Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme

Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Prognostic value of MRI volumetric parameters in non-small cell lung cancer patients after immune checkpoint inhibitor therapy: comparison with response assessment criteria

BACKGROUND

Accurate response parameters are important for patients with brain metastasis (BM) undergoing clinical trials using immunotherapy, considering poorly defined enhancement and variable responses. This study investigated MRI-based surrogate endpoints for patients with BM receiving immunotherapy.

METHODS

Sixty-three non-small cell lung cancer patients with BM who received immune checkpoint inhibitors and underwent MRI were included. Tumor diameters were measured using a modification of the RECIST 1.1 (mRECIST), RANO-BM, and iRANO adjusted for BM (iRANO-BM). Tumor volumes were segmented on 3D contrast-enhanced T1-weighted imaging. Differences between the sum of the longest diameter (SLD) or total tumor volume at baseline and the corresponding measurement at time of the best overall response were calculated as "changes in SLDs" (for each set of criteria) and "change in volumetry," respectively. Overall response rate (ORR), progressive disease (PD) assignment, and progression-free survival (PFS) were compared among the criteria. The prediction of overall survival (OS) was compared between diameter-based and volumetric change using Cox proportional hazards regression analysis.

RESULTS

The mRECIST showed higher ORR (30.1% vs. both 17.5%) and PD assignment (34.9% vs. 25.4% [RANO-BM] and 19% [iRANO-BM]). The iRANO-BM had a longer median PFS (13.7 months) than RANO-BM (9.53 months) and mRECIST (7.73 months, P = 0.003). The change in volumetry was a significant predictor of OS (HR = 5.87, 95% CI: 1.46-23.64, P = 0.013). None of the changes in SLDs, as determined by RANO-BM or iRANO-BM, were significant predictors of OS, except for the mRECIST, which exhibited a weak association with OS.

CONCLUSION

Quantitative volume measurement may be an accurate surrogate endpoint for OS in patients with BM undergoing immunotherapy, especially considering the challenges of multiplicity and the heterogeneity of sub-centimeter size responses.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm³ (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Feasibility of deep learning-based tumor segmentation for target delineation and response assessment in grade-4 glioma using multi-parametric MRI

Background

Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment.

Methods

We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting.

Results

For CE, median Dice scores were 0.81 (95% CI 0.71-0.83) and 0.82 (95% CI 0.74-0.84) for operator-1 and operator-2, respectively. For NE, median Dice scores were 0.65 (95% CI 0.56-0,69) and 0.63 (95% CI 0.57-0.67), respectively. Comparing volume sizes, we found excellent intra-class correlation coefficients of 0.90 (P < .001) and 0.95 (P < .001), for CE, respectively, and 0.97 (P < .001) and 0.90 (P < .001), for NE, respectively. Moreover, there was a strong correlation between response assessment in Neuro-Oncology volumes and HD-GLIO-volumes (P < .001, Spearman's R² = 0.83). Longitudinal growth relations between CE- and NE-volumes distinguished patients by clinical response: Pearson correlations of CE- and NE-volumes were 0.55 (P = .04) for responders, 0.91 (P > .01) for non-responders, and 0.80 (P = .05) for intermediate/mixed responders.

Conclusions

HD-GLIO was feasible for RT target delineation and MRI tumor volume assessment. CE/NE tumor-compartment growth correlation showed potential to predict clinical response to treatment.

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics

Background

Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable.

Methods

A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed.

Results

IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed.

Conclusion

NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others.

Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation

In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.

Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks

Background

Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.

Methods

A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).

Results

The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset.

Conclusion

The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.

Quantitative volumetric assessment of baseline enhancing tumor volume as an imaging biomarker predicts overall survival in patients with glioblastoma

BACKGROUND

Glioblastoma multiforme (GBM) is the commonest malignant primary brain tumor and still has one of the worst prognoses among cancers in general. There is a need for non-invasive methods to predict individual prognosis in patients with GBM.

PURPOSE

To evaluate quantitative volumetric tissue assessment of enhancing tumor volume on cranial magnetic resonance imaging (MRI) as an imaging biomarker for predicting overall survival (OS) in patients with GBM.

MATERIAL AND METHODS

MRI scans of 49 patients with histopathologically confirmed GBM were analyzed retrospectively. Baseline contrast-enhanced (CE) MRI sequences were transferred to a segmentation-based three-dimensional quantification tool, and the enhancing tumor component was analyzed. Based on a cut-off percentage of the enhancing tumor volume (PoETV) of >84.78%, samples were dichotomized, and the OS and intracranial progression-free survival (PFS) were evaluated. Univariable and multivariable analyses, including variables such as sex, Karnofsky Performance Status score, O⁶-methylguanine-DNA-methyltransferase status, age, and resection status, were performed using the Cox regression model.

RESULTS

The median OS and PFS were 16.9 and 7 months in the entire cohort, respectively. Patients with a CE tumor volume of >84.78% showed a significantly shortened OS (12.9 months) compared to those with a CE tumor volume of ≤84.78% (17.7 months) (hazard ratio [HR] 2.72; 95% confidence interval [CI] 1.22-6.03; P = 0.01). Multivariable analysis confirmed that PoETV had a significant prognostic role (HR 2.47; 95% CI 1.08-5.65; P = 0.03).

CONCLUSION

We observed a correlation between PoETV and OS. This imaging biomarker may help predict the OS of patients with GBM.

Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging

Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.

Conventional and advanced imaging throughout the cycle of care of gliomas

Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.

Validation of an effective implantable pump-infusion system for chronic convection-enhanced delivery of intracerebral topotecan in a large animal model

OBJECTIVE

Intracerebral convection-enhanced delivery (CED) has been limited to short durations due to a reliance on externalized catheters. Preclinical studies investigating topotecan (TPT) CED for glioma have suggested that prolonged infusion improves survival. Internalized pump-catheter systems may facilitate chronic infusion. The authors describe the safety and utility of long-term TPT CED in a porcine model and correlation of drug distribution through coinfusion of gadolinium.

METHODS

Fully internalized CED pump-catheter systems were implanted in 12 pigs. Infusion algorithms featuring variable infusion schedules, flow rates, and concentrations of a mixture of TPT and gadolinium were characterized over increasing intervals from 4 to 32 days. Therapy distribution was measured using gadolinium signal on MRI as a surrogate. A 9-point neurobehavioral scale (NBS) was used to identify side effects.

RESULTS

All animals tolerated infusion without serious adverse events. The average NBS score was 8.99. The average maximum volume of distribution (Vdmax) in chronically infused animals was 11.30 mL and represented 32.73% of the ipsilateral cerebral hemispheric volume. Vdmax was achieved early during infusions and remained relatively stable despite a slight decline as the infusion reached steady state. Novel tissue TPT concentrations measured by liquid chromatography mass spectroscopy correlated with gadolinium signal intensity on MRI (p = 0.0078).

CONCLUSIONS

Prolonged TPT-gadolinium CED via an internalized system is safe and well tolerated and can achieve a large Vdmax, as well as maintain a stable Vd for up to 32 days. Gadolinium provides an identifiable surrogate for measuring drug distribution. Extended CED is potentially a broadly applicable and safe therapeutic option in select patients.

Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports

BACKGROUND

Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation.

METHODS

In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor.

RESULTS

Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66-68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products.

CONCLUSIONS

Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.

Accuracy of the neurosurgeons estimation of extent of resection in glioblastoma

BACKGROUND

The surgeons' estimate of the extent of resection (EOR) shows little accuracy in previous literature. Considering the developments in surgical techniques of glioblastoma (GBM) treatment, we hypothesize an improvement in this estimation. This study aims to compare the EOR estimated by the neurosurgeon with the EOR determined using volumetric analysis on the post-operative MR scan.

METHODS

Pre- and post-operative tumor volumes were calculated through semi-automatic volumetric assessment by three observers. Interobserver agreement was measured using intraclass correlation coefficient (ICC). A univariate general linear model was used to study the factors influencing the accuracy of estimation of resection percentage.

RESULTS

ICC was high for all three measurements: pre-operative tumor volume was 0.980 (0.969-0.987), post-operative tumor volume 0.974 (0.961-0.984), and EOR 0.947 (0.917-0.967). Estimation of EOR by the surgeon showed moderate accuracy and agreement. Multivariable analysis showed a statistically significant effect of operating neurosurgeon (p = 0.01), use of fluorescence (p < 0.001), and resection percentage (p < 0.001) on the accuracy of the EOR estimation.

CONCLUSION

All measurements through semi-automatic volumetric analysis show a high interobserver agreement, suggesting this to be a reliable assessment of EOR. We found a moderate reliability of the surgeons' estimate of EOR. Therefore, (early) post-operative MRI scanning for evaluation of EOR remains paramount.

Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study

BACKGROUND

The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden.

METHODS

In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset).

FINDINGS

For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan).

INTERPRETATION

Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases.

FUNDING

Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

Monitoring optic chiasmatic-hypothalamic glioma volumetric changes by MRI in children under clinical surveillance or chemotherapy

PURPOSE

Optic pathway gliomas represent 5% of pediatric brain tumors and are typically low-grade lesions. Because of their unpredictable clinical course, adequate treatment approaches have been controversial, involving surveillance, surgery, chemotherapy, and radiotherapy. In this study, we use volumetric imaging to compare evolution of optic chiasmatic-hypothalamic gliomas (OCHG) treated with and without chemotherapy, analyzing tumor volume variation during the overall period.

METHODS

A total of 45 brain MRI were retrospectively analyzed for 14 patients with OCHG. Volumetric assessment of the lesions was performed by a neuroradiologist, using software DISPLAY. OCHG patients were allocated into two groups: group 1 (n = 8) who underwent chemotherapy and group 2 (n = 6) who did not receive chemotherapy. Outcome analysis was performed comparing tumor volume evolution of these two groups.

RESULTS

The results showed a reduction of 4.4% of the volume of the lesions for group 1 after the end of chemotherapy, with an increase of 5.3% in volume in the late follow-up examination. For group 2, we found a slight reduction (5%) of the overall volume of the lesions, both with no statistical significance (p > 0.05).

CONCLUSIONS

From the limited series analyzed in this study, no significant differences were observed in relation to the volume change of lesions treated or not treated with chemotherapy. Larger prospective clinical trials are needed to better evaluate the effect of chemotherapy and radiological response of OCHG.

Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab-a report from the BELOB trial

Background

The current method for assessing progressive disease (PD) in glioblastoma is according to the Response Assessment in Neuro-Oncology (RANO) criteria. Bevacizumab-treated patients may show pseudo-response on postcontrast T1-weighted (T1w) MRI, and a more infiltrative non-enhancing growth pattern on T2w/fluid attenuated inversion recovery (FLAIR) images. We investigated whether the RANO criteria remain the method of choice for assessing bevacizumab-treated recurrent glioblastoma when compared with various volumetric methods.

Methods

Patients with assessable MRI data from the BELOB trial (n = 148) were included. Patients were treated with bevacizumab, lomustine, or both. At first and second radiological follow-up (6 and 12 wk), PD was determined using the 2D RANO criteria and various volumetric methods based on enhancing tumor only and enhancing plus non-enhancing tumor. Differences in overall survival (OS) between PD and non-PD patients were assessed with the log-rank test and a Cox model. Hazard ratios (HRs) and their 95% CIs were determined.

Results

For all patients together, all methods (except subtraction of non-enhancing from enhancing volume at first follow-up) showed significant differences in OS between PD and non-PD patients (P < .001). The largest risk increase for death in case of PD at both first and second follow-up was found with the RANO criteria: HR = 2.81 (95% CI, 1.92-4.10) and HR = 2.80 (95% CI, 1.75-4.49), respectively. In the bevacizumab-treated patients, all methods assessed showed significant differences in OS between PD and non-PD patients. There were no significant differences between methods.

Conclusions

In the first 12 weeks, volumetric methods did not provide significant improvement over the RANO criteria as a posttreatment prognostic marker.

Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery

PURPOSE

Glioblastoma (GBM) neurosurgical resection relies on contrast-enhanced MRI-based neuronavigation. However, it is well-known that infiltrating tumor extends beyond contrast enhancement. Fluorescence-guided surgery (FGS) using 5-aminolevulinic acid (5-ALA) was evaluated to improve extent of resection (EOR) of GBMs. Preoperative morphological tumor metrics were also assessed.

PROCEDURES

Thirty patients from a phase II trial evaluating 5-ALA FGS in newly diagnosed GBM were assessed. Tumors were segmented preoperatively to assess morphological features as well as postoperatively to evaluate EOR and residual tumor volume (RTV).

RESULTS

Median EOR and RTV were 94.3 % and 0.821 cm(3), respectively. Preoperative surface area to volume ratio and RTV were significantly associated with overall survival, even when controlling for the known survival confounders.

