Dataset Information

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.

ABSTRACT:

Background

Pseudoprogression 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.

Methods

The 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).

Results

Twelve 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.

Conclusions

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.

SUBMITTER: Kim JY

PROVIDER: S-EPMC6380413 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.

Kim Jung Youn JY Park Ji Eun JE Jo Youngheun Y Shim Woo Hyun WH Nam Soo Jung SJ Kim Jeong Hoon JH Yoo Roh-Eul RE Choi Seung Hong SH Kim Ho Sung HS

Neuro-oncology 20190201 3

<h4>Background</h4>Pseudoprogression 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.<h4>Methods</h4>The 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 ...[more]

PMID: 30107606

Similar Datasets

Project description:BackgroundThe advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with histologically proven pancreatic cancer.MethodsIn this study, a total of 143 participants (63 years ± 13, 48 females) who underwent third-generation dual-source DECT and DWI between November 2014 and October 2022 were included. Among these, 83 received a final diagnosis of pancreatic cancer, 20 had pancreatitis, and 40 had no evidence of pancreatic pathologies. Data comparisons were performed using chi-square statistic tests, one-way ANOVA, or two-tailed Student's t-test. For the assessment of the association of texture features with overall survival, receiver operating characteristics analysis and Cox regression tests were used.ResultsMalignant pancreatic tissue differed significantly from normal or inflamed tissue regarding radiomics features (overall P < .001, respectively) and iodine uptake (overall P < .001, respectively). The performance for the distinction of malignant from normal or inflamed pancreatic tissue ranged between an AUC of ≥ 0.995 (95% CI, 0.955-1.0; P < .001) for radiomics features, ≥ 0.852 (95% CI, 0.767-0.914; P < .001) for DECT-IC, and ≥ 0.690 (95% CI, 0.587-0.780; P = .01) for DWI, respectively. During a follow-up of 14 ± 12 months (range, 10-44 months), the multiparametric approach showed a moderate prognostic power to predict all-cause mortality (c-index = 0.778 [95% CI, 0.697-0.864], P = .01).ConclusionsOur reported multiparametric approach allowed for accurate discrimination of pancreatic cancer and revealed great potential to provide independent prognostic information on all-cause mortality.

Project description:To evaluate the incremental value of diffusion-weighted (DW) imaging and apparent diffusion coefficient (ADC) mapping in relation to conventional breast magnetic resonance (MR) imaging in the characterization of benign versus malignant breast lesions at 3.0 T.This retrospective HIPAA-compliant study was approved by the institutional review board, with the requirement for informed patient consent waived. Of 550 consecutive patients who underwent bilateral breast MR imaging over a 10-month period, 93 women with 101 lesions met the following study inclusion criteria: They had undergone three-dimensional (3D) high-spatial-resolution T1-weighted contrast material-enhanced MR imaging, dynamic contrast-enhanced MR imaging, and DW imaging examinations at 3.0 T and either had received a pathologic analysis-proven diagnosis (96 lesions) or had lesion stability confirmed at more than 2 years of follow-up (five lesions). DW images were acquired with b values of 0 and 600 sec/mm(2). Regions of interest were drawn on ADC maps of breast lesions and normal glandular tissue. Morphologic features (margin, enhancement pattern), dynamic contrast-enhanced MR results (semiquantitative kinetic curve data), absolute ADCs, and glandular tissue-normalized ADCs were included in multivariate models to predict a diagnosis of benign versus malignant lesion.Forty-one (44%) of the 93 patients were premenopausal, and 52 (56%) were postmenopausal. Thirty-three (32.7%) of the 101 lesions were benign, and 68 (67.3%) were malignant. Normalized ADCs were significantly different between the benign (mean ADC, 1.1 x 10(-3) mm(2)/sec +/- 0.4 [standard deviation]) and malignant (mean ADC, 0.55 x 10(-3) mm(2)/sec +/- 0.16) lesions (P < .001). Adding normalized ADCs to the 3D T1-weighted and dynamic contrast-enhanced MR data improved the diagnostic performance of MR imaging: The area under the receiver operating characteristic curve improved from 0.89 to 0.98, and the false-positive rate decreased from 36% (nine of 25 lesions) to 24% (six of 25 lesions).DW imaging with glandular tissue-normalized ADC assessment improves the characterization of breast lesions beyond the characterization achieved with conventional 3D T1-weighted and dynamic contrast-enhanced MR imaging at 3.0 T.

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.

Dataset Information

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.

Background

Methods

Results

Conclusions

Publications

Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets