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Comparison of Diffusion Tensor Imaging and Magnetic Resonance Perfusion Imaging in Differentiating Recurrent Brain Neoplasm From Radiation Necrosis.

ABSTRACT: To compare differences in diffusion tensor imaging (DTI) and dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance (MR) perfusion imaging characteristics of recurrent neoplasm and radiation necrosis in patients with brain tumors previously treated with radiotherapy with or without surgery and chemotherapy.Patients with a history of brain neoplasm previously treated with radiotherapy with or without chemotherapy and surgery who developed a new enhancing lesion on posttreatment surveillance MRI were enrolled. DSC perfusion MRI and DTI were performed. Region of interest cursors were manually drawn in the contrast-enhancing lesions, in the perilesional white matter edema, and in the contralateral normal-appearing frontal lobe white matter. DTI and DSC perfusion MR indices were compared in recurrent tumor versus radiation necrosis.Twenty-two patients with 24 lesions were included. Sixteen (67%) lesions were placed into the recurrent neoplasm group and eight (33%) lesions were placed into the radiation necrosis group using biopsy results as the gold standard in all but three patients. Mean apparent diffusion coefficient values, mean parallel eigenvalues, and mean perpendicular eigenvalues in the contrast-enhancing lesion were significantly lower, and relative cerebral blood volume was significantly higher for the recurrent neoplasm group compared to the radiation necrosis group (P?

SUBMITTER: Masch WR

PROVIDER: S-EPMC4836992 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Comparison of Diffusion Tensor Imaging and Magnetic Resonance Perfusion Imaging in Differentiating Recurrent Brain Neoplasm From Radiation Necrosis.

Masch William R WR Wang Page I PI Chenevert Thomas L TL Junck Larry L Tsien Christina C Heth Jason A JA Sundgren Pia C PC

Academic radiology 20160223 5

<h4>Rationale and objectives</h4>To compare differences in diffusion tensor imaging (DTI) and dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance (MR) perfusion imaging characteristics of recurrent neoplasm and radiation necrosis in patients with brain tumors previously treated with radiotherapy with or without surgery and chemotherapy.<h4>Materials and methods</h4>Patients with a history of brain neoplasm previously treated with radiotherapy with or without chemotherapy a ...[more]

PMID: 26916251

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Project description:PurposeTo develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues.Materials and methodsTwenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients' scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients ?1, ?2, ?3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a ?1-vector map, and a main diffusion-direction map.ResultsDTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm² yielded: ?1?=?(2.65±0.35)×10?³, ?2?=?(1.87±0.22)×10?³, ?3?=?(1.20±0.18)×10?³, ADC?=?(1.91±0.22)×10?³ (all in mm²/s units) and FA?=?0.38±0.06. Using b-values of 100,500 s/mm² led to a significant reduction in ?1, ?2, ?3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b?100 s/mm², which was confirmed by the multi-b DWI results. In PDACs, ?1, ?2, ?3 and ADC in both 0,500 s/mm² and 100,500 s/mm² b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue.ConclusionDTI using two reference b-values 0 and 100 s/mm² enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm², helped identifying the presence of malignancy.

Project description:BackgroundThe radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients.ObjectiveThis research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans.MethodsIn this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons.ResultsDNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy).ConclusionsDeep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

Dataset Information

Comparison of Diffusion Tensor Imaging and Magnetic Resonance Perfusion Imaging in Differentiating Recurrent Brain Neoplasm From Radiation Necrosis.

Publications

Comparison of Diffusion Tensor Imaging and Magnetic Resonance Perfusion Imaging in Differentiating Recurrent Brain Neoplasm From Radiation Necrosis.

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