CONCLUSIONS

This study supports claims that 5-ALA FGS is helpful at decreasing tumor burden and prolonging survival in GBM. Moreover, morphological indices are shown to impact both resection and patient survival.

Monitoring Radiation Treatment Effects in Glioblastoma: FLAIR Volume as Significant Predictor of Survival

Glioblastoma is the most common adult central nervous system malignancy and carries a poor prognosis. Disease progression and recurrence after chemoradiotherapy are assessed via serial magnetic resonance imaging sequences. T2-weighted fluid-attenuated inversion recovery (FLAIR) signal is presumed to represent edema containing microscopic cancer infiltration. Here we assessed the prognostic impact of computerized volumetry of FLAIR signal in the peri-treatment setting for glioblastoma. We analyzed pre- and posttreatment FLAIR sequences of 40 patients treated at the Columbia University Medical Center between 2011 and 2014, excluding those without high-quality FLAIR imaging within 2 weeks before treatment and 60 to 180 days afterward. We manually contoured regions of FLAIR hyperintensity as per Radiation Therapy Oncology Group guidelines and calculated the volumes of nonenhancing tumor burden. At the time of this study, all but 1 patient had died. Pre- and posttreatment FLAIR volumes were assessed for correlation to overall and progression-free survival. Larger post-treatment FLAIR volumes from sequences taken between 60 and 180 days after conclusion of chemoradiotherapy were negatively correlated with overall survival (P = .048 on Pearson's correlation and P = .017 and P = .043 on univariable and multivariable Cox regression analyses, respectively) and progression-free survival (P = .002 on Pearson's correlation and P = < .001 and P = < .001 on univariable and multivariable Cox regression analyses). This study suggests that higher FLAIR volumes in the 2- to 6-month posttreatment window are associated with worsened survival.

BRAIM: A computer-aided diagnosis system for neurodegenerative diseases and brain lesion monitoring from volumetric analyses

BACKGROUND AND OBJECTIVE

This paper presents BRAIM, a computer-aided diagnosis (CAD) system to help clinicians in diagnosing and treatment monitoring of brain diseases from magnetic resonance image processing. BRAIM can be used for early diagnosis of neurodegenerative diseases such as Parkinson, Alzheimer or Multiple Sclerosis and also for brain lesion diagnosis and monitoring.

METHODS

The developed CAD system includes different user-friendly tools for segmenting and determining whole brain and brain structure volumes in an easy and accurate way. Specifically, three types of measurements can be performed: (1) total volume of white, gray matter and cerebrospinal fluid; (2) brain structure volumes (volume of putamen, thalamus, hippocampus and caudate nucleus); and (3) brain lesion volumes.

RESULTS

As a proof of concept, some study cases were analyzed with the presented system achieving promising results. In addition to be used to quantify treatment effectiveness in patients with brain lesions, it was demonstrated that BRAIM is able to classify a subject according to the brain volume measurements using as reference a healthy control database created for this purpose.

CONCLUSIONS

The CAD system presented in this paper simplifies the daily work of clinicians and provides them with objective and quantitative volume data for prospective and retrospective analyses.

Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis

PURPOSE

To investigate whether the degree of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) is associated with the amount of breast metabolic activity measured by breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography / computed tomography (PET/CT).

MATERIALS AND METHODS

An Institutional Review Board (IRB)-approved retrospective study was performed. Of 327 patients who underwent preoperative breast MRI from 1/1/12 to 12/31/15, 73 patients had 18F-FDG PET/CT evaluation performed within 1 week of breast MRI and no suspicious findings in the contralateral breast. MRI was performed on a 1.5T or 3.0T system. The imaging sequence included a triplane localizing sequence followed by sagittal fat-suppressed T₂ -weighted sequence, and a bilateral sagittal T₁ -weighted fat-suppressed fast spoiled gradient-echo sequence, which was performed before and three times after a rapid bolus injection (gadobenate dimeglumine, Multihance; Bracco Imaging; 0.1 mmol/kg) delivered through an IV catheter. The unaffected contralateral breast in these 73 patients underwent BPE and BPU assessments. For PET/CT BPU calculation, a 3D region of interest (ROI) was drawn around the glandular breast tissue and the maximum standardized uptake value (SUV_max ) was determined. Qualitative MRI BPE assessments were performed on a 4-point scale, in accordance with BI-RADS categories. Additional 3D quantitative MRI BPE analysis was performed using a previously published in-house technique. Spearman's correlation test and linear regression analysis was performed (SPSS, v. 24).

RESULT

The median time interval between breast MRI and 18F-FDG PET/CT evaluation was 3 days (range, 0-6 days). BPU SUV_max mean value was 1.6 (SD, 0.53). Minimum and maximum BPU SUV_max values were 0.71 and 4.0. The BPU SUV_max values significantly correlated with both the qualitative and quantitative measurements of BPE, respectively (r(71) = 0.59, P < 0.001 and r(71) = 0.54, P < 0.001). Qualitatively assessed high BPE group (BI-RADS 3/4) had significantly higher BPU SUV_max of 1.9 (SD = 0.44) compared to low BPE group (BI-RADS 1/2) with an average BPU SUV_max of 1.17 (SD = 0.32) (P < 0.001). On linear regression analysis, BPU SUV_max significantly predicted qualitative and quantitative measurements of BPE (β = 1.29, t(71) = 3.88, P < 0.001 and β = 19.52, t(71) = 3.88, P < 0.001).

CONCLUSION

There is a significant association between breast BPU and BPE, measured both qualitatively and quantitatively. Increased breast cancer risk in patients with high MRI BPE could be due to elevated basal metabolic activity of the normal breast tissue, which may provide a susceptible environment for tumor growth.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:753-759.

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice.

Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry

Purpose

Unambiguous evaluation of glioblastoma (GB) progression is crucial, both for clinical trials as well as day by day routine management of GB patients. 3D-volumetry in the follow-up of GB provides quantitative data on tumor extent and growth, and therefore has the potential to facilitate objective disease assessment. The present study investigated the utility of absolute changes in volume (delta) or regional, segmentation-based subtractions for detecting disease progression in longitudinal MRI follow-ups.

Methods

165 high resolution 3-Tesla MRIs of 30 GB patients (23m, mean age 60.2y) were retrospectively included in this single center study. Contrast enhancement (CV) and tumor-related signal alterations in FLAIR images (FV) were semi-automatically segmented. Delta volume (dCV, dFV) and regional subtractions (sCV, sFV) were calculated. Disease progression was classified for every follow-up according to histopathologic results, decisions of the local multidisciplinary CNS tumor board and a consensus rating of the neuro-radiologic report.

Results

A generalized logistic mixed model for disease progression (yes / no) with dCV, dFV, sCV and sFV as input variables revealed that only dCV was significantly associated with prediction of disease progression (P = .005). Delta volume had a better accuracy than regional, segmentation-based subtractions (79% versus 72%) and a higher area under the curve by trend in ROC curves (.83 versus .75).

Conclusion

Absolute volume changes of the contrast enhancing tumor part were the most accurate volumetric determinant to detect progressive disease in assessment of GB and outweighed FLAIR changes as well as regional, segmentation-based image subtractions. This parameter might be useful in upcoming objective response criteria for glioblastoma.

Different Volumetric Measurement Methods for Pituitary Adenomas and Their Crucial Clinical Significance

Confirming the status of residual tumors is crucial. In stationary or spontaneous regression cases, early treatments are inappropriate. The long-used geometric calculation formula is 1/2 (length × width × height). However, it yields only rough estimates and is particularly unreliable for irregularly shaped masses. In our study, we attempted to propose a more accurate method. Between 2004 and 2014, 94 patients with pituitary tumors were enrolled in this retrospective study. All patients underwent transsphenoidal surgery and received magnetic resonance imaging (MRI). The pre- and postoperative volumes calculated using the traditional formula were termed A1 and A2, and those calculated using the proposed method were termed O1 and O2, respectively. Wilcoxon signed rank test revealed no significant difference between the A1 and O1 groups (P = 0.1810) but a significant difference between the A2 and O2 groups (P < 0.0001). Significant differences were present in the extent of resection (P < 0.0001), high-grade cavernous sinus invasion (P = 0.0312), and irregular shape (P = 0.0116). Volume is crucial in evaluating tumor status and determining treatment. Therefore, a more scientific method is especially useful when lesions are irregularly shaped or when treatment is determined exclusively based on the tumor volume.

Automatic estimation of extent of resection and residual tumor volume of patients with glioblastoma

OBJECTIVE In the treatment of glioblastoma, residual tumor burden is the only prognostic factor that can be actively influenced by therapy. Therefore, an accurate, reproducible, and objective measurement of residual tumor burden is necessary. This study aimed to evaluate the use of a fully automatic segmentation method-brain tumor image analysis (BraTumIA)-for estimating the extent of resection (EOR) and residual tumor volume (RTV) of contrast-enhancing tumor after surgery. METHODS The imaging data of 19 patients who underwent primary resection of histologically confirmed supratentorial glioblastoma were retrospectively reviewed. Contrast-enhancing tumors apparent on structural preoperative and immediate postoperative MR imaging in this patient cohort were segmented by 4 different raters and the automatic segmentation BraTumIA software. The manual and automatic results were quantitatively compared. RESULTS First, the interrater variabilities in the estimates of EOR and RTV were assessed for all human raters. Interrater agreement in terms of the coefficient of concordance (W) was higher for RTV (W = 0.812; p < 0.001) than for EOR (W = 0.775; p < 0.001). Second, the volumetric estimates of BraTumIA for all 19 patients were compared with the estimates of the human raters, which showed that for both EOR (W = 0.713; p < 0.001) and RTV (W = 0.693; p < 0.001) the estimates of BraTumIA were generally located close to or between the estimates of the human raters. No statistically significant differences were detected between the manual and automatic estimates. BraTumIA showed a tendency to overestimate contrast-enhancing tumors, leading to moderate agreement with expert raters with respect to the literature-based, survival-relevant threshold values for EOR. CONCLUSIONS BraTumIA can generate volumetric estimates of EOR and RTV, in a fully automatic fashion, which are comparable to the estimates of human experts. However, automated analysis showed a tendency to overestimate the volume of a contrast-enhancing tumor, whereas manual analysis is prone to subjectivity, thereby causing considerable interrater variability.

Aggressive resection at the infiltrative margins of glioblastoma facilitated by intraoperative fluorescein guidance

OBJECTIVE Extent of resection is an important prognostic factor in patients undergoing surgery for glioblastoma (GBM). Recent evidence suggests that intravenously administered fluorescein sodium associates with tumor tissue, facilitating safe maximal resection of GBM. In this study, the authors evaluate the safety and utility of intraoperative fluorescein guidance for the prediction of histopathological alteration both in the contrast-enhancing (CE) regions, where this relationship has been established, and into the non-CE (NCE), diffusely infiltrated margins. METHODS Thirty-two patients received fluorescein sodium (3 mg/kg) intravenously prior to resection. Fluorescence was intraoperatively visualized using a Zeiss Pentero surgical microscope equipped with a YELLOW 560 filter. Stereotactically localized biopsy specimens were acquired from CE and NCE regions based on preoperative MRI in conjunction with neuronavigation. The fluorescence intensity of these specimens was subjectively classified in real time with subsequent quantitative image analysis, histopathological evaluation of localized biopsy specimens, and radiological volumetric assessment of the extent of resection. RESULTS Bright fluorescence was observed in all GBMs and localized to the CE regions and portions of the NCE margins of the tumors, thus serving as a visual guide during resection. Gross-total resection (GTR) was achieved in 84% of the patients with an average resected volume of 95%, and this rate was higher among patients for whom GTR was the surgical goal (GTR achieved in 93.1% of patients, average resected volume of 99.7%). Intraoperative fluorescein staining correlated with histopathological alteration in both CE and NCE regions, with positive predictive values by subjective fluorescence evaluation greater than 96% in NCE regions. CONCLUSIONS Intraoperative administration of fluorescein provides an easily visualized marker for glioma pathology in both CE and NCE regions of GBM. These findings support the use of fluorescein as a microsurgical adjunct for guiding GBM resection to facilitate safe maximal removal.

Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma

BACKGROUND

Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis.

METHODS

Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression.

RESULTS

We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features.

CONCLUSION

Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study

BACKGROUND

The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value.

METHODS

An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT.

RESULTS

There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%).

CONCLUSIONS

Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized measurements with limited intra or inter-observer variability.

Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

BACKGROUND

Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab.

METHODS

The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model.

RESULTS

Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively.

CONCLUSION

With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

BACKGROUND AND PURPOSE

Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method.

METHODS

Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T₂ and T₁ ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively.

RESULTS

The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy.

