Diffusion-weighted MRI characteristics of the cerebral metastasis to brain boundary predicts patient outcomes

A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases

AIM

To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).

MATERIALS AND METHODS

A total of 215 lung cancer patients with BrMs confirmed by surgical pathology were retrospectively included in five centres, 167 patients were assigned to the training cohort, and 48 to the external test cohort. All patients underwent regular follow-up brain MRIs. Clinical and morphological MRI models for predicting RFS were built using univariate and multivariate Cox regressions, respectively. Handcrafted and deep learning (DL) signatures were constructed from BrMs pretreatment MR images using the least absolute shrinkage and selection operator (LASSO) method, respectively. A DLRM was established by integrating the clinical and morphological MRI predictors, handcrafted and DL signatures based on the multivariate Cox regression coefficients. The Harrell C-index, area under the receiver operating characteristic curve (AUC), and Kaplan-Meier's survival analysis were used to evaluate model performance.

RESULTS

The DLRM showed satisfactory performance in predicting RFS and 6- to 18-month intracranial recurrence in lung cancer patients after BrMs resection, achieving a C-index of 0.79 and AUCs of 0.84-0.90 in the training set and a C-index of 0.74 and AUCs of 0.71-0.85 in the external test set. The DLRM outperformed the clinical model, morphological MRI model, handcrafted signature, DL signature, and clinical-morphological MRI model in predicting RFS (P < 0.05). The DLRM successfully classified patients into high-risk and low-risk intracranial recurrence groups (P < 0.001).

CONCLUSION

This MRI-based DLRM could predict RFS in lung cancer patients after surgical resection of BrMs.

Bench-to-bedside imaging in brain metastases: a road to precision oncology

Radiology has seen tremendous evolution in the last few decades. At the same time, oncology has made great strides in diagnosing and treating cancer. Distant metastases of neoplasms are being encountered more often in light of longer patient survival due to better therapeutic strategies and diagnostic methods. Brain metastasis (BM) is a dismal manifestation of systemic cancer. In the present scenario, magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are playing a big role in providing molecular information about cancer. Lately, molecular imaging has emerged as a stirring arena of dynamic imaging techniques that have enabled clinicians and scientists to noninvasively visualize and understand biological processes at the cellular and molecular levels. This knowledge has impacted etiopathogenesis, detection, personalized treatment, drug development, and our understanding of carcinogenesis. This article offers insight into the molecular biology underlying brain metastasis, its pathogenesis, imaging protocols, and algorithms. It also discusses disease-specific molecular imaging features, focusing on common tumors that spread to the brain, such as lung, breast, colorectal cancer, melanoma, and renal cell carcinoma. Additionally, it covers various targeted treatment options, criteria for assessing treatment response, and the role of artificial intelligence in diagnosing, managing, and predicting prognosis for patients with brain metastases.

A metric for quantitative evaluation of glioma margin changes in magnetic resonance imaging

BACKGROUND

Gliomas differ from meningiomas in their margins, most of which are not separated from the surrounding tissue by a distinct interface.

PURPOSE

To characterize the margins of gliomas quantitatively based on the margin sharpness coefficient (MSC) is significant for clinical judgment and invasive analysis of gliomas.

MATERIAL AND METHODS

The data for this study used magnetic resonance image (MRI) data from 67 local patients and 15 open patients to quantify the intensity of changes in the glioma margins of the brain using MSC. The accuracy of MSC was assessed by consistency analysis and Bland-Altman test analysis, as well as invasive correlations using receiver operating characteristic (ROC) and Spearman correlation coefficients for subjects.

RESULTS

In grading the tumors, the mean MSC values were significantly lower for high-grade gliomas (HGG) than for low-grade gliomas (LGG). The concordance correlation between the measured gradient and the actual gradient was high (HGG: 0.981; LGG: 0.993), and the Bland-Altman mean difference at the 95% confidence interval (HGG: -0.576; LGG: 0.254) and the limits of concordance (HGG: 5.580; LGG: 5.436) indicated no statistical difference. The correlation between MSC and invasion based on the margins of gliomas showed an AUC of 0.903 and 0.911 for HGG and LGG, respectively. The mean Spearman correlation coefficient of the MSC versus the actual distance of invasion was -0.631 in gliomas.

CONCLUSION

The relatively low MSC on the blurred margins and irregular shape of gliomas may help in benign-malignant differentiation and invasion prediction of gliomas and has potential application for clinical judgment.

Peritumoural MRI radiomics signature of brain metastases can predict epidermal growth factor receptor mutation status in lung adenocarcinoma

AIM

To investigate whether magnetic resonance imaging (MRI) radiomics features of brain metastases (BMs) can predict epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma.

MATERIALS AND METHODS

Between June 2014 and December 2022, 58 histopathologically confirmed lung adenocarcinoma patients (27 with EGFR wild-type, 31 with EGFR mutation) who underwent gadobenate dimeglumine-enhanced brain MRI were recruited retrospectively. A total of 123 metastatic brain lesions were allocated randomly into the training cohort (n=86) and test cohort (n=37) at a ratio of 7:3. Radiomics models based on multi-sequence MRI images in different regions such as volume of interest (VOI)enhancing tumour, VOIwholetumour, VOIperitumour 1mm, VOIperitumour 3mm, and VOIperitumour 5mm were built. The optimal radiomics model was integrated into the clinical or radiological indicators to construct a fusion model through multivariable logistic regression analysis.

RESULTS

The optimal radiomics model based on the VOIperitumour 1mm, a combination of nine features selected from the fluid-attenuated inversion recovery (FLAIR) sequence, yielded areas under the curves (AUCs) of >0.75 in the training and test cohorts. The prediction of the fusion model with integration of clinical factors (age) and radiomics score (the optimal radiomics model) was not better than that of the optimal radiomics model alone in the test cohort (AUC: 0.808 and 0.785, respectively, p=0.525).

CONCLUSION

The FLAIR radiomics model based on VOIperitumour 1mm as an effective biomarker helps predict EGFR mutation status in lung adenocarcinoma patients with BMs and then assists clinicians in selecting optimal treatment strategies.

Radiosurgery for Brain Metastases: Challenges in Imaging Interpretation after Treatment

Stereotactic radiosurgery (SRS) has transformed the management of brain metastases by achieving local tumor control, reducing toxicity, and minimizing the need for whole-brain radiation therapy (WBRT). This review specifically investigates radiation-induced changes in patients treated for metastasis, highlighting the crucial role of magnetic resonance imaging (MRI) in the evaluation of treatment response, both at very early and late stages. The primary objective of the review is to evaluate the most effective imaging techniques for assessing radiation-induced changes and distinguishing them from tumor growth. The limitations of conventional imaging methods, which rely on size measurements, dimensional criteria, and contrast enhancement patterns, are critically evaluated. In addition, it has been investigated the potential of advanced imaging modalities to offer a more precise and comprehensive evaluation of treatment response. Finally, an overview of the relevant literature concerning the interpretation of brain changes in patients undergoing immunotherapies is provided.

The relationship of T2 hypointensity and diffusion restriction of brain metastases with the presence and amount of vasogenic edema in MRI

AIM

Brain metastases are seen in 15-40% of patients diagnosed with cancer. We aimed to search the relationship between the T2 hypointensity, diffusion-weighted imaging characteristics, and the presence and amount of vasogenic edema of brain metastasis in magnetic resonance imaging (MRI).

METHODS

A total of 292 patients with brain metastasis were included in the study. T2 signals of metastatic lesions, accompanying diffusion restriction and perilesional vasogenic edema findings, were investigated. In metastases accompanied by vasogenic edema, the largest dimension of the vasogenic edema-mass complex on T2-weighted sequences and the largest dimension of the mass in contrast-enhanced T1-weighted series were measured and the edema-mass ratio (EMR) was calculated by comparing these two values.

RESULTS

The frequency of vasogenic edema was statistically significantly higher in T2 hypointense metastases (89.1% vs 58.8%, χ2 = 18.949, p = <.001) and metastases accompanied by diffusion restriction(81% vs 61.5%, χ2 = 6.971, p = .008). EMR values were found to be statistically significantly higher in T2 hypointense metastases (EMR→ Z = -4.507, p = <.001) and metastases with diffusion restriction(EMR→ Z = -3.819, p = .001).

CONCLUSIONS

The frequency of vasogenic edema and EMR rates were higher in patients in T2 hypointense metastases and metastases accompanied by diffusion restriction in MRI.

The impact of preoperative MRI-based apparent diffusion coefficients on local recurrence and outcome in patients with cerebral metastases

BACKGROUND

Surgery of single cerebral metastases is standard but frequently fails to achieve local tumour control. Reliable predictors for local tumour progression and overall survival are unknown. MRI-based apparent diffusion coefficients (ADC) correlate with tumour cellularity and invasion. The present study analysed a potential relation between the MRI based apparent diffusion coefficients local recurrence and outcome in patients with brain metastases.

METHODS

A retrospective analysis was performed for patients with cerebral metastases and complete surgical resection evaluated by an early postoperative MRI < 72h. Minimal ADC and mean ADC were assessed in preoperative 1,5T-MRI scans by placing regions of interests in the tumour and the peritumoural tissue.

RESULTS

Analysis of the relation between ADC values, local progression and outcome was performed in 86 patients with a mean age of 59 years (range 33-83 years). Primary site was NSCLC in 37.2% of all cases. Despite complete resection 33.7% of all patients suffered from local in-brain-progression. There were no significant differences in ADC values in groups based on histology. In the present cohort, the mean ADCmin and the mean ADCmean within the metastasis did not differ significantly between patients with and without a later local in-brain progression (634 × 10^-6 vs. 661 × 10^-6 mm²/s and 1324 × 10^-6 vs. 1361 × 10^-6 mm²/s; 1100 × 10^-6 vs. 1054 × 10^-6 mm²/s; each p > 0.05). Mean ADC values did not correlate significantly with PFS and OAS.

CONCLUSION

In the present study analysed ADC values had no significant impact on local in brain progression and survival parameters.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Metastatic Lesions of the Brain and Spine

Brain and spinal metastases are common in cancer patients and are associated with significant morbidity and mortality. Continued advancement in the systemic care of cancer has increased the life expectancy of patients, and consequently, the incidence of brain and spine metastasis has increased. There has been an increase in the understanding of oncogenic mutations, and research has also demonstrated spatial and temporal mutations in patients that may drive overall treatment resistance and failure. Combinatory treatments with radiation, surgery, and newer systemic therapies have continued to increase the life expectancy of patients with brain and spine metastases. Given the overall complexity of brain and spine metastases, this chapter aims to give a comprehensive overview and cover important topics concerning brain and spine metastases. This will include the molecular, genetic, radiographic, surgical, and non-surgical treatments of brain and spinal metastases.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI

OBJECTIVES

To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.

METHODS

Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum.

RESULTS

Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.

CONCLUSION

Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.

ADVANCES IN KNOWLEDGE

The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Biological implications and clinical potential of invasion and migration related miRNAs in glioma

Gliomas are the most common primary malignant brain tumors and are highly aggressive. Invasion and migration are the main causes of poor prognosis and treatment resistance in gliomas. As migration and invasion occur, patient survival and prognosis decline dramatically. MicroRNAs (miRNAs) are small, non-coding 21-23 nucleotides involved in regulating the malignant phenotype of gliomas, including migration and invasion. Numerous studies have demonstrated the mechanism and function of some miRNAs in glioma migration and invasion. However, the biological and clinical significance (including diagnosis, prognosis, and targeted therapy) of glioma migration and invasion-related miRNAs have not been systematically discussed. This paper reviews the progress of miRNAs-mediated migration and invasion studies in glioma and discusses the clinical value of migration and invasion-related miRNAs as potential biomarkers or targeted therapies for glioma. In addition, these findings are expected to translate into future directions and challenges for clinical applications. Although many biomarkers and their biological roles in glioma invasion and migration have been identified, none have been specific so far, and further exploration of clinical treatment is still in progress; therefore, we aimed to further identify specific markers that may guide clinical treatment and improve the quality of patient survival.

Role of apparent diffusion coefficient measurement in differentiating histological subtypes of brain metastasis of lung cancer

OBJECTIVE:

The aim of this study was to investigate the role of apparent diffusion coefficient of diffusion-weighted imaging in differentiating histological subtypes of brain metastasis of lung cancer.

METHODS:

Diffusion-weighted imaging of 158 patients (mean age: 61.2±10.68 years) with brain metastasis of lung cancer (36 small cell lung cancer and 122 non-small cell lung cancer) were retrospectively evaluated. The minimum and mean apparent diffusion coefficient values of the metastasis, apparent diffusion coefficient of edema around the metastasis, and apparent diffusion coefficient of contralateral brain parenchyma were measured. Normalized apparent diffusion coefficient was calculated by proportioning the mean apparent diffusion coefficient of the metastasis to the apparent diffusion coefficient of the contralateral brain parenchyma. Minimum and mean apparent diffusion coefficient of the metastasis, apparent diffusion coefficient of edema around metastasis, and normalized apparent diffusion coefficient were compared between small cell lung cancer and non-small cell lung cancer metastases.

RESULTS:

Minimum apparent diffusion coefficient, mean apparent diffusion coefficient, and normalized apparent diffusion coefficient values of small cell lung cancer metastases (0.43±0.19×10⁻³mm²/s, 0.63±0.20×10⁻³mm²/s, and 0.81 [0.55–1.44], respectively) were significantly lower than those of non-small cell lung cancer metastases (0.71±0.26×10⁻³mm²/s, 0.93±0.29×10⁻³mm²/s, and 1.30 [0.60–3.20], respectively; p<0.001). Mean apparent diffusion coefficient of edema of small cell lung cancer metastases (1.21±0.28×10⁻³mm²/s) was significantly lower than that of non-small cell lung cancer metastases (1.39±0.26×10⁻³mm²/s, p=0.020). The best cutoff values of minimum apparent diffusion coefficient, mean apparent diffusion coefficient, normalized apparent diffusion coefficient, and apparent diffusion coefficient of edema for the differentiation of small cell lung cancer and non-small cell lung cancer were found to be 0.56×10⁻³mm²/s, 0.82×10⁻³mm²/s, 1.085, and 1.21×10⁻³mm²/s, respectively. The area under the receiver operating characteristic curve, sensitivity, and specificity values were, respectively, 0.812, 80.6, and 73.8% for minimum apparent diffusion coefficient; 0.825, 91.7, and 61.5% for mean apparent diffusion coefficient; 0.845, 80.6, and 73.8% for normalized apparent diffusion coefficient; and 0.698, 75.0, and 67.7% for apparent diffusion coefficient of edema.

CONCLUSIONS:

Minimum apparent diffusion coefficient, mean apparent diffusion coefficient, normalized apparent diffusion coefficient, and apparent diffusion coefficient of edema around metastasis can differentiate histological subtypes of brain metastasis of lung cancer.

ADC textural features in patients with single brain metastases improve clinical risk models

AIMS

In this retrospective study we performed a quantitative textural analysis of apparant diffusion coefficient (ADC) images derived from diffusion weighted MRI (DW-MRI) of single brain metastases (BM) patients from different primary tumors and tested whether these imaging parameters may improve established clinical risk models.

METHODS

We identified 87 patients with single BM who had a DW-MRI at initial diagnosis. Applying image segmentation, volumes of contrast-enhanced lesions in T1 sequences, hyperintense T2 lesions (peritumoral border zone (T2PZ)) and tumor-free gray and white matter compartment (GMWMC) were generated and registered to corresponding ADC maps. ADC textural parameters were generated and a linear backward regression model was applied selecting imaging features in association with survival. A cox proportional hazard model with backward regression was fitted for the clinical prognostic models (diagnosis-specific graded prognostic assessment score (DS-GPA) and the recursive partitioning analysis (RPA)) including these imaging features.

RESULTS

Thirty ADC textural parameters were generated and linear backward regression identified eight independent imaging parameters which in combination predicted survival. Five ADC texture features derived from T2PZ, the volume of the T2PZ, the normalized mean ADC of the GMWMC as well as the mean ADC slope of T2PZ. A cox backward regression including the DS-GPA, RPA and these eight parameters identified two MRI features which improved the two risk scores (HR = 1.14 [1.05;1.24] for normalized mean ADC GMWMC and HR = 0.87 [0.77;0.97]) for ADC 3D kurtosis of the T2PZ.) CONCLUSIONS: Textural analysis of ADC maps in patients with single brain metastases improved established clinical risk models. These findings may aid to better understand the pathogenesis of BM and may allow selection of patients for new treatment options.

VCAM-1-targeted MRI Improves Detection of the Tumor-brain Interface

PURPOSE

Despite optimal local therapy, tumor cell invasion into normal brain parenchyma frequently results in recurrence in patients with solid tumors. The aim of this study was to determine whether microvascular inflammation can be targeted to better delineate the tumor-brain interface through vascular cell adhesion molecule-1 (VCAM-1)-targeted MRI.

EXPERIMENTAL DESIGN

Intracerebral xenograft rat models of MDA231Br-GFP (breast cancer) brain metastasis and U87MG (glioblastoma) were used to histologically examine the tumor-brain interface and to test the efficacy of VCAM-1-targeted MRI in detecting this region. Human biopsy samples of the brain metastasis and glioblastoma margins were examined for endothelial VCAM-1 expression.

RESULTS

The interface between tumor and surrounding normal brain tissue exhibited elevated endothelial VCAM-1 expression and increased microvessel density. Tumor proliferation and stemness markers were also significantly upregulated at the tumor rim in the brain metastasis model. T2*-weighted MRI, following intravenous administration of VCAM-MPIO, highlighted the tumor-brain interface of both tumor models more extensively than gadolinium-DTPA-enhanced T1-weighted MRI. Sites of VCAM-MPIO binding, evident as hypointense signals on MR images, correlated spatially with endothelial VCAM-1 upregulation and bound VCAM-MPIO beads detected histologically. These findings were further validated in an orthotopic medulloblastoma model. Finally, the tumor-brain interface in human brain metastasis and glioblastoma samples was similarly characterized by microvascular inflammation, extending beyond the region detectable using conventional MRI.

CONCLUSIONS

This work illustrates the potential of VCAM-1-targeted MRI for improved delineation of the tumor-brain interface in both primary and secondary brain tumors.

Multiparametric MR Imaging Features of Primary CNS Lymphomas

Objective

Primary central nervous system lymphomas (PCNS) are relatively rare tumors, accounting for about 4% of all brain tumors. On neuroimaging, they are characterized by a low MR signal in T1, isointense in T2, bright uniform contrast enhancement, and diffusion restriction. The aim of this study is to note the lack of effectiveness of the MR/CT perfusion technique in complex multiparametric imaging in the differential diagnosis of primary lymphomas of the central nervous system in comparison with highly malignant gliomas and brain metastases.

Materials and Methods

This prospective study included 80 patients with CNS tumors examined/operated at the Federal Center for Neurosurgery (Tyumen, Russia) from 2018 to 2021. The patients were divided into 4 groups: group 1 consisted of 33 cases with primary CNS lymphomas (10 cases with atypical manifestations according to perfusion parameters and 23 cases of classic CNS lymphomas), group 2 with anaplastic astrocytomas—14 cases, group 3—23 cases with glioblastomas and group 4—10 cases with solitary metastatic lesions. The study was carried out on a General Electric Discovery W750 3T magnetic resonance tomograph, a Canon Aquilion One multispiral X-ray computed tomograph (Gadovist 7.5 ml, Yomeron 400 mg−50 ml). Additionally, immunohistochemical analysis was carried out with the following markers: CD3, CD20, CD34, Ki-67, VEGF.

Results

It has been established that MR/CT perfusion is not a highly sensitive method for visualizing primary CNS lymphomas, as previously thought, but at the same time, the method has a number of undeniable advantages that make it indispensable in the algorithm of a complex multiparametric diagnostic approach for this type of tumor. Nevertheless, PLCNS is characterized by an atypical manifestation, which is an exception to the rule.

Conclusions

The possibilities of neuroimaging of primary lymphomas, even with the use of improved techniques for collecting MR/CT data, are limited and do not always allow reliable differentiation from other neoplasms.

