Preoperative diffusion-weighted imaging of single brain metastases correlates with patient survival times

Navigating Post-Operative Outcomes: A Comprehensive Reframing of an Original Graded Prognostic Assessment in Patients with Brain Metastases

BACKGROUND

Graded Prognostic Assessment (GPA) has been proposed for various brain metastases (BMs) tailored to the primary histology and molecular profiles. However, it does not consider whether patients have been operated on or not and does not include surgical outcomes as prognostic factors. The residual tumor burden (RTB) is a strong predictor of overall survival. We validated the GPA score and introduced "volumetric GPA" in the largest cohort of operated patients and further explored the role of RTB as an additional prognostic factor.

METHODS

A total of 630 patients with BMs between 2007 and 2020 were included. The four GPA components were analyzed. The validity of the original score was assessed using Cox regression, and a modified index incorporating RTB was developed by comparing the accuracy, sensitivity, specificity, F1-score, and AUC parameters.

RESULTS

GPA categories showed an association with survival: age (p < 0.001, hazard ratio (HR) 2.9, 95% confidence interval (CI) 2.5-3.3), Karnofsky performance status (KPS) (p < 0.001, HR 1.3, 95% CI 1.2-1.5), number of BMs (p = 0.019, HR 1.4, 95% CI 1.1-1.8), and the presence of extracranial manifestation (p < 0.001, HR 3, 95% CI 1.6-2.5). The median survival for GPA 0-1 was 4 months; for GPA 1.5-2, it was 12 months; for GPA 2.5-3, it was 21 months; and for GPA 3.5-4, it was 38 months (p < 0.001). RTB was identified as an independent prognostic factor. A cut-off of 2 cm3 was used for further analysis, which showed a median survival of 6 months (95% CI 4-8) vs. 13 months (95% CI 11-14, p < 0.001) for patients with RTB > 2 cm3 and <2 cm3, respectively. RTB was added as an additional component for a modified volumetric GPA score. The survival rates with the modified GPA score were: GPA 0-1: 4 months, GPA 1.5-2: 7 months, GPA 2.5-3: 18 months, and GPA 3.5-4: 34 months. Both scores showed good stratification, with the new score showed a trend towards better discrimination in patients with more favorable prognoses.

CONCLUSION

The prognostic value of the original GPA was confirmed in our cohort of patients who underwent surgery for BM. The RTB was identified as a parameter of high prognostic significance and was incorporated into an updated "volumetric GPA". This score provides a novel tool for prognosis and clinical decision making in patients undergoing surgery. This method may be useful for stratification and patient selection for further treatment and in future clinical trials.

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Background

Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.

Methods

Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.

Results

The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.

Conclusions

The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma

OBJECTIVE

This retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1-5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax.

METHOD

The study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model's performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values.

RESULTS

The random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson's r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively).

CONCLUSION

SSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.

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.

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.

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.

Prognostic factors in adult brainstem glioma: a tertiary care center analysis and review of the literature

Introduction

Adult brainstem gliomas (BSGs) are rare central nervous system tumours characterized by a highly heterogeneous clinical course. Median survival times range from 11 to 84 months. Beyond surgery, no treatment standard has been established. We investigated clinical and radiological data to assess prognostic features providing support for treatment decisions.

Methods

34 BSG patients treated between 2000 and 2019 and aged ≥ 18 years at the time of diagnosis were retrospectively identified from the databases of the two largest Austrian Neuro-Oncology centres. Clinical data including baseline characteristics, clinical disease course, applied therapies, the outcome as well as neuroradiological and neuropathological findings were gathered and analysed. The tumour apparent diffusion coefficient (ADC), volumetry of contrast-enhancing and non-contrast-enhancing lesions were determined on magnetic resonance imaging scans performed at diagnosis.

