Findings of DTI-p maps in comparison with T2/T2-FLAIR to assess postoperative hyper-signal abnormal regions in patients with glioblastoma.

PURPOSE: The aim of this study was to compare diffusion tensor imaging (DTI) isotropic map (p-map) with current radiographically (T2/T2-FLAIR) methods based on abnormal hyper-signal size and location of glioblastoma tumor using a semi-automatic approach. MATERIALS AND METHODS: Twenty-five patients with biopsy-proved diagnosis of glioblastoma participated in this study. T2, T2-FLAIR images and diffusion tensor imaging (DTI) were acquired 1 week before radiotherapy. Hyper-signal regions on T2, T2-FLAIR and DTI p-map were segmented by means of semi-automated segmentation. Manual segmentation was used as ground truth. Dice Scores (DS) were calculated for validation of semiautomatic method. Discordance Index (DI) and area difference percentage between the three above regions from the three modalities were calculated for each patient. RESULTS: Area of abnormality in the p-map was smaller than the corresponding areas in the T2 and T2-FLAIR images in 17 patients; with mean difference percentage of 30 ± 0.15 and 35 ± 0.15, respectively. Abnormal region in the p-map was larger than the corresponding areas in the T2-FLAIR and T2 images in 4 patients; with mean difference percentage of 26 ± 0.17 and 29 ± 0.28, respectively. This region in the p-map was larger than the one in the T2 image and smaller than the one in the T2-FLAIR image in 3 patients; with mean difference percentage of 34 ± 0.08 and 27 ± 0.06, respectively. Lack of concordance was observed ranged from 0.214-0.772 for T2-FLAIR/p-map (average: 0.462 ± 0.18), 0.266-0.794 for T2 /p-map (average: 0.468 ± 0.13) and 0.123-0.776 for T2/ T2-FLAIR (average: 0.423 ± 0.2). These regions on three modalities were segmented using a semi-automatic segmentation method with over 86% sensitivity, 90% specificity and 89% dice score for three modalities. CONCLUSION: It is noted that T2, T2-FLAIR and DTI p-maps represent different but complementary information for delineation of glioblastoma tumor margins. Therefore, this study suggests DTI p-map modality as a candidate to improve target volume delineation based on conventional modalities, which needs further investigations with follow-up data to be confirmed.

Malignancy probability map as a novel imaging biomarker to predict malignancy distribution: employing MRS in GBM patients.

The main aim of this study was to propose a new statistical method for evaluation of spatial malignancy distribution within Magnetic Resonance Spectroscopy (MRS) grid in Glioblastoma Multiforme patients. Voxels with different malignancy probabilities were presented as a novel MRS-based Malignancy Probability Map (MPM). For this purpose, a predictive probability-based clustering approach was developed, including the two following steps: (1) Gaussian Mixture Model, (2) Quadratic Discriminate Analysis coupled with Genetic Algorithm. Clustered probability values from two methods were then integrated to exploit the MPM. Results show that the suggested method is able to estimate the malignancy distribution with over 90% sensitivity and specificity. The proposed MRS-based MPM has an acceptable accuracy for providing useful complementary information about regional diffuse glioma malignancy, with the potential to lead to better detection of tumoral regions with high probability of malignancy. So, it also may encourage the use of additional information of this map as a tool for dose painting.

Heterogeneity analysis of diffusion-weighted MRI for prediction and assessment of microstructural changes early after one cycle of induction chemotherapy in nasopharyngeal cancer patients.

PURPOSE: To evaluate whether the pretreatment apparent diffusion coefficient (ADC) heterogeneity parameters and their alterations, after one cycle of induction chemotherapy, can be used as reliable markers of treatment response to induction chemotherapy in patients with nasopharyngeal cancer. MATERIALS AND METHODS: Ten patients were recruited and received induction chemotherapy (IC). Diffusion-weighted imaging was performed prior to, during, and after IC. The first-order ADC histogram parameters at the intra-treatment time-point were compared to the baseline time-point in the metastatic lymph nodes (LNs). Some ADC pretreatment parameters were combined with each other, employing discriminant analysis to achieve a feasible model to separate the complete response (CR) from the partial response (PR) groups. RESULTS: For ten patients, significant rise in Mean and Txt1Mean (p = 0.048 and 0.015, respectively) was observed in the metastatic nodes following one cycle of IC. Txt5Energy significantly decreased (p = 0.002). Discriminant analysis on pretreatment parameters illustrated that Txt5Energypre was the best parameter to use to correctly classify CR and PR patients. This was followed by Txt9Percentile75pre, Txt1Meanpre, and Txt2Standard Deviationpre. CONCLUSIONS: Our results suggest that heterogeneity metrics extracted from ADC-maps in metastatic lymph nodes, before and after IC, can be used as supplementary IC response indicators.