Sarcoma, Soft TissueNCT03798795
Radiomics for Tumor Grading of Soft Tissue Sarcomas.
An observational study of Sarcoma, Soft Tissue, run by Technical University of Munich at 1 site in Germany.
Trial 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… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Sarcoma, Soft Tissue
- How many people
- 285 participants
- Where
- 1 siteGermany
- Study dates
- 1 Oct 2017to 1 Mar 2019
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with histologically proven soft tissue sarcomas with known FNCLCC tumor grading determined by biopsy prior to therapy.
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Germany1 site
Klinik für RadioOnkologie Strahlentherapie
Munich, Bavaria, 81675
About this study
Official title: 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.
Read the 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.
- Conditions
- Sarcoma, Soft Tissue
- Medical subject headings
- Sarcoma
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathological tumor grading
Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)
Measured: Baseline
Secondary outcomes (1)
Overall Survival
Overall Survival
Measured: From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2019
- Study start1 Oct 2017
- Primary completion31 Mar 2018
- Study completion1 Mar 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Sarcoma, Soft TissueStudies of the same condition, open and under way.
- More trials run by Technical University of MunichThe sponsor's other registered studies.
- 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
- 285 participants (actual)
Sponsor and investigators
- Lead sponsor
- Technical University of Munich
- Sponsor type
- University, hospital or other
- Collaborators
- University of Washington
- Responsible party
- Sponsor
- Investigators
- Stephanie E Combs, MD, Principal investigator, Technical University of Munich
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03798795
- Sponsor's protocol ID
- Sarcoma_Grading_Radiomics
- Status last verified
- Apr 2019
- First submitted
- 7 Jan 2019
- Last updated
- 10 Apr 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 30397175Aerts 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.
PubMed 24892406Peeken 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.
PubMed 31522983
Study at a glance
From the registry record, last updated by the sponsor on 10 Apr 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.
- ClinicalTrials.gov ID
- NCT03798795
- Acronym / sponsor ID
- Sarcoma_Grading_Radiomics
- Conditions
- Sarcoma, Soft Tissue
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 285 participants (actual)
- Lead sponsor
- Technical University of MunichUniversity, hospital or other
- Collaborators
- University of Washington
- Study start
- 1 Oct 2017 (actual)
- Primary completion
- 31 Mar 2018 (actual)
- Study completion
- 1 Mar 2019 (actual)
- First posted
- 10 Jan 2019 (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.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with histologically proven soft tissue sarcomas with known FNCLCC tumor grading determined by biopsy prior to therapy.
Key inclusion criteria (2 listed)
- 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)
Key exclusion criteria (6 listed)
- Indeterminate tumor grading
- Osteosarcoma
- Ewing Sarcoma
- Endoprothesis-dependent MRI artifacts
- Previous radiotherapy or chemotherapy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Pathological tumor grading
Time frame: Baseline
Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)
1 secondary outcome
Overall Survival
Time frame: From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.
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 Apr 2019.
- Background: PubMed 30397175Liang 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.
- Background: PubMed 24892406Aerts 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.
- Derived from this study: PubMed 31522983Peeken 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.
- Individual participant dataSharing planned: Undecided
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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.
Stephanie E Combs, MD principal investigator
Technical University of Munich
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sarcoma, Soft Tissue, run by Technical University of Munich at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. 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 Apr 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Pathological tumor grading (time frame: Baseline). 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 NCT03798795, which also lists 3 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 Apr 2019.