Results

The median age at diagnosis was 38.5 years (range 18–71 years). Tumour progression occurred in 26/34 (76.5%) patients after a median follow up time of 19 months (range 0.9–236.2). Median overall survival (OS) and progression-free survival (PFS) was 24.1 months (range 0.9–236.2; 95% CI 18.1–30.1) and 14.5 months (range 0.7–178.5; 95% CI 5.1–23.9), respectively. Low-performance status, high body mass index (BMI) at diagnosis and WHO grading were associated with shorter PFS and OS at univariate analysis (p < 0.05, log rank test, respectively). ADC values below the median were significantly associated with shorter OS (14.9 vs 44.2 months, p = 0.018).

Conclusion

ECOG, BMI, WHO grade and ADC values were associated with the survival prognosis of BSG patients and should be included in the prognostic assessment.

MRI Texture Analysis for the Prediction of Stereotactic Radiosurgery Outcomes in Brain Metastases from Lung Cancer

This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell’s concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.

Breast Cancer Brain Metastasis: The Potential Role of MRI Beyond Current Clinical Applications

PURPOSE

Breast cancer brain metastasis (BCBM) represents a major clinical challenge. Can MRI help in advancements in the management of BCBM? This review discusses MRI developments and the corresponding potential advancements in BCBM management.

METHODS

An exhaustive literature search was undertaken to identify studies which look into the potential of MRI in BCBM management. Seven hundred and eighty-four studies published from September 1984 to May 2020 were identified. Three topics are covered where MRI is not clinically established yet: 1) the prognosis of BCBM; 2) the screening of BC patients for BCBM development, and 3) the assessment of imaging features correlated to BC subtype.

RESULTS

Thirty-six studies were considered eligible for the purposes of this review. On-going progress is made with the identification of different BCBM characteristics and MRI metrics that might be related to prognosis. Progress has been made with the identification of different BCBM characteristics, including BCBM location, degree of edema, white matter disruption, tumor edge sharpness, and temporal muscle thickness. A more accurate prediction of prognosis could lead to more suitable patient management and treatment. The use of MRI in BCBM screening of the high-risk breast cancer population remains a controversial subject. To date, there are no results from clinical trials; however, there is a rising number of relatively small studies that show concern on this subject and support BCBM screening. It is important to oncologists to be able to assess the tumor subtype non-invasively. MRI features, which have shown some correlation with subtype, include the number of tumors, location, and their distribution in the brain. Advanced tools and metrics have been produced to carry out radiological characteristics analysis on MRI images. Assessing MRI features in more detail could provide a more personalized management of patients.

CONCLUSION

Developments in the use of MRI have the potential to improve BCBM management.

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.

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.

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.

Evaluation of intracranial tuberculomas using diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and susceptibility weighted imaging (SWI)

OBJECTIVE:

The present study was aimed to evaluate patients of suspected intracranial tuberculomas with diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and susceptibility-weighted imaging (SWI).

METHODS:

The present study evaluated 116 patients known or suspected of having central nervous system tuberculosis with advanced MRI techniques comprising of DWI, MRS and SWI in addition to the conventional MRI.

RESULTS:

Apparent diffusion coefficient value of tuberculomas was not significantly different (p > 0.05) from apparent diffusion coefficient value of metastatic lesions and high-grade gliomas. MRS revealed that NAA/Cr and NAA/Cho ratios of tuberculomas were not significantly different (p > 0.05) from that of malignant brain lesions. However, Cho/Cr ratio of tuberculomas (1.36 ± 0.41) was significantly lower from that of malignant brain lesions (2.63 ± 0.99). SWI revealed a complete and regular hypointense peripheral ring in 42 cases of tuberculomas (58%) and in none of the malignant brain lesions.

CONCLUSION:

DWI offers no clear advantage in differentiating tuberculomas from metastasis and gliomas. Tuberculomas may be differentiated from metastases and gliomas by their unique metabolite pattern on MRS. Presence of a complete and regular peripheral hypointense ring in SWI favors the diagnosis of tuberculomas.

ADVANCES IN KNOWLEDGE:

The results from the present study suggest promising role of SWI in the discrimination of tuberculomas from metastatic brain lesions and gliomas with the presence of a complete and regular peripheral hypointense ring favoring the diagnosis of tuberculomas.

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.

Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean

Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified. MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients. The pooled correlation coefficient between ADC_mean and KI 67 for all included tumors was ρ = -0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = -0.62, urothelial carcinomas: ρ = -0.56, cerebral lymphoma: ρ = -0.55, neuroendocrine tumors: ρ = -0.52, glioma: ρ = -0.51, lung cancer: ρ = -0.50, prostatic cancer: ρ = -0.43, rectal cancer: ρ = -0.42, pituitary adenoma:ρ = -0.44, meningioma, ρ = -0.43, hepatocellular carcinoma: ρ = -0.37, breast cancer: ρ = -0.22.

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.

Descriptive statistical analysis of a real life cohort of 2419 patients with brain metastases of solid cancers

Aim

We provide a descriptive statistical analysis of baseline characteristics and the clinical course of a large real-life cohort of brain metastases (BM) patients.

Methods

We performed a retrospective chart review for patients treated for BM of solid cancers at the Medical University of Vienna between 1990 and 2011.

Results

We identified a total of 2419 BM patients (50.5% male, 49.5% female, median age 59 years). The primary tumour was lung cancer in 43.2%, breast cancer in 15.7%, melanoma in 16.4%, renal cell carcinoma in 9.1%, colorectal cancer in 9.3% and unknown in 1.4% of cases. Rare tumour types associated with BM included genitourinary cancers (4.1%), sarcomas (0.7%). gastro-oesophageal cancer (0.6%) and head and neck cancers (0.2%). 48.7% of patients presented with a singular BM, 27.7% with 2–3 and 23.5% with >3 BM. Time from primary tumour to BM diagnosis was shortest in lung cancer (median 11 months; range 1–162) and longest in breast cancer (median 44 months; 1–443; p<0.001). Multiple BM were most frequent in breast cancer (30.6%) and least frequent in colorectal cancer (8.5%; p<0.001). Patients with breast cancer had the longest median overall survival times (8 months), followed by patients with lung cancer (7 months), renal cell carcinoma (7 months), melanoma (5 months) and colorectal cancer (4 months; p<0.001; log rank test). Recursive partitioning analysis and graded prognostic assessment scores showed significant correlation with overall survival (both p<0.001, log rank test). Evaluation of the disease status in the past 2 months prior to patient death showed intracranial progression in 35.9%, extracranial progression in 27.5% and combined extracranial and intracranial progression in 36.6% of patients.

Conclusions

Our data highlight the heterogeneity in presentation and clinical course of BM patients in the everyday clinical setting and may be useful for rational planning of clinical studies.

The future of targeted therapies for brain metastases

Brain metastases (BM) are an increasing challenge in the management of patients with advanced cancer. Treatment options for BM are limited and mainly focus on the application of local therapies. Systemic therapies including targeted therapies are only poorly investigated, as patients with BM were frequently excluded from clinical trials. Several targeted therapies have shown promising activity in patients with BM. In the present review we discuss existing and emerging targeted therapies for the most frequent BM primary tumor types. We focus on challenges in the conduction of clinical trials on targeted therapies in BM patients such as patient selection, combination with radiotherapy, the obstacles of the blood–brain barrier and the definition of study end points.

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).

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

Background

Diffusion-weighted MRI (DWI) has been used in neurosurgical practice mainly to distinguish cerebral metastases from abscess and glioma. There is evidence from other solid organ cancers and metastases that DWI may be used as a biomarker of prognosis and treatment response. We therefore investigated DWI characteristics of cerebral metastases and their peritumoral region recorded pre-operatively and related these to patient outcomes.

Methods

Retrospective analysis of 76 cases operated upon at a single institution with DWI performed pre-operatively at 1.5T. Maps of apparent diffusion coefficient (ADC) were generated using standard protocols. Readings were taken from the tumor, peritumoral region and across the brain-tumor interface. Patient outcomes were overall survival and time to local recurrence.

