Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

OBJECTIVES: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.

OBJECTIVES: Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development. METHODS: In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation. RESULTS: All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748-0.975) in internal validation, and 0.780 (95% CI: 0.673-0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration. CONCLUSION: This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment. CLINICAL RELEVANCE STATEMENT: The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans. KEY POINTS: • Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance. • The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951-0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans. • Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.

Background: Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex. Methods: In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced. Results: The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019). Conclusions: The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

Dose-staged Gamma Knife radiosurgery for meningiomas: A retrospective study in a single center.

Objective: This study aimed to study the efficiency and safety of a dose-staged Gamma Knife radiosurgery strategy for large meningiomas or meningiomas close to important nerve structures. Methods: This study evaluates the outcome of a prospectively accrued series of 71 consecutive patients with meningiomas treated with staged dose-fractionated Gamma Knife radiosurgery. The average peripheral doses for the first and second fractions were 9.0 ± 0.9 Gy (8-12 Gy) and 8.6 ± 0.7 Gy (range, 7-10 Gy), respectively. The interval between fractions was 6.1 ± 1.9 months (range, 3-12 months). The median follow-up time was 36 months (12-96 months). Results: During the follow-up period after the second fraction, 97.2% achieved tumor control in our series. A total of 2 patients exhibited local recurrence at 30 and 60 months after the second fraction, respectively. No treatment-related complications or new long-term neurological dysfunctions were reported. MRIs observed slightly or moderately increased peritumoral edema in six patients, but no specific neurological complaints are attributed to this finding. Conclusion: This study investigates the efficiency and safety of dose-staged Gamma Knife radiosurgery as an alternative option for meningiomas that were large in volume, adjacent to crucial structures, or in patients with contraindications to craniotomy.

Analysis of Key Clinical Variables and Radiological Manifestations Associated with the Treatment Response of Patients with Brain Metastases to Stereotactic Radiosurgery.

BACKGROUND: Stereotactic radiosurgery (SRS) is considered a promising treatment for brain metastases (BM) with better healing efficacy, relatively faster treatment time, and lower neurotoxicity, which can achieve local control rates above 70%. Although SRS improves the local control of BM, this may not translate into improvements in survival time. Thus, screening out the key factors influencing the treatment response to SRS, instead of the survival time following SRS, might be of more significance. This may assist doctors when making adjustments to treatment strategies for patients with BM. METHODS: This is a retrospective review of 696 patients with BM who were treated with SRS at Huashan Hospital, Fudan University between June 2015 and February 2020. According to the patients' treatment response to SRS, the patients were divided into an improved group (IG) and a progressive group (PG). The clinical data and magnetic resonance imaging (MRI) performed pre- and post-treatment were collected for the two groups. Five clinical variables (gender, age, Karnofsky performance status (KPS), primary tumor type, and extracranial lesion control) and seven radiological manifestations (location, number, volume, maximum diameter, edema index (EI), diffusion weighted imaging (DWI) sequence signal, and enhanced pattern) were selected and compared. A stepwise regression analysis was performed in order to obtain the best prediction effect of a group of variables and their regression coefficients, and finally to build an SRS treatment response scoring model based on the coefficients. The performance of the model was evaluated by calculating the AUC and performing the Hosmer-Lemeshow test. RESULTS: A total of 323 patients were enrolled in the study based on the inclusion and exclusion criteria, including 209 patients in the IG and 114 patients in the PG. In the Chi-square test and t-test analysis, the significant p values of KPS, extracranial lesion control, volume, and EI were less than 0.05. Moreover, the cut-off values for volume and EI were 1801.145 mm3 and 3.835, respectively. The scoring model that was based on multivariate logistic regression coefficients performed better, achieving AUCs of 0.755 ± 0.062 and 0.780 ± 0.061 for the internal validation and validation cohorts, with p values of 0.168 and 0.073 for the Hosmer-Lemeshow test. CONCLUSIONS: KPS, extracranial lesion control, tumor volume, and EI had a certain correlation with the treatment response to SRS. Scoring models that are based on these variables can accurately predict the treatment response of patients with BM to SRS, thereby assisting doctors to make an appropriate first treatment strategy for patients with BM to a certain degree.

Brain network analyses of diffusion tensor imaging for brain aging.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery.

BACKGROUND: Gamma Knife radiosurgery (GKS) was recommended for treating patients with breast cancer brain metastasis (BCBM), but predictions of the existing prognostic models for therapeutic responsiveness vary substantially. PURPOSE: To investigate the prognostic value of pretreatment clinical, MRI radiologic, and texture features in patients with BCBM undergoing GKS. MATERIAL AND METHODS: The data of 81 BCBMs in 44 patients were retrospectively reviewed. Progressive disease was defined as an increase of at least 20% in the longest diameter of the target lesion or the presence of new intracranial lesions on contrast-enhanced T1-weighted (CE-T1W) imaging. Radiomic features were extracted from pretreatment CE-T1W images, T2-weighted (T2W) images, and ADC maps. Cox proportional hazard analyses were performed to identify independent predictors associated with BCBM-specific progression-free survival (PFS). A nomogram was constructed and its calibration ability was assessed. RESULTS: The cumulative BCBM-specific PFS was 52.27% at six months and 11.36% at one year, respectively. Age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.01-1.06; P = 0.004) and CE-T1W-based kurtosis (HR 0.72; 95% CI 0.57-0.92; P = 0.008) were the independent predictors. The combination of CE-T1W-based kurtosis and age displayed a higher C-index (C-index 0.70; 95% CI 0.63-0.77) than did CE-T1W-based kurtosis (C-index 0.65; 95% CI 0.57-0.73) or age (C-index 0.63; 95% CI 0.56-0.70) alone. The nomogram based on the combinative model provided a better performance over age (P < 0.05). The calibration curves elucidated good agreement between prediction and observation for the probability of 7- and 12-month BCBM-specific PFS. CONCLUSION: Pretreatment CE-T1W-based kurtosis combined with age could improve prognostic ability in patients with BCBM undergoing GKS.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors.