The Extent of Necrosis in Brain Metastases May Predict Subtypes of Primary Cancer and Overall Survival in Patients Receiving Craniotomy

Although necrosis is common in brain metastasis (BM), its biological and clinical significances remain unknown. We evaluated necrosis extent differences by primary cancer subtype and correlated BM necrosis to overall survival post-craniotomy. We analyzed 145 BMs of patients receiving craniotomy. Necrosis to tumor ratio (NTR) was measured. Patients were divided into two groups by NTR: BMs with sparse necrosis and with abundant necrosis. Clinical features were compared. To investigate factor relevance for BM necrosis, multivariate logistic regression, random forests, and gradient boosting machine analyses were performed. Kaplan−Meier analysis and log-rank tests were performed to evaluate the effect of BM necrosis on overall survival. Lung cancer was a more common origin for BMs with abundant necrosis (42/72, 58.33%) versus sparse necrosis (23/73, 31.51%, p < 0.01). Primary cancer subtype and tumor volume were the most relevant factors for BM necrosis (p < 0.01). BMs harboring moderately abundant necrosis showed longer survival, versus sparse or highly abundant necrosis (p = 0.04). Lung cancer BM may carry larger necrosis than BMs from other cancers. Further, moderately abundant necrosis in BM may predict a good prognosis post-craniotomy.

Advanced MRI to differentiate schwannomas and metastases in the cerebellopontine angle/internal auditory canal

BACKGROUND AND PURPOSE

Differentiating schwannomas and metastases in the cerebellopontine angles (CPA)/internal auditory canals (IAC) can be challenging. This study aimed to assess the role of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) to differentiate schwannomas and metastases in the CPA/IAC.

METHODS

We retrospectively reviewed 368 patients who were diagnosed with schwannomas or metastases in the CPA/IAC between April 2017 and February 2022 in a single academic center. Forty-three patients had pretreatment DWI and DCE-MRI along with conventional MRI. Normalized mean apparent diffusion coefficient ratio (nADCmean) and DCE-MRI parameters of fractional plasma volume (Vp), flux rate constant (Kep), and forward volume transfer constant were compared along with patients' demographics and conventional imaging features between schwannomas and metastases as appropriate. The diagnostic performances and multivariate logistic regression analysis were performed using the significantly different values.

RESULTS

Between 23 schwannomas (15 males; median 48 years) and 20 metastases (9 males; median 61 years), nADCmean (median: 1.69 vs. 1.43; p = .002), Vp (median: 0.05 vs. 0.20; p < .001), and Kep (median: 0.41 vs. 0.81 minute^-1 ; p < .001) were significantly different. The diagnostic performances of nADCmean, Vp, and Kep were 0.77, 0.90, and 0.83 area under the curves, with cutoff values of 1.68, 0.12, and 0.53, respectively. Vp was identified as the most significant parameter for the tumor differentiation in the multivariate logistic regression analysis (p < .001).

CONCLUSIONS

DWI and DCE-MRI can help differentiate CPA/IAC schwannomas and metastases, and Vp is the most significant parameter.

Value of DWI Combined with Magnetic Resonance Spectroscopy in the Differential Diagnosis between Recurrent Glioma and Radiation Injury: A Meta-Analysis

To analyse the value of the apparent diffusion coefficient (ADC) in diffusion-weighted imaging (DWI) and the choline (Cho)/creatine (Cr) ratio and Cho/N-acetyl-aspartate (NAA) ratio in magnetic resonance spectroscopy (MRS) in the differential diagnosis between recurrent glioma and radiation injury. Chinese and English studies related to the diagnosis of recurrent glioma and radiation injury using DWI and MRS and published before 15 October 2022 were retrieved from PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biomedical Literature Database, VIP Journal Database, and Wanfang Database for a meta-analysis. A total of 11 articles were included in this study. ADC was lower in the recurrent glioma group than in the radiation injury group (standardized mean difference = -1.29, 95% confidence interval (CI) (-1.87, -0.71), P < 0.001). The Cho/Cr ratio was higher in the recurrent glioma group than in the radiation injury group (weighted mean difference = 0.65, 95% CI (0.40, 0.90), and P < 0.001). The Cho/NAA ratio was higher in the recurrent glioma group than in the radiation injury group, as evidenced by the sensitivity analysis. The sensitivity and specificity of the Cho/Cr ratio were 0.85 (0.73-0.92) and 0.82 (0.67-0.91), respectively, and the area under the curve was 0.86. The sensitivity and specificity of the Cho/NAA ratio were 0.82 (0.66-0.91) and 0.94 (0.69-0.99), respectively, and the area under the curve was 0.93. This meta-analysis showed that ADC, Cho/Cr, and Cho/NAA ratios all had high sensitivity and specificity. Therefore, DWI combined with MRS can effectively improve the diagnosis of recurrent glioma and radiation injury.

The Role of the Immune Response in Brain Metastases: Novel Imaging Biomarkers for Immunotherapy

Brain metastases are a major clinical problem, and immunotherapy offers a novel treatment paradigm with the potential to synergize with existing focal therapies like surgery and radiosurgery or even replace them in future. The brain is a unique microenvironment structurally and immunologically. The immune response is likely to be crucial to the adaptation of systemic immune modulating agents against this disease. Imaging is frequently employed in the clinical diagnosis and management of brain metastasis, so it is logical that brain imaging techniques are investigated as a source of biomarkers of the immune response in these tumors. Current imaging techniques in clinical use include structural MRI (post-contrast T1W sequences, T2, and FLAIR), physiological sequences (perfusion- and diffusion-weighted imaging), and molecular imaging (MR spectroscopy and PET). These are reviewed for their application to predicting and measuring the response to immunotherapy in brain metastases.

Investigating diffusion patterns of brain metastases pre- and post-stereotactic radiosurgery: a feasibility study

Purpose.Metastatic complications are responsible for 90% of cancer-associated mortality. Magnetic resonance imaging (MRI) can be used to observe the brain's microstructure and potentially correlate changes with metastasis occurrence. Diffusion weighted imaging (DWI) is an MRI technique that utilizes the kinetics of water molecules within the body. The aim of this study is to use DWI to characterize diffusion changes within brain metastases in cancer patients pre- and post-stereotactic radiosurgery (SRS).Methods.We retrospectively analyzed 113 metastases from 13 patients who underwent SRS for brain metastasis recurrence. Longitudinal apparent diffusion coefficient (ADC) maps were registered to Gd-T1 images and CT, and clinical metastasis ROIs from all SRS treatments were retrospectively transferred onto these ADC maps for analysis. Metastases were characterized based on pre-SRS diffusion pattern, primary cancer site, and post-SRS outcome. ADC values were calculated pre- and post-SRS.Results.ADC values were significantly elevated (980.2 × 10^-6mm²s^-1and 1040.3 × 10^-6mm²s^-1pre- and post-SRS, respectively) when compared to healthy brain tissue (826.8 × 10^-6mm²s^-1) for all metastases. Three identified pre-SRS patterns were significantly different before SRS and within 6 months post-SRS. No significant differences were observed between different primaries pre-SRS. Post-SRS, Lung metastases ADC decreased by 86.2 × 10^-6mm²s^-1, breast metastases increased by 116.7 × 10^-6mm²s^-1, and genitourinary metastases showed no significant ADC change. SRS outcomes showed ADC variability pre-treatment but no significant differences pre- and post-SRS, except at 6-9 months post-SRS where progressing metastases were elevated when compared to other response groups.Conclusion. This study provided a unique opportunity to characterize diffusion changes in brain metastases before their manifestation on standard Gd-T1 images and post-SRS. Identified patterns may improve early detection of brain metastases as well as predict their response to treatment.

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps

BACKGROUND

Brain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less  aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC.

METHODS

We enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere.

RESULTS

Out of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988.

CONCLUSIONS

In patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer.

TRIAL REGISTRATION

The study was registered in the German Clinical Trials Register (DRKS00023016).

Diffusion Tensor Imaging Can Discriminate the Primary Cell Type of Intracranial Metastases for Patients with Lung Cancer

PURPOSE

Histopathological differentiation of primary lung cancer is clinically important. We aimed to investigate whether diffusion tensor imaging (DTI) parameters of metastatic brain lesions could predict the histopathological types of the primary lung cancer.

METHODS

In total, 53 patients with 98 solid metastatic brain lesions of lung cancer were included. Lung tumors were subgrouped as non-small cell carcinoma (NSCLC) (n = 34) and small cell carcinoma (SCLC) (n = 19). Apparent diffusion coefficient (ADC) and Fractional anisotropy (FA) values were calculated from solid enhanced part of the brain metastases. The association between FA and ADC values and histopathological subtype of the primary tumor was investigated.

RESULTS

The mean ADC and FA values obtained from the solid part of the brain metastases of SCLC were significantly lower than the NSCLC metastases (P < 0.001 and P = 0.003, respectively). ROC curve analysis showed diagnostic performance for mean ADC values (AUC=0.889, P = < 0.001) and FA values (AUC = 0.677, P = 0.002). Cut-off value of > 0.909 × 10^-3 mm²/s for mean ADC (Sensitivity = 80.3, Specificity = 83.8, PPV = 89.1, NPV = 72.1) and > 0.139 for FA values (Sensitivity = 80.3, Specificity = 54.1, PPV = 74.2, NPV= 62.5) revealed in differentiating NSCLC from NSCLC.

CONCLUSION

DTI parameters of brain metastasis can discriminate SCLC and NSCLC. ADC and FA values of metastatic brain lesions due to the lung cancer may be an important tool to differentiate histopathological subgroups. DTI may guide clinicians for the management of intracranial metastatic lesions of lung cancer.

A Rare Case of Diffuse Subependymal Periventricular Metastases from Small Cell Lung Carcinoma

Small cell lung cancer, whose essence is neuroendocrine tumors, makes up proximately 14-20% of all lung cancer circumstances. Compared to non-small cell lung cancer, its clinical manifestation seems more positive and has a tendency to disseminate earlier in the process of its natural past. About 10% of patients present with brain metastases at the time of provisional diagnosis and sometimes all along the course of their disease, there will be 40-50% of developed brain metastases in addition. Although metastases in the brain parenchyma are often found in patients with advanced lung cancer, periventricular metastases are rare. We report one case of diffuse subependymal periventricular metastases from small cell carcinoma of the lung.

Brain metastases from breast cancer

BACKGROUND

Breast cancer (BC) is one of the commonest causes of brain metastases (BM): approximately 10-16 % of patients diagnosed with metastatic breast cancer will eventually develop BM during the course of their disease, however, certain subtypes have a higher risk of this event. The aim of this analysis was therefore to evaluate the prognosis and the pattern and imaging features of BM according to different BC subtypes.

PATIENTS AND METHODS

We retrospectively reviewed the case records of patients with breast cancer and evidence of brain metastases from the database of IPO Porto between 2014-2018. The data obtained were statistically analysed.

RESULTS

We analysed 147 patients with BM from BC. The triple-negative subtype had the shortest overall survival (OS) after BM, besides a short period of time between BC and BM. HER2 overexpressing tumors had the longest OS. The estrogen-receptor positive group had the greatest interval between initial BC diagnosis and diagnosis of BM. Larger lesions showed a heterogeneous contrast enhancement and were heterogeneous pn T2WI sequences; a hyposignal on T2*WI was also associated with larger lesions. Triple-negative BC tended to have more heterogeneous lesions on T1WI. We noticed that the hippocampus is rarely affected by metastatic lesions.

CONCLUSIONS

Based on the BC subtype it is possible to make a prediction about the prognosis of the disease and some imaging features of the BM, but not about their pattern of distribution. These data support further research concerning prevention, early detection, and treatment of BM from BC.

Whole body diffusion-weighted MRI in detection of metastasis and lymphoma: a prospective longitudinal clinical study

Background

Whole-body diffusion-weighted magnetic resonance imaging (WB-DWI-MRI) is an emerging tool that has an increasing role in the diagnosis of metastasis and lymphoma. This is a longitudinal study in actual clinical settings designed to assess WB-DWI-MRI in detection of tumor spread. The study included all patients who were referred to Radiology Department, during the period from June 2016 till May 2018, with either a known primary tumor (either laboratory, radiologically, or histologically proven, of any type, affecting any organ) or with biopsy-proven lymphoma of any subtype, affecting any organ. All patients underwent WB coronal T1-weighted, STIR, axial T2-weighted, and DWI-MRI examinations before commencing any treatment with curative intent. The body was divided into lymph nodes (LNs), skeletal system, and organs (brain, lung, and liver). Patients were followed up till the nature of the lesion(s) was confirmed (clinically, radiologically, or histologically).

Results

The study included 46 patients; 27 patients had metastases and 19 had lymphomas. Sensitivities, specificities, and accuracies for LN detection were 77%, 85%, and 83%; for skeletal metastasis were 88%, 94%, and 92%; for brain lesions were 78%, 95%, and 91%; and for lung lesion were 64%, 88%, and 76%, respectively. As for the liver, all lesions were correctly identified and did not miss any lesion with accuracy of 100%. Overall, 1739 lesions were discovered in 1271 regions out of 3818 examined regions with overall sensitivity, specificity, and accuracy of 86%, 92%, and 90% respectively.

Conclusion

The diagnostic performance of WB-DWI-MRI is variable among different anatomical sites. It has good performance in diagnosis of some organs as liver, bone marrow, and some LNs regions as porta-hepatis. It has a less diagnostic performance in the lung, and LNs located in cervical, mediastinum, supraclavicular, and mesenteric regions.

Evaluating Spinal Canal Lesions Using Apparent Diffusion Coefficient Maps with Diffusion-Weighted Imaging

STUDY DESIGN

Observational study.

PURPOSE

To evaluate healthy volunteers and patients with spinal canal lesions using apparent diffusion coefficient (ADC) maps with diffusion-weighted imaging.

OVERVIEW OF LITERATURE

Decompression surgery for lumbar spinal stenosis (LSS) is selected on the basis of subjective assessment and cross-sectional magnetic resonance imaging (MRI). However, there is no objective standard for this procedure.

METHODS

We performed 3T MRI in 10 healthy volunteers and 13 patients with LSS. The ADC values in the spinal canal were evaluated at 46 vertebrae (L4/5 and L5/S1 for each participant), and the reduced and conventional fields of view were compared.

RESULTS

The ADC values were 2.72±0.12 at L4/5 in healthy volunteers, 2.76±0.19 at L5/S1 in healthy volunteers, 1.77±0.58 at L4/5 in patients with LSS, and 2.35±0.29 at L5/S1 in patients with LSS. The ADC value at L4/5 in patients with LSS was significantly lower than that at L5/S1 in patients with LSS and that at L4/5 and L5/S1 in healthy volunteers (p <0.05). With an ADC cutoff value of 2.46 to identify LSS, this approach provided an area under the curve of 0.81, sensitivity of 0.92, and specificity of 0.76 (p <0.05).

CONCLUSIONS

Preoperative examination using ADC maps permits visualization and quantification of spinal canal lesions, thus proving the utility of ADC maps in the selection of decompression surgery for LSS.

Does the application of diffusion weighted imaging improve the prediction of survival in patients with resected brain metastases? A retrospective multicenter study

BACKGROUND

Brain metastases are common in clinical practice. Many clinical scales exist for predicting survival and hence deciding on best treatment but none are individualised and none use quantitative imaging parameters. A multicenter study was carried out to evaluate the prognostic utility of a simple diffusion weighted MRI parameter, tumor apparent diffusion coefficient (ADC).

METHODS

A retrospective analysis of imaging and clinical data was performed on a cohort of 223 adult patients over a ten-year period 2002-2012 pooled from three institutions. All patients underwent surgical resection with histologically confirmed brain metastases and received adjuvant whole brain radiotherapy and/or chemotherapy. Survival was modelled using standard clinical variables and statistically compared with and without the addition of tumor ADC.

RESULTS

The median overall survival was 9.6 months (95% CI 7.5-11.7) for this cohort. Greater age (p = 0.002), worse performance status (p < 0.0001) and uncontrolled extracranial disease (p < 0.0001) were all significantly associated with shorter survival in univariate analysis. Adjuvant whole brain radiotherapy (p = 0.007) and higher tumor ADC (p < 0.001) were associated with prolonged survival. Combining values of tumor ADC with conventional clinical scoring systems such as the Graded Prognostic Assessment (GPA) score significantly improved the modelling of survival (e.g. concordance increased from 0.5956 to 0.6277 with Akaike's Information Criterion reduced from 1335 to 1324).

CONCLUSIONS

Combining advanced MRI readings such as tumor ADC with clinical scoring systems is a potentially simple method for improving and individualising the estimation of survival in patients having surgery for brain metastases.

THE ROLE OF DIFFUSION-WEIGHTED MRI OF PATIENTS WITH SPINE METASTASES

ABSTRACT Objective: The role of diffusion-weighted MRI in differential diagnostics and predicting the survival of patients with spine metastases was studied. Methods: The study included data from MRI and morphological studies of 23 patients with spine metastases. Results: The values obtained for the apparent diffusion coefficient (ADC) of tumors were compared with their histological type, cell density and Ki-67 proliferation index. The effect of ADC values on overall patient survival was also assessed. A reliable inverse correlation was established between ADC values and Ki-67 proliferation index for various types of spine metastases (r=-0.753, p=0.017). The dependence of ADC values and overall survival of patients with metastases in the spine is shown. Conclusion: The technique of diffusion-weighted MRI can be used as part of a comprehensive assessment in the preoperative planning of surgical treatment, and as a prognostic factor of overall survival for this group of patients. Level of Evidence II. Prognostic retrospective study,

Brain MRI imaging characteristics predict treatment response and outcome in patients with de novo brain metastasis of EGFR-mutated NSCLC

Patients with non-small cell lung cancer (NSCLC) and de novo brain metastasis (BM) have poor prognosis. We aim to investigate the characteristic of brain magnetic resonance (MR) imaging and the association with the treatment response of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) for lung cancer with BM.EGFR-mutated NSCLC patients with BM from October 2013 to December 2017 in a tertiary referral center were retrospectively analyzed. Patient's age, sex, cell type, EGFR mutation status, treatment, and characteristics of BM were collected. Survival analysis was performed using Kaplan-Meier method. The efficacy of different EGFR-TKIs were also analyzed.Among the 257 eligible patients, 144 patients with Exon 19 deletion or Exon 21 L858R were included for analysis. The erlotinib group had the best progression free survival (PFS) (median PFS 13 months, P = .04). The overall survival (OS) revealed no significant difference between three EGFR-TKI groups. Brain MR imaging features including tumor necrosis, rim enhancement and specific tumor locations (frontal lobe, putamen or cerebellum) were factors associated with poor prognosis. Patients with poor prognostic imaging features, the high-risk group, who received erlotinib had the best PFS (median PFS 12 months, P < .001). However, the OS revealed no significant difference between 3 EGFR-TKI groups. The low risk group patients had similar PFS and OS treated with three different EGFR-TKIs.In NSCLC patients with common EGFR mutation and de novo BM, those with poor prognostic brain MR characteristics, erlotinib provided better PFS than afatinib or gefitinib.

Probabilistic independent component analysis of dynamic susceptibility contrast perfusion MRI in metastatic brain tumors

PURPOSE

To identify clinically relevant magnetic resonance imaging (MRI) features of different types of metastatic brain lesions, including standard anatomical, diffusion weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion MRI.

METHODS

MRI imaging was retrospectively assessed on one hundred and fourteen (N = 114) brain metastases including breast (n = 27), non-small cell lung cancer (NSCLC, n = 43) and 'other' primary tumors (n = 44). Based on 114 patient's MRI scans, a total of 346 individual contrast enhancing tumors were manually segmented. In addition to tumor volume, apparent diffusion coefficients (ADC) and relative cerebral blood volume (rCBV) measurements, an independent component analysis (ICA) was performed with raw DSC data in order to assess arterio-venous components and the volume of overlap (AVOL) relative to tumor volume, as well as time to peak (TTP) of T₂* signal from each component.

