Radiomics for Tumor Grading of Soft Tissue Sarcomas.

April 8, 2019 updated by: Technical University of Munich

Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.

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.

Study Overview

Status

Completed

Conditions

Detailed Description

Soft tissue sarcomas (STS) constitute an overall rare malignant entity comprising 1% of all cancers with a yearly incidence rate of 3.8 per 100.000 inhabitants. Therapy decisions are made using clinical and pathological determinants defined by the American Joint Committee on Cancer (AJCC). It involves the TNM staging system that classifies STS by their tumor size (measured as maximal diameter), pathological tumor grading defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) and the occurrence of nodal or distant metastases.

For the guidance of therapy, the most important factor constitutes tumor grading. In "low-grade" sarcomas (G1), surgical resection is often sufficient for durable tumor control. In "high risk" STS, however, resection of the tumor is combined with radiotherapy improving locoregional control and eventually survival.

Currently, invasive biopsies followed by pathological work-up are necessary to determine tumor grading. However, bioptic specimens are always restricted to small tumor subvolume.

Medical imaging-based analyses constitutes an alternative tool to characterize tissue. Recent developments in quantitative image analysis and data science have led to the evolvement of "Radiomics". It is defined as an algorithm-based large-scale quantitative analysis of imaging features. It should be considered as a two-step process with (1) extraction of relevant imaging features, and (2) incorporating these features into a mathematical model to ultimately predict patient or tumor-specific outcomes. In previous scientific studies, radiomic models have been associated with survival, tumor progression, and molecular changes including genetic mutations or expression profiles as shown in multiple malignant entities. In addition, radiomic models were able to predict tumor grading e.g. for gliomas, meningiomas, hepatocellular carcinoma or pancreatic neuroendocrine tumors. In contrast to pathology, quantitative image analysis (radiomics) has the principal advantage of analyzing the whole tumor.

In this study, the investigators are aiming to correlate radiomic features with tumor grading of STS. The ultimate goal is to develop a prediction model to non-invasively classify tumor grading. In a first step, the focus will be laid on differentiating "low-grade" and "high-grade" STS. In a second step, "high-grade" STS will be divided into G2 and G3 tumors.

To this end, the investigators will retrospectively analyze a patient cohort of 138 patients (139 tumors) with known tumor grading and available pre-therapeutic MRI-scans. As secondary endpoint overall survival will be determined for all patients. An independent patient cohort from the University of Washington (139 patients) will be used for external validation of the developed models.

Study Type

Observational

Enrollment (Actual)

285

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Germany
    • Bavaria
      • Munich, Bavaria, Germany, 81675
        • Klinik für RadioOnkologie Strahlentherapie

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • ADULT
  • OLDER_ADULT
  • CHILD

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

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.

Description

Inclusion Criteria:

  • 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)

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Pathological tumor grading
Time Frame: Baseline
Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)
Baseline

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall Survival
Time Frame: From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.
Overall Survival
From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Technical University of Munich

Collaborators

University of Washington

Investigators

  • Principal Investigator: Stephanie E Combs, MD, Technical University of Munich

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2017

Primary Completion (Actual)

March 31, 2018

Study Completion (Actual)

March 1, 2019

Study Registration Dates

First Submitted

January 7, 2019

First Submitted That Met QC Criteria

January 8, 2019

First Posted (Actual)

January 10, 2019

Study Record Updates

Last Update Posted (Actual)

April 10, 2019

Last Update Submitted That Met QC Criteria

April 8, 2019

Last Verified

April 1, 2019

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Sarcoma_Grading_Radiomics

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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