Clinical trial · Observational
Machine Learning Applied to EHRs Data of Patients With Sarcoma
Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma
NCT07215728CI-TRIAL-00096481AMLAScompletedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Application of computational statistics and machine learning methods to data derived from electronic health records of patients diagnosed with sarcoma.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Ewing Sarcoma | Ewing Sarcoma | ONTOLOGY_EXACT | 0.98 |
| Osteosarcoma | Osteosarcoma | ONTOLOGY_EXACT | 0.90 |
| Sarcoma | Sarcoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| No intervention studied | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Osteosarcoma
- description
- Data of patients diagnosed with osteosarcoma
- interventionNames
- Other: No intervention studied
- label
- Ewing sarcoma
- description
- Data of patients diagnosed with Ewing sarcoma
- interventionNames
- Other: No intervention studied
Primary outcomes (1)
- measure
- Survival
- timeFrame
- 6 months
- description
- Survival of patients during the follow-up
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 21 Years
Show eligibility criteria text
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.
References
Publications (5)
- BACKGROUNDFernandes 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. PMID 33816808
- BACKGROUNDChicco 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. PMID 33504243
- BACKGROUNDChicco 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. PMID 37708628
- BACKGROUNDCerono 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. PMID 38273986
- BACKGROUNDChicco 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. PMID 40506780