CONCLUSIONS

The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

Segmentation of peritumoral oedema offers a valuable radiological feature of cerebral metastasis

OBJECTIVE

Peritumoral oedema (PTO) is commonly observed on MRI in malignant brain tumours including brain metastasis (bMET) and glioblastoma multiforme (GBM). This study aimed to differentiate bMET from GBM by comparing the volume ratio of PTO to tumour lesion (Rvol).

METHODS

56 patients with solitary bMET or GBM were enrolled, and MRI was analyzed by a semi-automatic methodology based on MATLAB (Mathworks, Natick, MA). The PTO volume (Voedema) was segmented for quantification using T2 fluid-attenuated inversion-recovery images, while the tumour volume was quantified with enhanced T1 images. The quantitative volume of the tumour, PTO and the ratio of PTO to tumour were interpreted using SPSS(®) (IBM Corp., New York, NY; formerly SPSS Inc., Chicago, IL) by considering different locations and pathologies.

RESULTS

The tumour volumes of supratentorial GBM, supratentorial bMET (supra-bMET) and infratentorial bMET were 32.22 ± 21.9, 18.45 ± 17.28 and 11.40 ± 5.63 ml, respectively. The corresponding Voedema were 44.08 ± 25.84, 73.20 ± 40.35 and 23.74 ± 7.78 ml, respectively. The Voedema difference between supratentorial and infratentorial lesions is significant (p-value = 0.002). Supra-bMET has a smaller tumour volume (p-value = 0.032), but a larger PTO (p-value = 0.007). The ratio of Voedema to the tumour volume in bMET is statistically higher than that in GBM (p-value = 0.015). The cut-off ratio for identifying bMET from GBM is 3.9, with a specificity and sensitivity of 90.0% and 68.8%, respectively.

CONCLUSION

Segmentation is an efficient method to quantify irregular PTO. bMET possesses more extensive oedema with smaller tumour volume than does GBM. The Rvol is a valuable index to distinguish bMET from GBM.

ADVANCES IN KNOWLEDGE

This study presents a new method for the quantitation of PTO to differentiate bMET from GBM.

Virtual Raters for Reproducible and Objective Assessments in Radiology

Volumetric measurements in radiologic images are important for monitoring tumor growth and treatment response. To make these more reproducible and objective we introduce the concept of virtual raters (VRs). A virtual rater is obtained by combining knowledge of machine-learning algorithms trained with past annotations of multiple human raters with the instantaneous rating of one human expert. Thus, he is virtually guided by several experts. To evaluate the approach we perform experiments with multi-channel magnetic resonance imaging (MRI) data sets. Next to gross tumor volume (GTV) we also investigate subcategories like edema, contrast-enhancing and non-enhancing tumor. The first data set consists of N = 71 longitudinal follow-up scans of 15 patients suffering from glioblastoma (GB). The second data set comprises N = 30 scans of low- and high-grade gliomas. For comparison we computed Pearson Correlation, Intra-class Correlation Coefficient (ICC) and Dice score. Virtual raters always lead to an improvement w.r.t. inter- and intra-rater agreement. Comparing the 2D Response Assessment in Neuro-Oncology (RANO) measurements to the volumetric measurements of the virtual raters results in one-third of the cases in a deviating rating. Hence, we believe that our approach will have an impact on the evaluation of clinical studies as well as on routine imaging diagnostics.

Small increases in enhancement on MRI may predict survival post radiotherapy in patients with glioblastoma

To assess impact of volumetric changes in tumour volume post chemoradiotherapy in glioblastoma. Patients managed with chemoradiotherapy between 2008 and 2011 were included. Patients with incomplete MRI sets were excluded. Analyses were performed on post-operative MRI, and MRIs at 1 month (M+1), 3 months (M+3), 5 months (M+5), 7 months (M+7), and 12 months (M+12) post completion of RT. RANO definitions of response were used for all techniques. Modified RANO criteria and two volumetric analysis techniques were used. The two volumetric analysis techniques involved utility of the Eclipse treatment planning software to calculate the volume of delineated tissue: surgical cavity plus all surrounding enhancement (Volumetric) versus surrounding enhancement only (Rim). Retrospective analysis of 49 patients with median survival of 18.4 months. Using Volumetric analysis the difference in MS for patients who had a <5 % increase versus ≥5 % at M+3 was 23.1 versus 15.1 months (p = 0.006), and M+5 was 26.3 versus 15.1 months (p = 0.006). For patients who were classified as progressive disease using modified RANO criteria at M+1 and M+3 there was a difference in MS compared with those who were not (M+1: 13.1 vs. 19.4 months, p = 0.017, M+3: 13.2 vs. 20.1 months, p = 0.096). An increase in the volume of cavity and enhancement of ≥5 % at M+3 and M+5 post RT was associated with reduced survival, suggesting that increases in radiological abnormality of <25 % may predict survival.

