Machine Learning Applied to EHRs Data of Patients With Sarcoma

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

700 participants

Study Classification

OBSERVATIONAL

Study Start Date

2003-01-01

Study Completion Date

2012-12-31

Brief Summary

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

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Detailed Description

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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").

Conditions

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Sarcoma Osteosarcoma Ewing Sarcoma

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Osteosarcoma

Data of patients diagnosed with osteosarcoma

No intervention studied

Intervention Type OTHER

No intervention studied

Ewing sarcoma

Data of patients diagnosed with Ewing sarcoma

No intervention studied

Intervention Type OTHER

No intervention studied

Interventions

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No intervention studied

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

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.

Minimum Eligible Age

21 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No