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Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases.

ABSTRACT: Our goal was to develop strategies to quantify the accumulation of model therapeutics in small brain metastases using multimodal imaging, in order to enhance the potential for successful treatment. Human melanoma cells were injected into the left cardiac ventricle of immunodeficient mice. Bioluminescent, MR and PET imaging were applied to evaluate the limits of detection and potential for contrast agent extravasation in small brain metastases. A pharmacokinetic model was applied to estimate vascular permeability. Bioluminescent imaging after injecting d-luciferin (molecular weight (MW) 320 D) suggested that tumor cell extravasation had already occurred at week 1, which was confirmed by histology. 7T T1w MRI at week 4 was able to detect non-leaky 100 ?m sized lesions and leaky tumors with diameters down to 200 ?m after contrast injection at week 5. PET imaging showed that (18)F-FLT (MW 244 Da) accumulated in the brain at week 4. Gadolinium-based MRI tracers (MW 559 Da and 2.066 kDa) extravasated after 5 weeks (tumor diameter 600 ?m), and the lower MW agent cleared more rapidly from the tumor (mean apparent permeabilities 2.27 × 10(-5)cm/s versus 1.12 × 10(-5)cm/s). PET imaging further demonstrated tumor permeability to (64)Cu-BSA (MW 65.55 kDa) at week 6 (tumor diameter 700 ?m). In conclusion, high field T1w MRI without contrast may improve the detection limit of small brain metastases, allowing for earlier diagnosis of patients, although the smallest lesions detected with T1w MRI were permeable only to d-luciferin and the amphipathic small molecule (18)F-FLT. Different-sized MR and PET contrast agents demonstrated the gradual increase in leakiness of the blood tumor barrier during metastatic progression, which could guide clinicians in choosing tailored treatment strategies.

SUBMITTER: Thorsen F

PROVIDER: S-EPMC3922207 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases.

Thorsen Frits F Fite Brett B Mahakian Lisa M LM Seo Jai W JW Qin Shengping S Harrison Victoria V Johnson Sarah S Ingham Elizabeth E Caskey Charles C Sundstrøm Terje T Meade Thomas J TJ Harter Patrick N PN Skaftnesmo Kai Ove KO Ferrara Katherine W KW

Journal of controlled release : official journal of the Controlled Release Society 20131029 3

Our goal was to develop strategies to quantify the accumulation of model therapeutics in small brain metastases using multimodal imaging, in order to enhance the potential for successful treatment. Human melanoma cells were injected into the left cardiac ventricle of immunodeficient mice. Bioluminescent, MR and PET imaging were applied to evaluate the limits of detection and potential for contrast agent extravasation in small brain metastases. A pharmacokinetic model was applied to estimate vasc ...[more]

PMID: 24161382

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Project description:BackgroundMetastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.MethodsLongitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.ResultsThe radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.ConclusionsThe proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Project description:Chronic pain patients present with cortical gray matter alterations, observed with anatomical magnetic resonance (MR) imaging. Reduced regional gray matter volumes are often interpreted to reflect neurodegeneration, but studies investigating the cellular origin of gray matter changes are lacking. We used multimodal imaging to compare 26 postmenopausal women with fibromyalgia with 25 healthy controls (age range: 50-75 years) to test whether regional gray matter volume decreases in chronic pain are associated with compromised neuronal integrity. Regional gray matter decreases were largely explained by T1 relaxation times in gray matter, a surrogate measure of water content, and not to any substantial degree by GABAA receptor concentration, an indirect marker of neuronal integrity measured with [18F] flumazenil PET. In addition, the MR spectroscopy marker of neuronal viability, N-acetylaspartate, did not differ between patients and controls. These findings suggest that decreased gray matter volumes are not explained by compromised neuronal integrity. Alternatively, a decrease in neuronal matter could be compensated for by an upregulation of GABAA receptors. The relation between regional gray matter and T1 relaxation times suggests decreased tissue water content underlying regional gray matter decreases. In contrast, regional gray matter increases were explained by GABAA receptor concentration in addition to T1 relaxation times, indicating perhaps increased neuronal matter or GABAA receptor upregulation and inflammatory edema. By providing information on the histological origins of cerebral gray matter alterations in fibromyalgia, this study advances the understanding of the neurobiology of chronic widespread pain.Significance statementRegional gray matter alterations in chronic pain, as detected with voxel-based morphometry of anatomical magnetic resonance images, are commonly interpreted to reflect neurodegeneration, but this assumption has not been tested. We found decreased gray matter in fibromyalgia to be associated with T1 relaxation times, a surrogate marker of water content, but not with GABAA receptor concentration, a surrogate of neuronal integrity. In contrast, regional gray matter increases were partly explained by GABAA receptor concentration, indicating some form of neuronal plasticity. The study emphasizes that voxel-based morphometry is an exploratory measure, demonstrating the need to investigate the histological origin of gray matter alterations for every distinct clinical entity, and advances the understanding of the neurobiology of chronic (widespread) pain.

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Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases.

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Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases.

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