DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapya)

Authors

  • Farjam Reza,

    1. Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010
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  • Tsien Christina I.,

    1. Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010
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  • Lawrence Theodore S.,

    1. Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010
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  • Cao Yue

    Corresponding author
    1. Department of Radiation Oncology, University of Michigan, 1500 East Medical Center Drive, SPC 5010, Ann Arbor, Michigan 48109-5010; Department of Radiology, University of Michigan, 1500 East Medical Center Drive, Med Inn Building C478, Ann Arbor, Michigan 48109-5842; and Department of Biomedical Engineering, University of Michigan, 2200 Bonisteel Boulevard, Ann Arbor, Michigan 48109-2099
    • Author to whom correspondence should be addressed. Electronic mail: yuecao@umich.edu; Tel.: (734)647-2914; Fax: (734)936-7859.

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  • a)

    This work is in part presented in the 21th annual meeting and exhibition of International Society for Magnetic Resonance in Medicine (ISMRM 2013).

Abstract

Purpose:

To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy.

Methods:

Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes.

Results:

The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment.

Conclusions:

The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

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