Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up

1H-MRS parameters in non-enhancing peritumoral regions can predict the recurrence of glioblastoma

This study aimed to evaluate the predictive value of metabolic parameters in preoperative non-enhancing peritumoral regions (NEPTRs) for glioblastoma recurrence, using multivoxel hydrogen proton magnetic resonance spectroscopy (1H-MRS). Clinical and imaging data from patients with recurrent glioblastoma were analyzed. Through co-registration of preoperative and post-recurrence MRI, we identified future tumor recurrence regions (FTRRs) and future non-tumor recurrence regions (FNTRRs) within the NEPTRs. Metabolic parameters were recorded separately for each region. Cox regression analysis was applied to assess the association between metabolic parameters and glioblastoma recurrence. Compared to FNTRRs, FTRRs exhibited a higher Cho/Cr ratio, higher Cho/NAA ratio, and lower NAA/Cr ratio. Both Cho/NAA and Cho/Cr ratios were recognized as risk factors in univariate and multivariate analyses (P < 0.05). The Cox regression model indicated that Cho/NAA > 1.99 and Cho/Cr > 1.73 are independent risk factors for early glioblastoma recurrence. Based on these cut-off values, patients were stratified into low-risk and high-risk groups, with a statistically significant difference in recurrence rates between the two groups (P < 0.01). The Cho/NAA and Cho/Cr ratios in NEPTRs are independent predictors of future glioblastoma recurrence. Specifically, Cho/NAA > 1.99 and/or Cho/Cr > 1.73 in NEPTRs may indicate a higher risk of early postoperative recurrence at these regions.

Can quantifying the extent of 'high grade' features help explain prognostic variability in anaplastic astrocytoma?

PURPOSE

Both phenotypic and genotypic variations now underpin glioma classification, thus helping to more accurately guide their clinical management. However, WHO Grade III anaplastic astrocytoma (AA) remains an unpredictable, heterogeneous entity; displaying a variable prognosis, clinical course and treatment response. This study aims to examine whether additional tumour characteristics influence either overall survival (OS) or 3-year survival in AA.

MATERIALS AND METHODS

Data were collected on all newly diagnosed cases of AA between 2003 and 2014, followed up for a minimum of 3 years. Molecular information was obtained from case records and if missing, was re-analysed. Histological slides were independently examined for Ki-67 proliferation index, cellularity and number of mitotic figures. Kaplan-Meier and Cox regression analyses were used to assess OS.

RESULTS

In total, 50 cases were included with a median OS of 14.5 months (range: 1-150 months). Cumulative 3-year survival was 31.5%. Median age was 50 years (range: 24 - 77). Age, IDH1 mutation status, lobar location, oncological therapy and surgical resection were significant independent prognostic indicators for OS. In cases demonstrating an OS ≥ 3 years (n = 15), Ki-67 index, number of mitotic figures and percentage areas of 'high cellularity' were significantly reduced, i.e. more characteristic of lower-grade/WHO Grade II glioma.

CONCLUSIONS

IDH1 status, age, treatment and location remain the most significant prognostic indicators for patients with AA. However, Ki-67 index, mitotic figures and cellularity may help identify AA cases more likely to survive < 3 years, i.e. AA cases more similar to glioblastoma and those cases more likely to survive > 3 years, i.e. more similar to a low-grade glioma.

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.

Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases

BACKGROUND AND PURPOSE

MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading.

MATERIALS AND METHODS

One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks.

RESULTS

The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P < .01) when using automated color-coding, and the median Likert score for diagnostic certainty improved from 2 (interquartile range, 2-3) to 4 (interquartile range, 3-5) (P < .05) compared with the conventional reading. Interrater agreement was excellent (κ = 0.80; 95% CI, 0.71-0.89) with automated color-coding compared with a moderate agreement (κ = 0.46; 95% CI, 0.34-0.58) with the conventional reading approach. When considering the time required for image preprocessing, the overall average time for reading an examination was longer in the automated color-coding approach (91.5 [SD, 23.1] seconds versus 79.4 [SD, 34.7 ] seconds, P < .001).

CONCLUSIONS

Compared with the conventional reading, automated color-coding of lesion changes in follow-up examinations of patients with brain metastases significantly increased the rate of correct diagnoses and resulted in higher diagnostic certainty.

Glioblastoma surgery related emotion recognition deficits are associated with right cerebral hemisphere tract changes

Patients with glioblastoma face abysmal overall survival, cognitive deficits, poor quality of life and limitations to social participation; partly attributable to surgery. Emotion recognition deficits mediated by pathophysiological mechanisms in the right inferior fronto-occipital fasciculus and right inferior longitudinal fasciculus have been demonstrated in traumatic brain injury and dementia, with negative associations for social participation. We hypothesize similar mechanisms occur in patients undergoing resection surgery for glioblastoma. Here, we apply tract-based spatial statistics using a combination of automated image registration methods alongside cognitive testing before and after surgery. In this prospective, longitudinal, observational study of 15 patients, surgery is associated with an increase in emotion recognition deficits (P = 0.009) and this is correlated with decreases in fractional anisotropy in the inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, anterior thalamic radiation and uncinate fasciculus; all in the right hemisphere (P = 0.014). Methodologically, the combination of registration steps used demonstrate that tract-based spatial statistics can be applied in the context of large, scan distorting lesions such as glioblastoma. These results can inform clinical consultations with patients undergoing surgery, support consideration for social cognition rehabilitation and are consistent with theoretical mechanisms that implicate these tracts in emotion recognition deficits across different diseases.

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.

Fusion imaging to evaluate the radiographic anatomical relationship between primary tumors and local recurrences in retroperitoneal soft tissue sarcoma

BACKGROUND

Local recurrence (LR) of retroperitoneal soft tissue sarcoma (RPS) is a common and life-threatening event. The evaluation of the exact anatomical patterns of local recurrence might help to improve local treatment in RPS.

METHODS

Of our local database we extracted ten patients with LR of RPS with axial MRI and/or CT datasets of the primary tumor (PT) and the LR. Using the Osirix DICOM viewer Version v.3.9.4 64-bit (Pixmeo, Geneva, Switzerland) we performed a three-step fusion algorithm consisting of: a) 3-point co-registration of the axial datasets depicting the PT and the LR using three abdominal landmarks b) re-orientation of the datasets and c) image fusion. We evaluated the feasibility of this technique with regard to categorizing the localization of LR as within or distant from the PT.

RESULTS

Fusion imaging was feasible in seven out of ten patients. In the other three patients anatomical shifting of organs after surgery led to a relevant mismatch of anatomical landmarks and impeded interpretation of the fused images. In five of seven patients with successful fusion imaging, local recurrences were located within the anatomical borders of the primary tumor, in two out of seven patients local recurrences were distant to the primary.

CONCLUSIONS

Fusion imaging of primary tumors and local recurrences is feasible in most patients with RPS. Most local recurrences occurred within the anatomical localization of the primary tumor. For further investigations validation of the technique in larger patient cohorts is required.

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11-22.