GlioblastomaRadiomicsNCT02666066
Radiomics for Prediction of Survival in GBM
An observational study of Glioblastoma and Glioma, run by Maastricht Radiation Oncology at 1 site in Netherlands.
Trial summary
Radiomics, the extraction of large amounts of quantitative image features to convert medical images into minable data, is an in-development field that intends to provide accurate risk stratification of oncologic patients. Published prognostic scores only take clinical variables into account. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Glioblastoma, Glioma, Astrocytoma
- How many people
- 289 participants
- Where
- 1 siteNetherlands
- Study dates
- Jan 2016to Jul 2018
- 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: Histologically proven glioblastoma, diagnosed with a biopsy only, and treated with curative intent
Who may be able to join
- Histologically proven glioblastoma
- Diagnosed with a biopsy only
- Treated with curative intent
- Required data available (clinical/radiological/radiotherapy structure set)
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
Maastricht Radiation Oncology (MAASTRO clinic)
Maastricht, Limburg, 6229ET
About this study
Official title: Radiomics for the Prediction of Survival in GBM After Radiotherapy With/Without Temozolomide
Radiomics, the extraction of large amounts of quantitative image features to convert medical images into minable data, is an in-development field that intends to provide accurate risk stratification of oncologic patients. Published prognostic scores only take clinical variables into account. The investigators hypothesize that a combination of CT/MRI features, molecular biology and clinical data can provide an accurate prediction of medical outcome. The long term objective is to build a Decision Support System based on the predictive models established in this study.
Read the detailed description
Human oncologic tissues exhibit strong phenotypic differences. Due to advances in both acquisition and analysis methods of medical imaging technologies, the extraction of reliable and informative image features to quantify these differences is currently possible. Radiomics, the extraction of large amounts of quantitative image features to convert medical images into minable data, is an in-development field that intends to provide accurate risk stratification of oncologic patients. Previous studies at Maastro demonstrated the importance of a large number (n=440) of these radiomics features to quantify the tumor phenotype by intensity, shape and texture. A landmark study was the extraction of imaging features from computed tomography (CT) data of 943 patients with non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC) cancer in six distinct datasets 1. The model was trained on lung cancer patients and showed that a large number of radiomics features also have strong prognostic power in head and neck cancer patients. These data suggest that radiomics signatures decode a general prognostic phenotype existing in both NSCLC and HNSCC patients.
The investigators anticipate that their results will be a starting point of a novel field applying advanced computational methodologies on to medical imaging data, merging fields of medical imaging and bioinformatics. Also, as CT imaging has been applied in routine clinical oncology practice over decades in almost every hospital worldwide, the application of their analysis has potential to improve decision support in a large number of cancer patients.
Glioblastoma (GBM) are the most common type of primary brain tumors with an annual incidence of approximately 500 patients in the Netherlands. Despite extensive treatment including a resection, radiation therapy and chemotherapy, the median overall survival is only 14.6 months 7. On CT and magnetic resonance imaging (MRI) GBM usually appear as a heterogeneous tumor with central areas of necrosis, surrounded by thick irregular walls of solid, living neoplastic tissue. The gross tumor is often surrounded by extensive edema and it usually exerts considerable mass effect. So far several prognostic and predictive factors have been identified including age, performance status, extent of resection and biomarkers such as MGMT, EGFRvIII and IDH1. However, the value of imaging biomarkers such as radiomics has not yet fully been explored.
Radiomics can have an important role in the prediction of prognosis for patients with a GBM. As some patients only survive a few months, a subset of patients (10%) survives more than 5 years after diagnosis. Identification of these patients may benefit treatment decision by e.g. offering short-term survivors best-supportive care.
The investigators hypothesize that a combination of CT/MRI features, molecular biology and clinical data can provide an accurate prediction of medical outcome. The long term objective is to build a Decision Support System based on the predictive models established in this study.
An extensive dataset consisting of imaging, clinical, treatment data and outcomes of 360 patients treated in Maastricht since 2004 has been retrospectively collected. This includes 128 patients diagnosed with a biopsy only, with a tumor in situ on the planning CT. This dataset will be used to build predictive models of outcome (survival at 6- and 12 months). This analysis will be complemented by a radiomics study, analyzing both CT and MRI radiomics features.
In order to prove its value the signature will be validated on external datasets.
- Conditions
- Glioblastoma, Glioma, Astrocytoma
- Medical subject headings
- Glioblastoma, Glioma, Astrocytoma
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitive value of 6 months survival after radiotherapy with radiomics
Measured: 6 months
Secondary outcomes (3)
Sensitive value of 12 months survival after radiotherapy with radiomics
Measured: 12 months
Specificity value of 6 months survival after radiotherapy with radiomics
Measured: 6 months
Specificity value of 12 months survival after radiotherapy with radiomics
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2016 (estimated)
- Study startJan 2016
- Primary completionJan 2018
- Study completionJul 2018
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 GlioblastomaStudies 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
- 289 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht Radiation Oncology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02666066
- Sponsor's protocol ID
- 15-43-05/08-intern
- Acronym
- Radiomics
- Status last verified
- Aug 2018
- First submitted
- 24 Jan 2016
- Last updated
- 17 Aug 2018
- Results posted
- No
- Data monitoring committee
- No
- Individual data sharing
- No
Publications
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.
PubMed 24892406Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012 Mar;48(4):441-6. doi: 10.1016/j.ejca.2011.11.036. Epub 2012 Jan 16.
PubMed 22257792Carvalho S, Leijenaar RT, Velazquez ER, Oberije C, Parmar C, van Elmpt W, Reymen B, Troost EG, Oellers M, Dekker A, Gillies R, Aerts HJ, Lambin P. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol. 2013 Oct;52(7):1398-404. doi: 10.3109/0284186X.2013.812795. Epub 2013 Sep 9.
PubMed 24047338Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, Hoekstra CJ, Boellaard R, Dekker AL, Gillies RJ, Aerts HJ, Lambin P. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013 Oct;52(7):1391-7. doi: 10.3109/0284186X.2013.812798. Epub 2013 Sep 9.
PubMed 24047337Panth KM, Leijenaar RT, Carvalho S, Lieuwes NG, Yaromina A, Dubois L, Lambin P. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol. 2015 Sep;116(3):462-6. doi: 10.1016/j.radonc.2015.06.013. Epub 2015 Jul 7.
PubMed 26163091
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Radiomics for the Prediction of Survival in GBM After Radiotherapy With/Without Temozolomide
- ClinicalTrials.gov ID
- NCT02666066
- Acronym / sponsor ID
- Radiomics / 15-43-05/08-intern
- Conditions
- Glioblastoma, Glioma, Astrocytoma
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 289 participants (actual)
- Lead sponsor
- Maastricht Radiation OncologyUniversity, hospital or other
- Study start
- Jan 2016
- Primary completion
- Jan 2018 (actual)
- Study completion
- Jul 2018 (actual)
- First posted
- 28 Jan 2016 (estimated)
- 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, the extraction of large amounts of quantitative image features to convert medical images into minable data, is an in-development field that intends to provide accurate risk stratification of oncologic patients. Published prognostic scores only take clinical variables into account. The investigators hypothesize that a combination of CT/MRI features, molecular biology and clinical data can provide an accurate prediction of medical outcome. The long term objective is to build a Decision Support System based on the predictive models established in this study.
Detailed description
Human oncologic tissues exhibit strong phenotypic differences. Due to advances in both acquisition and analysis methods of medical imaging technologies, the extraction of reliable and informative image features to quantify these differences is currently possible. Radiomics, the extraction of large amounts of quantitative image features to convert medical images into minable data, is an in-development field that intends to provide accurate risk stratification of oncologic patients. Previous studies at Maastro demonstrated the importance of a large number (n=440) of these radiomics features to quantify the tumor phenotype by intensity, shape and texture. A landmark study was the extraction of imaging features from computed tomography (CT) data of 943 patients with non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC) cancer in six distinct datasets 1. The model was trained on lung cancer patients and showed that a large number of radiomics features also have strong prognostic power in head and neck cancer patients. These data suggest that radiomics signatures decode a general prognostic phenotype existing in both NSCLC and HNSCC patients.