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

A new classification and clinical results of Gamma Knife radiosurgery for cavernous sinus hemangiomas: a report of 53 cases.

BACKGROUND: Cavernous sinus hemangiomas (CaSHs) are rare vascular lesions in the cavernous sinus(CS). Gamma Knife radiosurgery (GKS) provides a treatment modality alternative to microsurgery. This study was conducted to describe a new classification of CaSHs based on their magnetic resonance (MR) imaging findings and determine the efficacy and safety of GKS in a large series of CaSH patients. METHODS: From April 2007 to November 2012, 53 patients harboring CaSHs were treated using Leksell Gamma Knife model C (before April 2012) or Perfexion (from May 2012 ) at the Gamma Knife Center of Huashan Hospital. Of the 53 patients, 15 with definitive histopathologic diagnoses after surgery, 38 were diagnosed based on their MR imaging findings. There were 15 male and 38 female patients with a mean age of 52 (range, 25-76) years old. The characteristics of MR images of CaSHs were their extremely high homogeneous intensity on T2-weighted and FLAIR images: as bright as cerebrospinal fluid signal. According to the relationship between the carotid line and their location, CaSHs were classified into three types: the intrasellar, parasellar and mixed type. The mean volume of the tumors was 13.2 ± 8.2 cm(3) (range, 1-41 cm(3)). A mean marginal dose of 13.3 Gy (range, 8-15 Gy) was directed to the 49%-64% isodose line (mean 53%). RESULTS: Of the 53 tumors, 6 (11%) were classified as intrasellar type. Eight (15%) were parasellar type and the other 39 cases(74%) mixed type. The mean radiological and clinical follow-up time of this study was 24 (range, 2-67 months) and 34 months (range, 2-73 months), respectively. The tumor control rate was 100%. The mean tumor volume reduction was 79.5% (range, 16.5%-100%) compared with the pre-GKS volume. Six months after GKS, MR imaging revealed an average of 60.2% tumor volume reduction (range, 16.5%-89.2%). Twenty-nine cases (55%) showed a >80% tumor volume decrease. Neurologically, only two of these patients showed clinical deterioration, and 33 patients demonstrated an obvious improvement in ocular or endocrine disorders. At last follow-up, there were no more complications related to GKS, and none of the tumors progressed. CONCLUSIONS: Our study showed that GKS is a useful and safe therapeutic method for CaSHs as both a primary and adjuvant treatment. The new classification of CaSHs may help predict their clinical course during tumor development and treatment response after GKS. Further studies with long-term follow-up and larger numbers of cases are necessary to optimize the treatment conditions and verify the benefit of this treatment.

Effect of increasing diffusion gradient direction number on diffusion tensor imaging fiber tracking in the human brain.

OBJECTIVE: To assess the effects of varying the number of diffusion gradient directions (NDGDs) on diffusion tensor fiber tracking (FT) in human brain white matter using tract characteristics. MATERIALS AND METHODS: Twelve normal volunteers underwent diffusion tensor imaging (DTI) scanning with NDGDs of 6, 11, 15, 21, and 31 orientations. Three fiber tract groups, including the splenium of the corpus callosum (CC), the entire CC, and the full brain tract, were reconstructed by deterministic DTI-FT. Tract architecture was first qualitatively evaluated by visual observation. Six quantitative tract characteristics, including the number of fibers (NF), average length (AL), fractional anisotropy (FA), relative anisotropy (RA), mean diffusivity (MD), and volume ratio (VR) were measured for the splenium of the CC at the tract branch level, for the entire CC at tract level, and for the full brain tract at the whole brain level. Visual results and those of NF, AL, FA, RA, MD, and VR were compared among the five different NDGDs. RESULTS: The DTI-FT with NDGD of 11, 15, 21, and 31 orientations gave better tracking results compared with NDGD of 6 after the visual evaluation. NF, FA, RA, MD, and VR values with NDGD of six were significantly greater (smallest p = 0.001 to largest p = 0.042) than those with four other NDGDs (11, 15, 21, or 31 orientations), whereas AL measured with NDGD of six was significantly smaller (smallest p = 0.001 to largest p = 0.041) than with four other NDGDs (11, 15, 21, or 31 orientations). No significant differences were observed in the results among the four NDGD groups of 11, 15, 21, and 31 directions (smallest p = 0.059 to largest p = 1.000). CONCLUSION: The main fiber tracts were detected with NDGD of six orientations; however, the use of larger NDGD (≥ 11 orientations) could provide improved tract characteristics at the expense of longer scanning time.