Dataset Information

Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation.

ABSTRACT: Although advanced magnetic resonance imaging (MRI) techniques provide useful information for the differential diagnosis of intra-axial mass-like lesions, the specific diagnostic role of multimodal MRI over conventional magnetic resonance imaging (CMRI) alone in the differential diagnosis of mass-like lesions from a large heterogeneous cohort has not been studied. In this study, we aimed to determine the added value of a joint approach of diffusion-weighted imaging (DWI) and dynamic-susceptibility-contrast perfusion imaging (DSC-PWI) for diagnosis of intra-axial mass-like lesions, comparing them with CMRI alone. Furthermore, we performed these evaluations in a manner simulating clinical practice. Our institutional review board approved this retrospective study and waived the requirement for informed consent. A total of 1038 patients with intra-axial mass-like lesions were retrospectively recruited according to their histological and clinico-radiological diagnoses made between January 2005 and December 2014. All patients underwent CMRI, DWI and DSC-PWI. The diagnostic accuracy and confidence in diagnosing each type of intra-axial mass-like lesions, and for differentiating the intra-axial brain tumors from non-neoplastic lesions, were compared according to the MRI protocols. The disease-specific sensitivity of joint approach differed according to specific disease entities in diagnosing each disease category. Joint approach provided the best diagnostic accuracy for discriminating intra-axial brain tumors from non-neoplastic lesions, with high diagnostic accuracy (95.3-96.7%), specificity (82-84.0%), positive-predictive-value (97.0-97.3%), and negative-predictive-value (84.8-92.7%), with the reader's confidence values being significantly improved over those on CMRI alone (all p-values < 0.001). In conclusion, joint approach of DWI, DSC-PWI to CMRI helps to differentiate non-neoplastic lesions from intra-axial brain tumors, and improves diagnostic confidence compared with CMRI alone. The benefit from the combined imaging differs for each disease category; thus joint approach needs to be customized according to clinical suspicion.

SUBMITTER: Yoon RG

PROVIDER: S-EPMC6128539 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation.

Yoon Ra Gyoung RG Kim Ho Sung HS Hong Gil Sun GS Park Ji Eun JE Jung Seung Chai SC Kim Sang Joon SJ Kim Jeong Hoon JH

PloS one 20180907 9

Although advanced magnetic resonance imaging (MRI) techniques provide useful information for the differential diagnosis of intra-axial mass-like lesions, the specific diagnostic role of multimodal MRI over conventional magnetic resonance imaging (CMRI) alone in the differential diagnosis of mass-like lesions from a large heterogeneous cohort has not been studied. In this study, we aimed to determine the added value of a joint approach of diffusion-weighted imaging (DWI) and dynamic-susceptibilit ...[more]

PMID: 30192785

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Project description:BackgroundAn accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.Material and methodsMulti-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.ResultsStatistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.ConclusionThe results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Project description:BackgroundPseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma.MethodsThe model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23).ResultsTwelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness.ConclusionsIncorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

Dataset Information

Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation.

Publications

Joint approach of diffusion- and perfusion-weighted MRI in intra-axial mass like lesions in clinical practice simulation.

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