Results

A minimum ADC greater than 919.4 × 10^-6 mm²/s within a metastasis predicted longer overall survival regardless of adjuvant therapies. This was not simply due to differences between the types of primary cancer because the effect was observed even in a subgroup of 36 patients with the same primary, non-small cell lung cancer. The change in diffusion across the tumor border and into peritumoral brain was measured by the “ADC transition coefficient” or ATC and this was more strongly predictive than ADC readings alone. Metastases with a sharp change in diffusion across their border (ATC >0.279) showed shorter overall survival compared to those with a more diffuse edge. The ATC was the only imaging measurement which independently predicted overall survival in multivariate analysis (hazard ratio 0.54, 95% CI 0.3 – 0.97, p = 0.04).

Conclusions

DWI demonstrates changes in the tumor, across the tumor edge and in the peritumoral region which may not be visible on conventional MRI and this may be useful in predicting patient outcomes for operated cerebral metastases.

Application of diffusion-weighted magnetic resonance imaging to predict the intracranial metastatic tumor response to gamma knife radiosurgery

To evaluate the effect of stereotactic radiosurgery (SRS) on intracranial metastases with diffusion-weighted imaging/apparent diffusion coefficient maps. A total of 107 patients with 144 metastases larger than 1 cm in diameter were retrospectively reviewed. We calculated the DWI(Tumor/white matter) ratios (DWI(T/WM) ratio) between the metastases and the normal, contralateral frontal white matter at each time point. We also recorded the ADC values for metastases (ADCT values). The DWI(T/WM) ratio and ADCT values were assessed for correlation with the patients' tumor response, brain edema, and survival. A decrease in DWI(T/WM) ratios was seen in the controlled metastases, and an increase in the DWI(T/WM) ratio were seen in the metastases with poor tumor control. On the other hand, an increase in ADCT values was seen in the controlled metastases, and a decrease in ADCT values was seen in the metastases with poor control. The differences were significant (p value: 0.001 and 0.002, respectively). Sensitivity of a decrease in the DWI(T/WM) ratio to make an early prediction of tumor control was 83.9%, and specificity was 88.5%. When using the initial ADCT values of metastases to predict tumor response, sensitivity and specificity were 85.5 and 72.7%, respectively. DWI/ADC is a practical method for studying the efficacy of SRS and predicting early metastases response progression. A decrease signal on DWI and increased ADC values are indicators of good tumor control, and reflect the beneficial effect of SRS.

The role of magnetic resonance imaging in the management of brain metastases: diagnosis to prognosis

This article reviews the different MRI techniques available for the diagnosis, treatment and monitoring of brain metastases with a focus on applying advanced MR techniques to practical clinical problems. Topics include conventional MRI sequences and contrast agents, functional MR imaging, diffusion weighted MR, MR spectroscopy and perfusion MR. The role of radiographic biomarkers is discussed as well as future directions such as molecular imaging and MR guided high frequency ultrasound.

The reliability of routine clinical post-processing software in assessing potential diffusion-weighted MRI "biomarkers" in brain metastases

BACKGROUND AND PURPOSE

Diffusion MRI characteristics have been used as biomarkers to guide prognosis in cerebral pathologies including brain metastases. The measurement of ADC is often described poorly in clinical and research studies with little detail given to the practical considerations of where to place ROIs, which post processing software package to use and how reproducible the resulting metrics will be.

METHOD

We investigated a series of 12 patients with brain metastases and preoperative DWI. Three post processing platforms were used. ROI were placed over the tumour, peritumoural region and across the brain-tumour interface. These recordings were made by a neurosurgeon and a neuroradiologist. Inter-intra-observer variability was assessed using Bland-Altman analysis. An exploratory analysis of DWI with overall survival and tumour type was made.

RESULTS

There was excellent correlation between the software packages used for all measures including assessing the whole tumour, selective regions with lowest ADC, the change of ADC across the brain-tumour interface and the relation of the tumour ADC to peritumoural regions and the normal white matter. There was no significant inter- or intra-observer variability for repeated readings. There were significant differences in the mean values obtained using different methodologies and different metrics had differing relationships to overall survival and primary tumour of origin.

CONCLUSION

Diffusion weighted MRI metrics offer promise as potential non-invasive biomarkers in brain metastases and a variety of metrics have been shown to be reliably measured using differing platforms and observers.