Radiomics for Tumor Grading of Soft Tissue Sarcomas.

NCT03798795 | Completed

• 1 other identifier: Sarcoma_Grading_Radiomics
• Study Type: observational
• Target: 285
• Locations: 1 country
• Sites: 1

Brief Summary

Radiomics is defined as a quantitative high-throughput analysis of imaging data combined with model development aiming to predict biological correlates or clinical endpoints. The investigators of this study hypothesize that radiomic features may correlate with pathology-defined tumor grading in soft tissue sarcoma patients. The aim of this study is to develop a predictive radiomics model for tumor grading determination.

Conditions

Sarcoma, Soft Tissue

Outcome Measures

Primary Outcomes (1)

• Pathological tumor grading

  Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)

  Baseline

Secondary Outcomes (1)

• Overall Survival

  From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with histologically proven soft tissue sarcomas with known FNCLCC tumor grading determined by biopsy prior to therapy.

You may qualify if:

• Histologically proven soft tissue sarcoma
• Available pre-therapeutic MRI with a contrast-enhanced T1 weight fat saturated sequence +/- fat saturated T2 sequences (e.g. STIR)

You may not qualify if:

• Indeterminate tumor grading
• Osteosarcoma
• Ewing Sarcoma
• Endoprothesis-dependent MRI artifacts
• Previous radiotherapy or chemotherapy
• Lack of a contrast-enhanced T1 weight fat saturated MRI sequence

Sponsors & Collaborators

• Technical University of Munich: lead
• University of Washington: collaborator

Study Sites (1)

Klinik für RadioOnkologie Strahlentherapie

Munich, Bavaria, 81675, Germany

Location

Related Publications (3)

• Liang W, Yang P, Huang R, Xu L, Wang J, Liu W, Zhang L, Wan D, Huang Q, Lu Y, Kuang Y, Niu T. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res. 2019 Jan 15;25(2):584-594. doi: 10.1158/1078-0432.CCR-18-1305. Epub 2018 Nov 5.

  PMID: 30397175 BACKGROUND

• Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006.

  PMID: 24892406 BACKGROUND

• Peeken JC, Spraker MB, Knebel C, Dapper H, Pfeiffer D, Devecka M, Thamer A, Shouman MA, Ott A, von Eisenhart-Rothe R, Nusslin F, Mayr NA, Nyflot MJ, Combs SE. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine. 2019 Oct;48:332-340. doi: 10.1016/j.ebiom.2019.08.059. Epub 2019 Sep 12.

  PMID: 31522983 DERIVED

MeSH Terms

Conditions

Sarcoma

Condition Hierarchy (Ancestors)

Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms

Study Officials

• Stephanie E Combs, MD

  Technical University of Munich

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 7, 2019
• First Posted: January 10, 2019
• Study Start: October 1, 2017
• Primary Completion: March 31, 2018
• Study Completion: March 1, 2019
• Last Updated: April 10, 2019

Record last verified: 2019-04

Locations

DE (1)