RESULTS

Results suggests non-breast or non-NSCLC ('other') tumors had higher volume compare to breast and NSCLC patients (p = 0.0056 and p = 0.0003, respectively). No differences in median ADC or rCBV were observed across tumor types; however, breast and NSCLC tumors had a significantly higher "arterial" proportion of the tumor volume as indicated by ICA (p = 0.0062 and p = 0.0018, respectively), while a higher "venous" proportion were prominent in breast tumors compared with NSCLC (p = 0.0027) and 'other' lesions (p = 0.0011). The AVOL component was positively related to rCBV in all groups, but no correlation was found for arterial and venous components with respect to rCBV values. Median time to peak of arterial and venous components were 8.4 s and 12.6 s, respectively (p < 0.0001). No difference was found in arterial or venous TTP across groups.

CONCLUSIONS

Advanced ICA-derived component analysis demonstrates perfusion differences between metastatic brain tumor types that were not observable with classical ADC and rCBV measurements. These results highlight the complex relationship between brain tumor vasculature characteristics and the site of primary tumor diagnosis.

Detectability of radiation-induced changes in magnetic resonance biomarkers following stereotactic radiosurgery: A pilot study

Our objective was to investigate direct voxel-wise relationship between dose and early MR biomarker changes both within and in the high-dose region surrounding brain metastases following stereotactic radiosurgery (SRS). Specifically, we examined the apparent diffusion coefficient (ADC) from diffusion-weighted imaging and the contrast transfer coefficient (Ktrans) and volume of extracellular extravascular space (ve) derived from dynamic contrast-enhanced (DCE) MRI data. We investigated 29 brain metastases in 18 patients using 3 T MRI to collect imaging data at day 0, day 3 and day 20 following SRS. The ADC maps were generated by the scanner and Ktrans and ve maps were generated using in-house software for dynamic tracer-kinetic analysis. To enable spatially-correlated voxel-wise analysis, we developed a registration pipeline to register all ADC, Ktrans and ve maps to the planning MRI scan. To interrogate longitudinal changes, we computed absolute ΔADC, ΔKtrans and Δve for day 3 and 20 post-SRS relative to day 0. We performed a Kruskall-Wallice test on each biomarker between time points and investigated dose correlations within the gross tumour volume (GTV) and surrounding high dose region > 12 Gy via Spearman's rho. Only ve exhibited significant differences between day 0 and 20 (p < 0.005) and day 3 and 20 (p < 0.05) within the GTV following SRS. Strongest dose correlations were observed for ADC within the GTV (rho = 0.17 to 0.20) and weak correlations were observed for ADC and Ktrans in the surrounding > 12 Gy region. Both ΔKtrans and Δve showed a trend with dose at day 20 within the GTV and > 12 Gy region (rho = -0.04 to -0.16). Weak dose-related decreases in Ktrans and ve within the GTV and high dose region at day 20 most likely reflect underlying vascular responses to radiation. Our study also provides a voxel-wise analysis schema for future MR biomarker studies with the goal of elucidating surrogates for radionecrosis.

Apparent diffusion coefficient as an effective index for the therapeutic efficiency of brain chemoradiotherapy for brain metastases from lung cancer

BACKGROUND

The potential of apparent diffusion coefficient (ADC) value alteration before and after chemoradiotherapy as a potential monitor for therapeutic efficiency of treatment for brain metastases from lung cancer were discussed.

METHOD

Thirty lung cancer patients with brain metastases, conventional magnetic resonance imaging (MRI) examination and diffusion weighted imaging (DWI) were performed one week before chemoradiotherapy and after one treatment cycle and two treatment cycles. 43 tumor lesions were divided into effective group and invalid group according to the changes of the tumor size. The differences in ADC values at different time points before and after treatment in each treatment group were analyzed.

RESULT

The maximum diameter of the tumor was no difference after one treatment cycle, but decreased after two treatment cycles. ADC values significantly increased after both one and two treatment cycles. In effective group, the ADC values were significantly increased after one and two treatment cycles. While, there are no difference in invalid group after one treatment cycle but decreased after two treatment cycles. ΔADC values in effective group after one and two treatment cycles were both significantly higher than those in the invalid group. ROC curve analysis then revealed that the area under the curve (AUC) of ΔADC after one treatment was 0.872.

CONCLUSION

ADC values in brain metastases from lung cancer can help monitor and dynamically observe the therapeutic efficiency of whole brain chemoradiotherapy.

Imaging biomarkers from multiparametric magnetic resonance imaging are associated with survival outcomes in patients with brain metastases from breast cancer

OBJECTIVES

The aim of this study is to investigate the correlation of survival outcomes with imaging biomarkers from multiparametric magnetic resonance imaging (MRI) in patients with brain metastases from breast cancer (BMBC).

METHODS

This study was approved by the institutional review board. Twenty-two patients with BMBC who underwent treatment involving bevacizumab on day 1, etoposide on days 2-4, and cisplatin on day 2 in 21-day cycles were prospectively enrolled for a phase II study. Three brain MRIs were performed: before the treatment, on day 1, and on day 21. Eight imaging biomarkers were derived from dynamic contrast-enhanced MRI (Peak, IAUC₆₀, K^trans, k_ep, v_e), diffusion-weighted imaging [apparent diffusion coefficient (ADC)], and MR spectroscopy (choline/N-acetylaspartate and choline/creatine ratios). The relative changes (Δ) in these biomarkers were correlated with the central nervous system (CNS)-specific progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier and Cox proportional hazard models.

RESULTS

There were no significant differences in the survival outcomes as per the changes in the biomarkers on day 1. On day 21, those with a low ΔK^trans (p = 0.024) or ΔADC (p = 0.053) reduction had shorter CNS-specific PFS; further, those with a low ΔPeak (p = 0.012) or ΔIAUC₆₀ (p = 0.04) reduction had shorter OS compared with those with high reductions. In multivariate analyses, ΔK^trans and ΔPeak were independent prognostic factors for CNS-specific PFS and OS, respectively, after controlling for age, size, hormone receptors, and performance status.

CONCLUSIONS

Multiparametric MRI may help predict the survival outcomes in patients with BMBC.

KEY POINTS

• Decreased angiogenesis after chemotherapy on day 21 indicated good survival outcome. • ΔK ^trans was an independent prognostic factors for CNS-specific PFS. • ΔPeak was an independent prognostic factors for OS. • Multiparametric MRI helps clinicians to assess patients with BMBC. • High-risk patients may benefit from more intensive follow-up or treatment strategies.