The investigators anticipate that their results will be a starting point of a novel field applying advanced computational methodologies on to medical imaging data, merging fields of medical imaging and bioinformatics. Also, as CT imaging has been applied in routine clinical oncology practice over decades in almost every hospital worldwide, the application of their analysis has potential to improve decision support in a large number of cancer patients.
Glioblastoma (GBM) are the most common type of primary brain tumors with an annual incidence of approximately 500 patients in the Netherlands. Despite extensive treatment including a resection, radiation therapy and chemotherapy, the median overall survival is only 14.6 months 7. On CT and magnetic resonance imaging (MRI) GBM usually appear as a heterogeneous tumor with central areas of necrosis, surrounded by thick irregular walls of solid, living neoplastic tissue. The gross tumor is often surrounded by extensive edema and it usually exerts considerable mass effect. So far several prognostic and predictive factors have been identified including age, performance status, extent of resection and biomarkers such as MGMT, EGFRvIII and IDH1. However, the value of imaging biomarkers such as radiomics has not yet fully been explored.
Radiomics can have an important role in the prediction of prognosis for patients with a GBM. As some patients only survive a few months, a subset of patients (10%) survives more than 5 years after diagnosis. Identification of these patients may benefit treatment decision by e.g. offering short-term survivors best-supportive care.
The investigators hypothesize that a combination of CT/MRI features, molecular biology and clinical data can provide an accurate prediction of medical outcome. The long term objective is to build a Decision Support System based on the predictive models established in this study.
An extensive dataset consisting of imaging, clinical, treatment data and outcomes of 360 patients treated in Maastricht since 2004 has been retrospectively collected. This includes 128 patients diagnosed with a biopsy only, with a tumor in situ on the planning CT. This dataset will be used to build predictive models of outcome (survival at 6- and 12 months). This analysis will be complemented by a radiomics study, analyzing both CT and MRI radiomics features.
In order to prove its value the signature will be validated on external datasets.
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
- Histologically proven glioblastoma, diagnosed with a biopsy only, and treated with curative intent
Key inclusion criteria (4 listed)
- Histologically proven glioblastoma
- Diagnosed with a biopsy only
- Treated with curative intent
- Required data available (clinical/radiological/radiotherapy structure set)
Key exclusion criteria (1 listed)
- -
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
Sensitive value of 6 months survival after radiotherapy with radiomics
Time frame: 6 months
3 secondary outcomes
Sensitive value of 12 months survival after radiotherapy with radiomics
Time frame: 12 months
Specificity value of 6 months survival after radiotherapy with radiomics
Time frame: 6 months
Specificity value of 12 months survival after radiotherapy with radiomics
Time frame: 12 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 17 Aug 2018.
- 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.
- Background: PubMed 22257792Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012 Mar;48(4):441-6. doi: 10.1016/j.ejca.2011.11.036. Epub 2012 Jan 16.
- Background: PubMed 24047338Carvalho S, Leijenaar RT, Velazquez ER, Oberije C, Parmar C, van Elmpt W, Reymen B, Troost EG, Oellers M, Dekker A, Gillies R, Aerts HJ, Lambin P. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol. 2013 Oct;52(7):1398-404. doi: 10.3109/0284186X.2013.812795. Epub 2013 Sep 9.
- Background: PubMed 24047337Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, Hoekstra CJ, Boellaard R, Dekker AL, Gillies RJ, Aerts HJ, Lambin P. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013 Oct;52(7):1391-7. doi: 10.3109/0284186X.2013.812798. Epub 2013 Sep 9.
- Background: PubMed 26163091Panth KM, Leijenaar RT, Carvalho S, Lieuwes NG, Yaromina A, Dubois L, Lambin P. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol. 2015 Sep;116(3):462-6. doi: 10.1016/j.radonc.2015.06.013. Epub 2015 Jul 7.
- Radiomics: Decoding the Tumor Phenotype with Non-Invasive ImagingLink provided by the sponsor.
- Individual participant dataSharing planned: No
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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.
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 Glioblastoma and Glioma, run by Maastricht Radiation Oncology at 1 site in Netherlands.
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 4 inclusion and 1 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 17 Aug 2018. 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 Netherlands. 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?
Sensitive value of 6 months survival after radiotherapy with radiomics (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 NCT02666066, 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 17 Aug 2018.