. The aim of this retrospective observational study is to characterize clusters of patients with different prognostic profiles and, secondarily, to identify the most predictive characteristics with respect to the prognosis of patients, applying computational intelligence algorithms using the open-source programming language R to already available data.

At the Simple Departmental Structure (SSD) of Anatomy and Pathological Histology of the Rizzoli Orthopaedic Institute (IOR), two datasets containing these variables are available and ready for use:

  • patients diagnosed with osteosarcoma at the IOR between January 1, 2003, and December 31, 2012.
  • patients diagnosed with Ewing's sarcoma at the IOR from 01/01/2003 to 31/12/2012.

Following ethical approval, access to these data will be requested, to be subsequently analyzed with computational intelligence algorithms (e.g., Random Forests) to determine the characteristics most predictive of prognosis (using a technique called "recursive feature elimination").

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

confirmed diagnosis of osteosarcoma or Ewing sarcoma between 2003 and 2012 at the IRCCS Rizzoli Orthopaedic Institute.

Exclusion criteria

diagnosis other than osteosarcoma or Ewing sarcoma and/or diagnosis made before 2003 and after 2012.

Treatment and study plan

No intervention studied

Other

No intervention studied

Primary outcomes

  1. Survival

    Time frame: 6 months

    Survival of patients during the follow-up

Sponsors and collaborators

Lead sponsor

University of Milano Bicocca

Other

Collaborators

  • IRCCS Istituto Ortopedico Rizzoli di Bologna

Registry information

Official study title

Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma

Acronym: AMLAS

Important dates

Study start
2003
Primary completion
2012
Study completion
2012
First posted
Oct 10, 2025
Registry last updated
Oct 10, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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