SarcomaAMLASNCT07215728
Machine Learning Applied to EHRs Data of Patients With Sarcoma
An observational study of Sarcoma and Osteosarcoma, run by University of Milano Bicocca.
Trial summary
Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma. Read more about this study
- Who is it for?
- Ages 21 years and older
- Conditions the study is for
- Sarcoma, Osteosarcoma, Ewing Sarcoma
- How many people
- 700 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2003to 31 Dec 2012
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Information unavailable.
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.
About this study
Official title: Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma
Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma.
Read the detailed description
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
- Sarcoma, Osteosarcoma, Ewing Sarcoma
- Being tested
- No intervention studied
- Keywords
- sarcoma, osteosarcoma, Ewing sarcoma, machine learning, computational statistics
- Medical subject headings
- Sarcoma, Osteosarcoma, Sarcoma, Ewing
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention studiedOther
No intervention studied
| Study group | Role | What they receive |
|---|---|---|
| Osteosarcoma | Data of patients diagnosed with osteosarcoma Receives: Other: No intervention studied | |
| Ewing sarcoma | Data of patients diagnosed with Ewing sarcoma Receives: Other: No intervention studied |
What is being measured
The primary outcome is the main question the study is designed to answer.
Survival
Survival of patients during the follow-up
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Oct 2025
- Study start1 Jan 2003
- Primary completion31 Dec 2012
- Study completion31 Dec 2012
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for SarcomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 700 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Milano Bicocca
- Sponsor type
- University, hospital or other
- Collaborators
- IRCCS Istituto Ortopedico Rizzoli di Bologna
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07215728
- Sponsor's protocol ID
- AMLAS2025
- Other IDs
- AMLAS2025
- Acronym
- AMLAS
- Status last verified
- Sep 2025
- First submitted
- 2 Oct 2025
- Last updated
- 10 Oct 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: If we have the authorization from the Ethical Committee of the IOR hospital, we might consider sharing the data in the future.
Publications
Fernandes K, Chicco D, Cardoso JS, Fernandes J. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Comput Sci. 2018 May 14;4:e154. doi: 10.7717/peerj-cs.154. eCollection 2018.
PubMed 33816808Chicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics J. 2021 Jan-Mar;27(1):1460458220984205. doi: 10.1177/1460458220984205.
PubMed 33504243Chicco D, Haupt R, Garaventa A, Uva P, Luksch R, Cangelosi D. Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction. Eur J Cancer. 2023 Nov;193:113291. doi: 10.1016/j.ejca.2023.113291. Epub 2023 Aug 19.
PubMed 37708628Cerono G, Melaiu O, Chicco D. Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme. J Healthc Inform Res. 2023 Sep 20;8(1):1-18. doi: 10.1007/s41666-023-00138-1. eCollection 2024 Mar.
PubMed 38273986Chicco D, Oneto L, Cangelosi D. DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma. BioData Min. 2025 Jun 12;18(1):40. doi: 10.1186/s13040-025-00455-8.
PubMed 40506780
Study at a glance
From the registry record, last updated by the sponsor on 10 Oct 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma
- ClinicalTrials.gov ID
- NCT07215728
- Acronym / sponsor ID
- AMLAS / AMLAS2025
- Conditions
- Sarcoma, Osteosarcoma, Ewing Sarcoma
- Keywords
- sarcoma, osteosarcoma, Ewing sarcoma, machine learning, computational statistics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 700 participants (actual)
- Lead sponsor
- University of Milano BicoccaUniversity, hospital or other
- Collaborators
- IRCCS Istituto Ortopedico Rizzoli di Bologna
- Study start
- 1 Jan 2003 (actual)
- Primary completion
- 31 Dec 2012 (actual)
- Study completion
- 31 Dec 2012 (actual)
- First posted
- 10 Oct 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma.
Detailed description
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").
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Osteosarcoma | – | Other: No intervention studiedData of patients diagnosed with osteosarcoma |
| Ewing sarcoma | – | Other: No intervention studiedData of patients diagnosed with Ewing sarcoma |
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Information unavailable.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention studiedNo intervention studied | Other | Ewing sarcoma, Osteosarcoma |
Primary outcome
Survival
Time frame: 6 months
Survival of patients during the follow-up
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Oct 2025.
- Background: PubMed 33816808Fernandes K, Chicco D, Cardoso JS, Fernandes J. Supervised deep learning embeddings for the prediction of cervical cancer diagnosis. PeerJ Comput Sci. 2018 May 14;4:e154. doi: 10.7717/peerj-cs.154. eCollection 2018.
- Background: PubMed 33504243Chicco D, Oneto L. Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma. Health Informatics J. 2021 Jan-Mar;27(1):1460458220984205. doi: 10.1177/1460458220984205.
- Background: PubMed 37708628Chicco D, Haupt R, Garaventa A, Uva P, Luksch R, Cangelosi D. Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction. Eur J Cancer. 2023 Nov;193:113291. doi: 10.1016/j.ejca.2023.113291. Epub 2023 Aug 19.
- Background: PubMed 38273986Cerono G, Melaiu O, Chicco D. Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme. J Healthc Inform Res. 2023 Sep 20;8(1):1-18. doi: 10.1007/s41666-023-00138-1. eCollection 2024 Mar.
- Background: PubMed 40506780Chicco D, Oneto L, Cangelosi D. DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma. BioData Min. 2025 Jun 12;18(1):40. doi: 10.1186/s13040-025-00455-8.
- Individual participant dataSharing planned: Undecided. If we have the authorization from the Ethical Committee of the IOR hospital, we might consider sharing the data in the future.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Sarcoma and Osteosarcoma, run by University of Milano Bicocca.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 10 Oct 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Survival (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07215728, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Oct 2025.