Brain metastases: neuroimaging

Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

T-Cell Densities in Brain Metastases Are Associated with Patient Survival Times and Diffusion Tensor MRI Changes

Brain metastases are common and are usually detected by MRI. Diffusion tensor imaging (DTI) is a derivative MRI technique that can detect disruption of white matter tracts in the brain. We have matched preoperative DTI with image-guided sampling of the brain-tumor interface in 26 patients during resection of a brain metastasis and assessed mean diffusivity and fractional anisotropy (FA). The tissue samples were analyzed for vascularity, inflammatory cell infiltration, growth pattern, and tumor expression of proteins associated with growth or local invasion such as Ki67, S100A4, and MMP2, 9, and 13. A lower FA in the peritumoral region indicated more white matter tract disruption and independently predicted longer overall survival times (HR for death = 0.21; 95% confidence interval, 0.06-0.82; P = 0.024). Of all the biological markers studied, only increased density of CD3⁺ lymphocytes in the same region correlated with decreased FA (Mann-Whitney U, P = 0.037) as well as confounding completely the effect of FA on multivariate survival analyses. We conclude that the T-cell response to brain metastases is not a surrogate of local tumor invasion, primary cancer type, or aggressive phenotype and is associated with patient survival time regardless of these biological factors. Furthermore, it can be assayed by DTI, potentially offering a quick, noninvasive, clinically available method to detect an active immune microenvironment and, in principle, to measure susceptibility to immunotherapy.Significance: These findings show that white matter tract integrity is degraded in areas where T-cell infiltration is highest, providing a noninvasive method to identify immunologically active microenvironments in secondary brain tumors. Cancer Res; 78(3); 610-6. ©2017 AACR.

State-of-the-art considerations in small cell lung cancer brain metastases

BACKGROUND

Small cell lung cancer (SCLC) frequently leads to development of brain metastases. These unfortunately continue to be associated with short survival. Substantial advances have been made in our understanding of the underlying biology of disease. This understanding on the background of previously evaluated and currently utilized therapeutic treatments can help guide the next steps in investigations into this disease with the potential to influence future treatments.

DESIGN

A comprehensive review of the literature covering epidemiology, pathophysiology, imaging characteristics, prognosis, and therapeutic management of SCLC brain metastases was performed.

RESULTS

SCLC brain metastases continue to have a poor prognosis. Both unique aspects of SCLC brain metastases as well as features seen more universally across other solid tumor brain metastases are discussed. Systemic therapeutic studies and radiotherapeutic approaches are reviewed.

CONCLUSIONS

A clearer understanding of SCLC brain metastases will help lay the framework for studies which will hopefully translate into meaningful therapeutic options for these patients.

Use of diffusion-weighted MRI to modify radiosurgery planning in brain metastases may reduce local recurrence

Stereotactic radiosurgery (SRS) is an effective and well tolerated treatment for selected brain metastases; however, local recurrence still occurs. We investigated the use of diffusion weighted MRI (DWI) as an adjunct for SRS treatment planning in brain metastases. Seventeen consecutive patients undergoing complete surgical resection of a solitary brain metastasis underwent image analysis retrospectively. SRS treatment plans were generated based on standard 3D post-contrast T1-weighted sequences at 1.5T and then separately using apparent diffusion coefficient (ADC) maps in a blinded fashion. Control scans immediately post operation confirmed complete tumour resection. Treatment plans were compared to one another and with volume of local recurrence at progression quantitatively and qualitatively by calculating the conformity index (CI), the overlapping volume as a proportion of the total combined volume, where 1 = identical plans and 0 = no conformation whatsoever. Gross tumour volumes (GTVs) using ADC and post-contrast T1-weighted sequences were quantitatively the same (related samples Wilcoxon signed rank test = -0.45, p = 0.653) but showed differing conformations (CI 0.53, p < 0.001). The diffusion treatment volume (DTV) obtained by combining the two target volumes was significantly greater than the treatment volume based on post contrast T1-weighted MRI alone, both quantitatively (median 13.65 vs. 9.52 cm3, related samples Wilcoxon signed rank test p < 0.001) and qualitatively (CI 0.74, p = 0.001). This DTV covered a greater volume of subsequent tumour recurrence than the standard plan (median 3.53 cm3 vs. 3.84 cm3, p = 0.002). ADC maps may be a useful tool in addition to the standard post-contrast T1-weighted sequence used for SRS planning.

Exosomal levels of miRNA-21 from cerebrospinal fluids associated with poor prognosis and tumor recurrence of glioma patients

Glioma is a most common type of primary brain tumors. Extracellular vesicles, in the form of exosomes, are known to mediate cell–cell communication by transporting cell-derived proteins and nucleic acids, including various microRNAs (miRNAs). Here we examined the cerebrospinal fluid (CSF) from patients with recurrent glioma for the levels of cancer-related miRNAs, and evaluated the values for prognosis by comparing the measures of CSF-, serum-, and exosome-contained miR-21 levels. Samples from seventy glioma patients following surgery were compared with those from brain trauma patients as a non-tumor control group. Exosomal miR-21 levels in the CSF of glioma patients were found significantly higher than in the controls; whereas no difference was detected in serum-derived exosomal miR-21 expression. The CSF-derived exosomal miR-21 levels correlated with tumor spinal/ventricle metastasis and the recurrence with anatomical site preference. From additional 198 glioma tissue samples, we verified that miR-21 levels associated with tumor grade of diagnosis and negatively correlated with the median values of patient overall survival time. We further used a lentiviral inhibitor to suppress miR-21 expression in U251 cells. The results showed that the levels of miR-21 target genes of PTEN, RECK and PDCD4 were up-regulated at protein levels. Therefore, we concluded that the exosomal miR-21 levels could be demonstrated as a promising indicator for glioma diagnosis and prognosis, particularly with values to predict tumor recurrence or metastasis.

Comparison of diffusion-weighted imaging findings in brain metastases of different origin

Our purpose was to estimate apparent diffusion coefficient (ADC) values from brain metastases (BMs). Our patient sample included 159 patients with 948 BMs. Magnetic resonance imaging was obtained with a 1.5-T device. For diffusion-weighted imaging, a multislice single-shot echo-planar imaging sequence was used (b values of 0, 500, and 1000 s/mm(2)). The mean ADC value of BMs was 0.98 ± 0.32 × 10(-3) mm(2) s(-1). A total of 72.8% of BM lesions showed ADC values under 0.90 × 10(-3) mm(2) s(-1). Small-cell lung cancer had the lowest ADC values (0.86 ± 0.27) in comparison to BMs from non-small-cell lung cancer (1.17 ± 0.49), breast carcinoma (0.97 ± 0.21), and malignant melanoma (0.99 ± 0.36).

Unsanctifying the sanctuary: challenges and opportunities with brain metastases

While the use of targeted therapies, particularly radiosurgery, has broadened therapeutic options for CNS metastases, patients respond minimally and prognosis remains poor. The inability of many systemic chemotherapeutic agents to penetrate the blood-brain barrier (BBB) has limited their use and allowed brain metastases to become a burgeoning clinical challenge. Adequate preclinical models that appropriately mimic the metastatic process, the BBB, and blood-tumor barriers (BTB) are needed to better evaluate therapies that have the ability to enhance delivery through or penetrate into these barriers and to understand the mechanisms of resistance to therapy. The heterogeneity among and within different solid tumors and subtypes of solid tumors further adds to the difficulties in determining the most appropriate treatment approaches and methods of laboratory and clinical studies. This review article discusses therapies focused on prevention and treatment of CNS metastases, particularly regarding the BBB, and the challenges and opportunities these therapies present.