Three-Dimensional Quantitative Validation of Breast Magnetic Resonance Imaging Background Parenchymal Enhancement Assessments

The magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and its clinical significance as a biomarker of breast cancer risk has been proposed based on qualitative studies. Previous BPE quantification studies lack appropriate correlation with BPE qualitative assessments. The purpose of this study is to validate our three-dimensional BPE quantification method with standardized BPE qualitative cases. An Institutional Review Board-approved study reviewed 500 consecutive magnetic resonance imaging cases (from January 2013-December 2014) using a strict inclusion criteria and 120 cases that best represented each of the BPE qualitative categories (minimal or mild or moderate or marked) were selected. Blinded to the qualitative data, fibroglandular tissue contours of precontrast and postcontrast images were delineated using an in-house, proprietary segmentation algorithm. Metrics of BPE were calculated including %BPE ([ratio of BPE volume to fibroglandular tissue volume] × 100) at multiple threshold levels to determine the optimal cutoff point for BPE quantification that best correlated with the reference BPE qualitative cases. The highest positive correlation was present at ×1.5 precontrast average signal intensity threshold level (r = 0.84, P < 0.001). At this level, the BPE qualitative assessment of minimal, mild, moderate, and marked correlated with the mean quantitative %BPE of 14.1% (95% CI: 10.9-17.2), 26.1% (95% CI: 22.8-29.3), 45.9% (95% CI: 40.2-51.7), and 74.0% (95% CI: 68.6-79.5), respectively. A one-way analysis of variance with post-hoc analysis showed that at ×1.5 precontrast average signal intensity level, the quantitative %BPE measurements best differentiated the four reference BPE qualitative groups (F [3,117] = 106.8, P < 0.001). Our three-dimensional BPE quantification methodology was validated using the reference BPE qualitative cases and could become an invaluable clinical tool to more accurately assess breast cancer risk and to test chemoprevention strategies.

Brain injury visible on early MRI after subarachnoid hemorrhage might predict neurological impairment and functional outcome

BACKGROUND

In subarachnoid hemorrhage (SAH), brain injury visible within 48 h of onset may impact on admission neurological disability and 3-month functional outcome. With volumetric MRI, we measured the volume of brain injury visible after SAH, and assessed the association with admission clinical grade and 3-month functional outcome.

METHODS

Retrospective cohort study conducted in the Neurocritical Care Division, Columbia University Medical Center, New York, USA. On brain MRI acquired within 48 h of SAH-onset and before aneurysm-securing (n = 27), two blinded readers measured DWI and FLAIR-lesion volumes using semi-automated, computer segmentation software.

RESULTS

Compared to post-resuscitation Hunt-Hess grade 1-3 (70 %), high-grade patients (30 %) had higher lesion volumes on DWI (34 ml [IQR: 0-64] vs. 2 ml [IQR: 0.5-7], P = 0.02) and on FLAIR (81 ml [IQR: 24-127] vs. 3 ml [IQR: 0-27], P = 0.02). On DWI, each 10 ml increase in lesion volume was associated with a 101 %-increase in the odds of presenting with 1 grade more in the Hunt-Hess scale (aOR 2.01, 95 % CI 1.10-3.68, P = 0.02), but was not significantly associated with 3-month outcome. On FLAIR, each 10 ml increase in lesion volume was associated with 34 % higher odds of a 1-point increase on the Hunt-Hess scale (aOR 1.34, 95 % CI 1.06-1.68, P = 0.01) and 139 % higher odds of a 1-point increase on the 3-month mRS (aOR 2.39, 95 % CI 1.13-5.07, P = 0.02).

CONCLUSION

The volume of brain injury visible on DWI and FLAIR within 48 h after SAH is proportional to neurological impairment on admission. Moreover, FLAIR-imaging implicates chronic brain injury-predating SAH-as potentially relevant cause of poor functional outcome.

Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth

BACKGROUND

Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed.

PURPOSE

To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined.

MATERIAL AND METHODS

Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics.

RESULTS

A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample.

CONCLUSION

Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs.