Radiomics for Prediction of Long Term Survival and Local Failure After Stereotactic Radiotherapy for Brain Metastases

NCT ID: NCT02265549

Last Updated: 2018-01-26

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

155 participants

Study Classification

OBSERVATIONAL

Study Start Date

2014-05-31

Study Completion Date

2014-09-04

Brief Summary

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Published prognostic scores have limitations in prediction of long term survival after stereotactic radiotherapy for brain metastases. Moreover, no validated tools are available for prediction of local failure. The value of radiomics is evaluated in this perspective.

Detailed Description

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Sensitivity is low for prediction of long term survival of currently available prognostic scores for brain metastases. First the current clinical models will be optimized with a new prognostic model and nomogram. Radiomics is a tool to analyze tumor characteristics based on CT and MR images. These tumor characteristics may reflect tumor biology and outcome. Therefore it is investigated if radiomics improves prediction of long term survival after stereotactic radiotherapy for brain metastases. Moreover, the predictive value of radiomics for local failure is investigated.

Conditions

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Metastases, Neoplasm

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Cerebral metastasized patients

Exclusion Criteria

\-
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Maastricht Radiation Oncology

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Maastro Clinic

Maastricht, , Netherlands

Site Status

Countries

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Netherlands

References

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Zindler JD, Rodrigues G, Haasbeek CJ, De Haan PF, Meijer OW, Slotman BJ, Lagerwaard FJ. The clinical utility of prognostic scoring systems in patients with brain metastases treated with radiosurgery. Radiother Oncol. 2013 Mar;106(3):370-4. doi: 10.1016/j.radonc.2013.01.015. Epub 2013 Mar 20.

Reference Type RESULT
PMID: 23522151 (View on PubMed)

Rodrigues G, Zindler J, Warner A, Lagerwaard F. Recursive partitioning analysis for the prediction of stereotactic radiosurgery brain metastases lesion control. Oncologist. 2013;18(3):330-5. doi: 10.1634/theoncologist.2012-0316. Epub 2013 Feb 19.

Reference Type RESULT
PMID: 23429647 (View on PubMed)

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.

Reference Type RESULT
PMID: 24892406 (View on PubMed)

Other Identifiers

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Radiomics SRT 09-2014

Identifier Type: -

Identifier Source: org_study_id

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