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Molecular MRI enables early and sensitive detection of brain metastases.

ABSTRACT: Metastasis to the brain is a leading cause of cancer mortality. The current diagnostic method of gadolinium-enhanced MRI is sensitive only to larger tumors, when therapeutic options are limited. Earlier detection of brain metastases is critical for improved treatment. We have developed a targeted MRI contrast agent based on microparticles of iron oxide that enables imaging of endothelial vascular cell adhesion molecule-1 (VCAM-1). Our objectives here were to determine whether VCAM-1 is up-regulated on vessels associated with brain metastases, and if so, whether VCAM-1-targeted MRI enables early detection of these tumors. Early up-regulation of cerebrovascular VCAM-1 expression was evident on tumor-associated vessels in two separate murine models of brain metastasis. Metastases were detectable in vivo using VCAM-1-targeted MRI 5 d after induction (<1,000 cells). At clinical imaging resolutions, this finding is likely to translate to detection at tumor volumes two to three orders of magnitude smaller (0.3-3 × 10(5) cells) than those volumes detectable clinically (10(7)-10(8) cells). VCAM-1 expression detected by MRI increased significantly (P < 0.0001) with tumor progression, and tumors showed no gadolinium enhancement. Importantly, expression of VCAM-1 was shown in human brain tissue containing both established metastases and micrometastases. Translation of this approach to the clinic could increase therapeutic options and change clinical management in a substantial number of cancer patients.

SUBMITTER: Serres S

PROVIDER: S-EPMC3340084 | biostudies-other | 2012 Apr

REPOSITORIES: biostudies-other

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Molecular MRI enables early and sensitive detection of brain metastases.

Serres Sébastien S Soto Manuel Sarmiento MS Hamilton Alastair A McAteer Martina A MA Carbonell W Shawn WS Robson Matthew D MD Ansorge Olaf O Khrapitchev Alexandre A Bristow Claire C Balathasan Lukxmi L Weissensteiner Thomas T Anthony Daniel C DC Choudhury Robin P RP Muschel Ruth J RJ Sibson Nicola R NR

Proceedings of the National Academy of Sciences of the United States of America 20120326 17

Metastasis to the brain is a leading cause of cancer mortality. The current diagnostic method of gadolinium-enhanced MRI is sensitive only to larger tumors, when therapeutic options are limited. Earlier detection of brain metastases is critical for improved treatment. We have developed a targeted MRI contrast agent based on microparticles of iron oxide that enables imaging of endothelial vascular cell adhesion molecule-1 (VCAM-1). Our objectives here were to determine whether VCAM-1 is up-regula ...[more]

PMID: 22451897

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Project description:BackgroundApproximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective.PurposeTo develop a deep-learning-based approach for finding brain metastasis on MRI.Study typeRetrospective.SequenceAxial postcontrast 3D T1 -weighted imaging.Field strength1.5T and 3T.PopulationA total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing.AssessmentTwo radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected.Statistical testsThe performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice.ResultsTesting on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79.ConclusionOur results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity.Level of evidence3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.

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.

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Molecular MRI enables early and sensitive detection of brain metastases.

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Molecular MRI enables early and sensitive detection of brain metastases.

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