Neuroimaging plays an ever evolving role in the diagnosis, treatment planning, and post-therapy assessment of brain tumors. This review provides an overview of current magnetic resonance imaging (MRI) methods routinely employed in the care of the brain tumor patient. Specifically, we focus on advanced techniques including diffusion, perfusion, spectroscopy, tractography, and functional MRI as they pertain to noninvasive characterization of brain tumors and pretreatment evaluation. The utility of both structural and physiological MRI in the post-therapeutic brain evaluation is also reviewed with special attention to the challenges presented by pseudoprogression and pseudoresponse.

Cerebral microbleeds, which are small focal hemorrhages in the brain that are prevalent in many diseases, are gaining increasing attention due to their potential as surrogate markers of disease burden, clinical outcomes, and delayed effects of therapy. Manual detection is laborious and automatic detection and labeling of these lesions is challenging using traditional algorithms. Inspired by recent successes of deep convolutional neural networks in computer vision, we developed a 3D deep residual network that can distinguish true microbleeds from false positive mimics of a previously developed technique based on traditional algorithms. A dataset of 73 patients with radiation-induced cerebral microbleeds scanned at 7 T with susceptibility-weighted imaging was used to train and evaluate our model. With the resulting network, we maintained 95% of the true microbleeds in 12 test patients and the average number of false positives was reduced by 89%, achieving a detection precision of 71.9%, higher than existing published methods. The likelihood score predicted by the network was also evaluated by comparing to a neuroradiologist's rating, and good correlation was observed.

The ability to accurately differentiate treatment-related changes (ie, pseudoprogression and radiation necrosis) from recurrent glioma remains a critical diagnostic problem in neuro-oncology. Because these entities are treated differently and have vastly different outcomes, accurate diagnosis is necessary to provide optimal patient care. In current practice, this diagnostic quandary commonly requires either serial imaging or histopathologic tissue confirmation. In this article, experts in the field debate the utility of 2-deoxy-2[¹⁸F]fluoro-d-glucose positron emission tomography (FDG PET) as an imaging tool to distinguish tumor recurrence from treatment-related changes in a patient with glioblastoma and progressive contrast enhancement on magnetic resonance (MR) following chemoradiotherapy.

Purpose

To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma.

Materials and methods

In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade.

Results

Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87).

Conclusion

The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

Objectives

To characterize clinical and imaging features in patients with pathologically confirmed demyelinating lesions.

Methods

In this retrospective chart review, we analyzed clinical-radiological-pathological correlations in patients >15years old who underwent brain biopsy at our institution between 2000 and 2015 and had inflammatory demyelination on neuropathology.

Results

Of 31 patients, the mean age was 42years (range 16 to 69years) and 55% were female. All but one of the biopsied lesions were considered tumefactive demyelinating lesions (TDLs) by imaging criteria, measuring >2cm on contrast-enhanced brain MRI. On clinical follow-up, the final diagnosis was a CNS malignancy in 2 patients (6.5%). In patients without malignant tumor, the TDL was solitary in 12 (41%) and multifocal in 17 (59%), with contrast enhancement in all but one case, primarily in an incomplete rim enhancement pattern (75.9%). Of 16 patients with at least 12months of clinical follow-up, 7 (43.8%) had a clinical relapse. Of patients without a prior neurologic history, relapse occurred in 2/7 (29%) in solitary TDL and 2/6 (33%) in multifocal lesions at initial presentation. Recurrent TDLs occurred in 3 patients, all with initially solitary TDLs. Stratifying by CSF analysis, 4 of 6 patients (67%) with either an elevated IgG Index or >2 oligoclonal bands suffered a clinical relapse compared to 2/8 (25%) with non-inflammatory CSF.

Conclusions

Pathologically confirmed TDLs call for careful clinical correlation, clinical follow-up and imaging surveillance. Although sometimes clinically monophasic, tumefactive demyelinating lesions carried nearly a 45% risk of near-term clinical relapse in our study, even when presenting initially as a solitary mass lesion.

This paper was published inadvertently as open access. It has been corrected online.

Early tumor growth, or increased contrast-enhancing tumor not related to evolving post-surgical injury, in the interval between surgical resection and initiation of radiotherapy has implications for treatment planning and clinical outcomes. In this study we evaluated the incidence of early tumor growth, correlated tumor growth with survival outcome measures, and assessed predictors of early tumor growth in glioblastoma. We reviewed the records of patients with newly-diagnosed glioblastoma who underwent surgical resection and chemoradiotherapy at our institution. Patients with preoperative, immediate postoperative, and preradiotherapy MRI were included. Conventional MRI and DWI features were assessed. The correlation between early tumor growth and extent of resection with survival was assessed with Kaplan-Meier analysis. Logistic regression was carried out to evaluate predictors of early tumor growth. Of 140 included patients, sixty-seven cases (48%) had new or increased contrast enhancement attributed to early tumor growth. Median progression free survival (PFS) and overall survival (OS) were shorter in patients with early tumor growth compared to those without early tumor growth (p < 0.001 for both). Additionally, PFS and OS were longer in patients who underwent gross total resection of enhancing tumor (p = 0.016 and <0.001, respectively). Of the evaluated predictors of early growth, subtotal resection was most likely to result in early growth (p < 0.001). Imaging evidence of early tumor growth is often observed at preradiotherapy MRI and is associated with shorter survival. Gross total resection of contrast enhancing tumor decreases likelihood of early tumor growth.

Imaging plays a central role in neuro-oncology including primary diagnosis, treatment planning, and surveillance of tumors. The emergence of quantitative imaging and radiomics provided an uprecedented opportunity to compile mineable databases that can be utilized in a variety of applications. In this review, we aim to summarize the current state of conventional and advanced imaging techniques, standardization efforts, fast protocols, contrast and sedation in pediatric neuro-oncologic imaging, radiomics-radiogenomics, multi-omics and molecular imaging approaches. We will also address the existing challenges and discuss future directions.

Objective

Tumefactive demyelinating lesions (TDLs) remain one of the most common brain lesions to mimic a brain tumor, particularly primary CNS lymphoma (PCNSL) and high-grade gliomas. The purpose of our study was to evaluate the ability of apparent diffusion coefficient (ADC) values and conventional MRI features to differentiate TDLs from PCNSLs and high-grade gliomas.

Materials and methods

Seventy-five patients (24 patients with TDLs, 28 with PCNSLs, and 23 with high-grade gliomas) with 168 brain lesions (70 TDLs, 68 PCNSLs, and 30 high-grade gliomas) who underwent DWI before surgery or therapy were included in the study. Minimum ADC (ADC(min)) and average ADC (ADC(avg)) values were calculated for each lesion. ANOVA and ROC analyses were performed. ROC analyses were also performed for the presence of incomplete rim enhancement and for the number of lesions. Multiple-variable logistic regression with ROC analysis was then performed to evaluate performance in multiple-variable models.

Results

ADC(min) was statistically significantly higher (p < 0.01) in TDLs (mean, 0.886; 95% CI, 0.802-0.931) than in PCNSLs (0.547; 95% CI, 0.496-0.598) and high-grade gliomas (0.470; 95% CI, 0.385-0.555). (All ADC values in this article are reported in units of × 10(-3) mm(2)/s.) ADC(avg) was statistically significantly higher (p < 0.01) in TDLs (mean, 1.362; 95% CI, 1.268-1.456) than in PCNSLs (0.990; 95% CI, 0.919-1.061) but not in high-grade gliomas (1.216; 95% CI, 1.074-1.356). Multiple-variable models showed statistically significant individual effects and superior diagnostic performance on ROC analysis.

Conclusion

TDLs can be diagnosed on preoperative MRI with a high degree of specificity; MRI features of incomplete rim enhancement, high ADC values, and a large number of lesions individually increase the probability and diagnostic confidence that a lesion is a TDL.