De novo versus recurrent metastatic breast cancer affects the extent of brain metastases.

PURPOSE: We aimed to identify factors associated with the extent of brain metastases in patients with breast cancer to help distinguish brain oligometastases (1-4 brain metastases) from extensive metastases (5 or more brain metastases). METHODS: This retrospective observational study included 100 female patients diagnosed with brain metastases from breast cancer at a single institution between January 2011 and April 2022. Patient demographics and tumor characteristics were compared between the brain oligometastases group and the extensive metastases group. Multivariable logistic regression analysis was performed to determine the independent factors, including age at initial diagnosis, initial stage, breast cancer subtype, detection time of brain metastases, and de novo or recurrent status of the metastatic disease. In a subgroup analysis of patients with brain oligometastases, demographic and tumor characteristics were compared between patients with single and two-four brain metastases. RESULTS: Of the 100 patients, 56 had brain oligometastases, while 44 had extensive brain metastases. The multivariable logistic regression analysis revealed that only the de novo/recurrent status of metastatic breast cancer was significantly associated with the extent of brain metastasis (p = 0.023). In the subgroup analysis of 56 patients with brain oligometastases, those diagnosed at an earlier stage were more likely to have a single brain metastasis (p = 0.008). CONCLUSION: Patients with de novo metastatic breast cancer are more likely to develop extensive brain metastases than those with recurrent metastatic breast cancer. This insight could influence the development of tailored approaches for monitoring and treating brain metastases, supporting the potential advantages of routine brain screening for patients newly diagnosed with stage IV breast cancer.

Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis.

BACKGROUND AND PURPOSE: To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis. MATERIALS AND METHODS: The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted. Three conventional machine learning classifiers, including least absolute shrinkage and selection operator (LASSO), adaptive boosting (Adaboost), and support vector machine with the linear kernel (SVC), combined with one of four feature selection methods, including forward sequential feature selection, F score, mutual information, and LASSO, were trained. Additionally, one ensemble classifier based on the three classifiers was used. The diagnostic performance of the optimized models was tested in the test set using the accuracy, F1-macro score, and the area under the receiver operating characteristic curve (AUCROC). RESULTS: The best performance was achieved when the LASSO was used as a feature selection method. In the test set, the best performance was achieved by the ensemble classifier, showing an accuracy of 76.3% (95% CI, 70.0-82.7), a F1-macro score of 0.704, and an AUCROC of 0.878. CONCLUSION: Our fully automated radiomics-based models for multiclass classification might be useful for differential diagnosis of a single enhancing brain tumor with a good diagnostic performance and generalizability.

Salvage multiple burr hole surgery in patients with Moyamoya disease: efficacy evaluation using probabilistic independent component analysis of dynamic susceptibility contrast perfusion MRI.

PURPOSE: Multiple burr hole surgery is considered to be an option for achieving indirect revascularization in patients with ischemic Moyamoya disease (MMD). We aimed to investigate the efficacy of stand-alone multiple burr hole surgery for salvage revascularization in patients with MMD by assessing the hemodynamic changes via normalized time-to-peak (nTTP) analysis and independent component analysis (ICA) of preoperative and postoperative dynamic susceptibility contrast (DSC) perfusion MRI data. METHODS: The DSC perfusion MRI data of 25 hemispheres from 21 patients with MMD, who underwent multiple burr hole surgery for salvage revascularization due to persistent or recurrent symptoms after primary revascularization with modified encephaloduroarteriosynangiosis (mEDAS), were analyzed. The nTTP, which was measured using the region of interests covering the entire surgical hemisphere, was compared between the preoperative and postoperative images. ICA was used to compare the relative arterial and venous components of the surgical hemispheres between the respective preoperative and postoperative images. RESULTS: The median postoperative nTTP (1.80 s) was significantly shorter than the median preoperative nTTP (4.10 s) (P < 0.001). The postoperative relative arterial component of the surgical hemisphere (median: 0.04) was significantly higher than the preoperative relative arterial component (median: - 0.02, P < 0.001). In contrast, the postoperative relative venous component of the surgical hemisphere (median: - 0.05) was significantly lower than the preoperative value (median: 0.05, P < 0.001). CONCLUSION: The improvement in cerebral perfusion parameters observed on postoperative DSC perfusion MRI demonstrated that stand-alone multiple burr hole surgery could be a favorable salvage revascularization technique after mEDAS failure in patients with ischemic MMD.

A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance.

OBJECTIVES: To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. METHODS: In a retrospective and multicenter study, MR images with aneurysms based on radiological reports were extracted. The examinations were randomly divided into two data sets: training set of 468 examinations and internal test set of 120 examinations. Additionally, 50 examinations without aneurysms were randomly selected and added to the internal test set. External test data set consisted of 56 examinations with intracranial aneurysms and 50 examinations without aneurysms, which were extracted based on radiological reports from a different institution. After manual ground truth segmentation of aneurysms, a deep learning algorithm based on 3D ResNet architecture was established with the training set. Its sensitivity, positive predictive value, and specificity were evaluated in the internal and external test sets. RESULTS: MR images included 551 aneurysms (mean diameter, 4.17 ± 2.49 mm) in the training, 147 aneurysms (mean diameter, 3.98 ± 2.11 mm) in the internal test, 63 aneurysms (mean diameter, 3.23 ± 1.69 mm) in the external test sets. The sensitivity, the positive predictive value, and the specificity were 87.1%, 92.8%, and 92.0% for the internal test set and 85.7%, 91.5%, and 98.0% for the external test set, respectively. CONCLUSION: A deep learning algorithm detected intracranial aneurysms with a high diagnostic performance which was validated using external data set. KEY POINTS: • A deep learning-based algorithm for the automated diagnosis of intracranial aneurysms demonstrated a high sensitivity, positive predictive value, and specificity. • The high diagnostic performance of the algorithm was validated using external test data set from a different institution with a different scanner. • The algorithm might be robust and effective for general use in real clinical settings.

Amide proton transfer imaging might predict survival and IDH mutation status in high-grade glioma.

OBJECTIVES: To assess the utility of amide proton transfer (APT) imaging as an imaging biomarker to predict prognosis and molecular marker status in high-grade glioma (HGG, WHO grade III/IV). METHODS: We included 71 patients with pathologically diagnosed HGG who underwent preoperative MRI with APT imaging. Overall survival (OS) and progression-free survival (PFS) according to APT signal, clinical factors, MGMT methylation status, and IDH mutation status were analyzed. Multivariate Cox regression models with and without APT signal data were constructed. Model performance was compared using the integrated AUC (iAUC). Associations between APT signals and molecular markers were assessed using the Mann-Whitney test. RESULTS: High APT signal was a significant predictor for poor OS (HR = 3.21, 95% CI = 1.62-6.34) and PFS (HR = 2.22, 95% CI = 1.33-3.72) on univariate analysis. On multivariate analysis, high APT signals were an independent predictor of poor OS and PFS when clinical factors alone (OS: HR = 2.89; PFS: HR = 2.13), or in combination with molecular markers (OS: HR = 2.85; PFS: HR = 2.00), were included as covariates. The incremental prognostic value of APT signals was significant for OS and PFS. IDH-wild type was significantly associated with high APT signals (p = 0.001) when compared to IDH-mutant; however, there was no difference based on MGMT methylation status (p = 0.208). CONCLUSION: High APT signal was a significant predictor of poor prognosis in HGG. APT data showed significant incremental prognostic value over clinical prognostic factors and molecular markers and may also predict IDH mutation status. KEY POINTS: • Amide proton transfer (APT) imaging is a promising prognostic marker of high-grade glioma. • APT signals were significantly higher in IDH-wild type compared to IDH-mutant high-grade glioma. • APT imaging may be valuable for preoperative screening and treatment guidance.

Amide proton transfer imaging for differentiation of benign and atypical meningiomas.

PURPOSE: To investigate the difference in amide proton transfer (APT)-weighted signals between benign and atypical meningiomas and determine the value of APT imaging for differentiating the two. METHODS: Fifty-seven patients with pathologically diagnosed meningiomas (benign, 44; atypical, 13), who underwent preoperative MRI with APT imaging between December 2014 and August 2016 were included. We compared normalised magnetisation transfer ratio asymmetry (nMTR asym ) values between benign and atypical meningiomas on APT-weighted images. Conventional MRI features were qualitatively assessed. Both imaging features were evaluated by multivariable logistic regression analysis. The discriminative value of MRI with and without nMTR asym was evaluated. RESULTS: The nMTR asym of atypical meningiomas was significantly greater than that of benign meningiomas (2.46% vs. 1.67%; P < 0.001). In conventional MR images, benign and atypical meningiomas exhibited significant differences in maximum tumour diameter, non-skull base location, and heterogeneous enhancement. On multivariable logistic regression analysis, high nMTR asym was an independent predictor of atypical meningiomas (adjusted OR, 11.227; P = 0.014). The diagnostic performance of MRI improved with nMTR asym for predicting atypical meningiomas. CONCLUSION: Atypical meningiomas exhibited significantly higher APT-weighted signal intensities than benign meningiomas. The discriminative value of conventional MRI improved significantly when combined with APT imaging for diagnosis of atypical meningioma. KEY POINTS: • APT imaging is useful for differentiating between atypical and benign meningiomas. • Atypical meningiomas exhibited high APT-weighted signal intensity than benign meningiomas. • The diagnostic performance of MRI improved with nMTR asym for predicting atypical meningiomas.

Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery.

PURPOSE: To investigate whether steep Trendelenburg in a major urologic surgery is associated with postoperative delirium, and to examine other potential clinical and radiologic factors predictive of postoperative delirium. METHODS: 182 patients who received a major urologic surgery and underwent a 3.0-T brain MRI scan within 1 year prior to the date of surgery were retrospectively enrolled. Preoperative brain MRIs were used to analyze features related to small vessel disease burden and mesial temporal atrophy. Presence of a significant mesial temporal atrophy was defined as Scheltens' scale ≥ 2. Patients' clinico-demographic data and MRI features were used to identify significant predictors of postoperative delirium using the logistic regression analysis. Independent predictors found significant in the univariate analysis were further evaluated in the multivariate analysis. RESULTS: Incidence of postoperative delirium was 6.0%. Patients with postoperative delirium had lower body mass index (21.3 vs. 25.0 kg/m2, P = 0.003), prolonged duration of anesthesia (362.7 vs. 224.7 min, P < 0.001) and surgery (302.2 vs. 174.5 min, P < 0.001), and had more significant mesial temporal atrophy (64% vs. 30%, P = 0.046). In the univariate analysis, female sex, type of surgery (radical prostatectomy over cystectomy), prolonged duration of anesthesia (≥ 6 h), and presence of a significant mesial temporal atrophy were significant predictors (all P-values < 0.050), but only the presence of significant mesial temporal atrophy was significant in the multivariate analysis [odds ratio (OR), 3.69; 95% CI 0.99-13.75; P = 0.046]. CONCLUSION: Steep Trendelenburg was not associated with postoperative delirium. Significant mesial temporal atrophy (Scheltens' scale ≥ 2) in preoperative brain MRI was predictive of postoperative delirium. TRIAL REGISTRATION: Not applicable.

Diagnostic Performance of Pointwise Encoding Time Reduction with Radial Acquisition Subtraction-based MR Angiography in the Follow-up of Intracranial Aneurysms after Clipping.

PURPOSE: While follow-up assessment of clipped aneurysms (CAs) using magnetic resonance angiography (MRA) can be challenging due to susceptibility artifacts, a novel MRA sequence pointwise encoding time reduction with radial acquisition (PETRA) subtraction-based MRA, has been developed to reduce these artifacts. The aim of the study was to validate the diagnostic performance of PETRA-MRA by comparing it with digital subtraction angiography (DSA) as a reference for follow-up of CAs using a 3T MR scanner. METHODS: Patients with clipping who underwent both PETRA-MRA and DSA between September 2019 and December 2021 were retrospectively included. Two neuroradiologists independently reviewed with the reconstructed images of PETRA-MRA to assess the visibility of the arteries around the clips and aneurysm recurrence or remnants of CA using a 3-point scale. The diagnostic accuracy of PETRA-MRA was evaluated in comparison to DSA. RESULTS: The study included 34 patients (28 females, mean age 59 ± 9.6 years) with 48 CAs. The PETRA-MRA allowed visualization of the parent vessels around the clips in 98% of cases, compared to 39% with time-of-flight (TOF) MRA (p < 0.0001). The DSA confirmed 14 (29.2%) residual or recurrent aneurysms. The PETRA-MRA demonstrated a high accuracy, specificity, positive predictive value, and negative predictive value of 99.2%, 100%, 100%, and 97.8%, respectively, while the sensitivity was 66.7%. CONCLUSION: This retrospective study demonstrates that PETRA-MRA provides excellent visibility of adjacent vessels near clips and has a high diagnostic accuracy in detecting aneurysm remnants or recurrences in CAs. Further prospective studies are warranted to establish its utility as a reliable alternative for follow-up after clipping.

Dural Metastasis in Breast Cancer: MRI-Based Morphological Subtypes and Their Clinical Implications.

Purpose: To investigate the clinical factors associated with breast cancer (BRCA) dural metastases (DMs), their impact on prognosis compared to brain parenchymal metastases (BPMs) alone, and differences between DM subtypes, aiming to inform clinical decisions. Materials and Methods: We retrospectively analyzed 119 patients with BRCA with brain metastasis, including 91 patients with BPM alone and 28 patients with DM. Univariate and multivariate analyses were performed to compare the clinical characteristics between the two groups and within subtypes of DM. Overall survival after DM (OSDM) and the interval from DM to leptomeningeal carcinomatosis (LMC) were compared using Kaplan-Meier analysis. Results: DM was notably linked with extracranial metastasis, luminal-like BRCA subtype (p=0.033), and skull metastases (p<0.001). Multiple logistic regression revealed a strong association of DM with extracranial and skull metastases, but not with subtype or hormone receptor (HR) status. Patients with DM did not show survival differences compared with patients with BPM alone. In the subgroup analysis, nodular type DM correlated with HER2 status (p=0.044), whereas diffuse type DM was significantly associated with a higher prevalence of the luminal-like subtype (p=0.048) and the presence of skull metastasis (p=0.002). Patients with diffuse DM did not exhibit a significant difference in OSDM but had a notably shorter interval from DM to LMC compared to those with nodular DM (p=0.049). Conclusion: While the impact of DM on the overall prognosis of patients with BRCA is minimal, our findings underscore distinct characteristics and prognostic outcomes within DM subgroups.

Viscoelastic Property of the Brain Assessed With Magnetic Resonance Elastography and Its Association With Glymphatic System in Neurologically Normal Individuals.

OBJECTIVE: To investigate the feasibility of assessing the viscoelastic properties of the brain using magnetic resonance elastography (MRE) and a novel MRE transducer to determine the relationship between the viscoelastic properties and glymphatic function in neurologically normal individuals. MATERIALS AND METHODS: This prospective study included 47 neurologically normal individuals aged 23-74 years (male-to-female ratio, 21:26). The MRE was acquired using a gravitational transducer based on a rotational eccentric mass as the driving system. The magnitude of the complex shear modulus |G*| and the phase angle ϕ were measured in the centrum semiovale area. To evaluate glymphatic function, the Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) method was utilized and the ALPS index was calculated. Univariable and multivariable (variables with P < 0.2 from the univariable analysis) linear regression analyses were performed for |G*| and ϕ and included sex, age, normalized white matter hyperintensity (WMH) volume, brain parenchymal volume, and ALPS index as covariates. RESULTS: In the univariable analysis for |G*|, age (P = 0.005), brain parenchymal volume (P = 0.152), normalized WMH volume (P = 0.011), and ALPS index (P = 0.005) were identified as candidates with P < 0.2. In the multivariable analysis, only the ALPS index was independently associated with |G*|, showing a positive relationship (ß = 0.300, P = 0.029). For ϕ, normalized WMH volume (P = 0.128) and ALPS index (P = 0.015) were identified as candidates for multivariable analysis, and only the ALPS index was independently associated with ϕ (ß = 0.057, P = 0.039). CONCLUSION: Brain MRE using a gravitational transducer is feasible in neurologically normal individuals over a wide age range. The significant correlation between the viscoelastic properties of the brain and glymphatic function suggests that a more organized or preserved microenvironment of the brain parenchyma is associated with a more unimpeded glymphatic fluid flow.

Assessment of Meningeal Lymphatics in the Parasagittal Dural Space: A Prospective Feasibility Study Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

OBJECTIVE: Meningeal lymphatic vessels are predominantly located in the parasagittal dural space (PSD); these vessels drain interstitial fluids out of the brain and contribute to the glymphatic system. We aimed to investigate the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the dynamic changes in the meningeal lymphatic vessels in PSD. MATERIALS AND METHODS: Eighteen participants (26-71 years; male:female, 10:8), without neurological or psychiatric diseases, were prospectively enrolled and underwent DCE-MRI. Three regions of interests (ROIs) were placed on the PSD, superior sagittal sinus (SSS), and cortical vein. Early and delayed enhancement patterns and six kinetic curve-derived parameters were obtained and compared between the three ROIs. Moreover, the participants were grouped into the young (< 65 years; n = 9) or older (≥ 65 years; n = 9) groups. Enhancement patterns and kinetic curve-derived parameters in the PSD were compared between the two groups. RESULTS: The PSD showed different enhancement patterns than the SSS and cortical veins (P < 0.001 and P < 0.001, respectively) in the early and delayed phases. The PSD showed slow early enhancement and a delayed wash-out pattern. The six kinetic curve-derived parameters of PSD was significantly different than that of the SSS and cortical vein. The PSD wash-out rate of older participants was significantly lower (median, 0.09; interquartile range [IQR], 0.01-0.15) than that of younger participants (median, 0.32; IQR, 0.07-0.45) (P = 0.040). CONCLUSION: This study shows that the dynamic changes of meningeal lymphatic vessels in PSD can be assessed with DCE-MRI, and the results are different from those of the venous structures. Our finding that delayed wash-out was more pronounced in the PSD of older participants suggests that aging may disturb the meningeal lymphatic drainage.

Prominent cerebral veins on susceptibility-weighted angiography in acute meningoencephalitis.

BACKGROUND AND PURPOSE: We have commonly observed prominent cerebral veins on susceptibility-weighted angiography (SWAN) in acute meningoencephalitis. This study aimed to investigate the clinical significance of these findings. METHODS: Cerebral veins on SWAN of 98 patients with acute meningoencephalitis diagnosed from February 2016 through October 2020 were classified into three groups according to the degree of venous prominence (mild, 23; moderate, 53; and prominent, 22). Clinical variables and laboratory findings were compared between these groups. The influence of variables on the prediction of prominent cerebral veins was measured by random forest (RF) and gradient boosting machine (GBM). RESULTS: As cerebral veins became more prominent, cerebrospinal fluid (CSF) glucose level decreased (69.61 ± 29.05 vs. 59.72 ± 22.57 vs. 48.36 ± 20.29 mg/dL, p = .01) and CSF protein level increased (100.73 ± 82.98 vs. 104.73 ± 70.99 vs. 159.12 ± 118.15 mg/dL, p = .03). The etiology of meningoencephalitis, neurological symptoms, and increased intracranial pressure (ICP) signs differed between groups (p < .05). RF and GBM demonstrated that CSF protein level was the variable with the highest power to predict the prominent cerebral vein (mean decrease in node impurity: 4.19, relative influence: 50.66). CONCLUSION: The presence of prominent cerebral veins on SWAN in acute meningoencephalitis was significantly associated with a low CSF glucose level and a high CSF protein level, as well as ICP. Thus, the visual grade of the cerebral veins on SWAN may be utilized for the management of patients with acute meningoencephalitis.

Associated Factors of Spontaneous Hemorrhage in Brain Metastases in Patients with Lung Adenocarcinoma.

BACKGROUND: Hemorrhage in brain metastases (BMs) from lung cancer is common and associated with a poor prognosis. Research on associated factors of spontaneous hemorrhage in patients with BMs is limited. This study aimed to investigate the predictive risk factors for BM hemorrhage and assess whether hemorrhage affects patient survival. METHODS: We retrospectively evaluated 159 BMs from 80 patients with lung adenocarcinoma from January 2017 to May 2022. Patients were classified into hemorrhagic and non-hemorrhagic groups. Patient demographics, lung cancer molecular subtype, treatment type, and tumor-node-metastasis stage were compared between the groups. Multivariate generalized estimating equation (GEE) analysis and gradient boosting were performed. To determine whether BM hemorrhage can stratify overall survival after BM (OSBM), univariate survival analysis was performed. RESULTS: In the univariate analysis, hemorrhagic BMs were significantly larger and had a history of receiving combination therapy with tyrosine kinase inhibitor (TKI) and intracranial radiation (p < 0.05). Multivariate GEE showed that tumor size and combination therapy were independent risk factors for BM hemorrhage (p < 0.05). Gradient boosting demonstrated that the strongest predictor of BM hemorrhage was tumor size (variable importance: 49.83), followed by age (16.65) and TKI combined with intracranial radiation (13.81). There was no significant difference in OSBM between the two groups (p = 0.33). CONCLUSIONS: Hemorrhage in BMs from lung adenocarcinomas may be associated with BM tumor size and a combination of TKI and intracranial radiotherapy. BM hemorrhage did not affect OSBM.

Clinicopathological Characteristics of NRG1 Fusion-Positive Solid Tumors in Korean Patients.

PURPOSE: Neuregulin 1 (NRG1) gene fusion is a potentially actionable oncogenic driver. The oncoprotein binds to ERBB3-ERBB2 heterodimers and activates downstream signaling, supporting a therapeutic approach for inhibiting ERBB3/ERBB2. However, the frequency and clinicopathological features of solid tumors harboring NRG1 fusions in Korean patients remain largely unknown. MATERIALS AND METHODS: We reviewed archival data from next-generation sequencing panel tests conducted at a single institution, specifically selecting patients with in-frame fusions that preserved the functional domain. The clinicopathological characteristics of patients harboring NRG1 fusions were retrospectively reviewed. RESULTS: Out of 8,148 patients, NRG1 fusions were identified in 22 patients (0.27%). The average age of the patients was 59 years (range, 32 to 78 years), and the male-to-female ratio was 1:1.2. The lung was the most frequently observed primary site (n=13), followed by the pancreaticobiliary tract (n=3), gastrointestinal tract (n=2, stomach and rectum each), ovary (n=2), breast (n=1), and soft tissue (n=1). Histologically, all tumors demonstrated adenocarcinoma histology, with the exception of one case of sarcoma. CD74 (n=8) and SLC3A2 (n=4) were the most frequently identified fusion partners. Dominant features included the presence of fewer than three co-occurring genetic alterations, a low tumor mutation burden, and low programmed death-ligand 1 expression. Various clinical responses were observed in patients with NRG1 fusions. CONCLUSION: Despite the rarity of NRG1 fusions in Korean patients with solid tumors, identification through next-generation sequencing enables the possibility of new targeted therapies.

Clinicoradiologic Characteristics of Intradural Extramedullary Conventional Spinal Ependymoma.

Purpose: Distinguishing intradural extramedullary (IDEM) spinal ependymoma from myxopapillary ependymoma is challenging due to the location of IDEM spinal ependymoma. This study aimed to investigate the utility of clinical and MR imaging features for differentiating between IDEM spinal and myxopapillary ependymomas. Materials and Methods: We compared tumor size, longitudinal/axial location, enhancement degree/pattern, tumor margin, signal intensity (SI) of the tumor on T2-weighted images and T1-weighted image (T1WI), increased cerebrospinal fluid (CSF) SI caudal to the tumor on T1WI, and CSF dissemination of pathologically confirmed 12 IDEM spinal and 10 myxopapillary ependymomas. Furthermore, classification and regression tree (CART) was performed to identify the clinical and MR features for differentiating between IDEM spinal and myxopapillary ependymomas. Results: Patients with IDEM spinal ependymomas were older than those with myxopapillary ependymomas (48 years vs. 29.5 years, p < 0.05). A high SI of the tumor on T1W1 was more frequently observed in IDEM spinal ependymomas than in myxopapillary ependymomas (p = 0.02). Conversely, myxopapillary ependymomas show CSF dissemination. Increased CSF SI caudal to the tumor on T1WI was observed more frequently in myxopapillary ependymomas than in IDEM spinal ependymomas (p < 0.05). Dissemination to the CSF space and increased CSF SI caudal to the tumor on T1WI were the most important variables in CART analysis. Conclusion: Clinical and radiological variables may help differentiate between IDEM spinal and myxopapillary ependymomas.

A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases.

PURPOSE: Breast cancer brain metastases (BCBM) may involve subtypes that differ from the primary breast cancer lesion. This study aimed to develop a radiomics-based model that utilizes preoperative brain MRI for multiclass classification of BCBM subtypes and to investigate whether the model offers better prediction accuracy than the assumption that primary lesions and their BCBMs would be of the same subtype (non-conversion model) in an external validation set. MATERIALS AND METHODS: The training and external validation sets each comprised 51 cases (102 cases total). Four machine learning classifiers combined with three feature selection methods were trained on radiomic features and primary lesion subtypes for prediction of the following four subtypes: 1) hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, 2) HR+/HER2+, 3) HR-/HER2+, and 4) triple-negative. After training, the performance of the radiomics-based model was compared to that of the non-conversion model in an external validation set using accuracy and F1-macro scores. RESULTS: The rate of discrepant subtypes between primary lesions and their respective BCBMs were 25.5% (n=13 of 51) in the training set and 23.5% (n=12 of 51) in the external validation set. In the external validation set, the accuracy and F1-macro score of the radiomics-based model were significantly higher than those of the non-conversion model (0.902 vs. 0.765, p=0.004; 0.861 vs. 0.699, p=0.002). CONCLUSION: Our radiomics-based model represents an incremental advance in the classification of BCBM subtypes, thereby facilitating a more appropriate personalized therapy.

Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.

Purpose/objectives: Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results: RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion: RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.

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.

Delirium-related factors and their prognostic value in patients undergoing craniotomy for brain metastasis.

Background: Delirium is characterized by acute brain dysfunction. Although delirium significantly affects the quality of life of patients with brain metastases, little is known about delirium in patients who undergo craniotomy for brain metastases. This study aimed to identify the factors influencing the occurrence of delirium following craniotomy for brain metastases and determine its impact on patient prognosis. Method: A total of 153 patients who underwent craniotomy for brain metastases between March 2013 and December 2020 were evaluated for clinical and radiological factors related to the occurrence of delirium. Statistical analysis was conducted by dividing the patients into two groups based on the presence of delirium, and statistical significance was confirmed by adjusting the clinical characteristics of the patients with brain metastases using propensity score matching (PSM). The effect of delirium on patient survival was subsequently evaluated using Kaplan-Meier analysis. Results: Of 153 patients, 14 (9.2%) had delirium. Age (P = 0.002), sex (P = 0.007), and presence of postoperative hematoma (P = 0.001) were significantly different between the delirium and non-delirium groups. When the matched patients (14 patients in each group) were compared using PSM, postoperative hematoma showed a statistically significant difference (P = 0.036) between the delirium and non-delirium groups. Kaplan-Meier survival analysis revealed that the delirium group had poorer prognosis (log-rank score of 0.0032) than the non-delirium group. Conclusion: In addition to the previously identified factors, postoperative hematoma was identified as a strong predictor of postoperative delirium. Also, the negative impact of delirium on patient prognosis including low survival rate was confirmed.

Hippocampal Metastasis Rate Based on Non-Small Lung Cancer TNM Stage and Molecular Markers.

Hippocampal-avoidance whole-brain radiation therapy (HA-WBRT) is justified because of low hippocampal brain metastases (BM) rate and its prevention of cognitive decline. However, we hypothesize that the risk of developing BM in the hippocampal-avoidance region (HAR) may differ depending on the lung-cancer stage and molecular status. We retrospectively reviewed 123 patients with non-small cell lung cancer (NSCLC) at the initial diagnosis of BM. The number of BMs within the HAR (5 mm expansion) was counted. The cohort was divided into patients with and without BMs in the HAR, and their clinical variables, TNM stage, and epidermal growth factor receptor (EGFR) status were compared. The most influential variable predicting BMs in the HAR was determined using multi-variable logistic regression, classification and regression tree (CART) analyses, and gradient boosting method (GBM). The feasibility of HAR expansion was tested using generalized estimating equation marginal model. Patients with BMs in the HAR were more frequently non-smokers, and more likely to have extra-cranial metastases and EGFR mutations (p<0.05). Multi-variable analysis revealed that extra-cranial metastases were independently associated with the presence of BM in the HAR (odds ratio=8.75, p=0.04). CART analysis and GBM revealed that the existence of extra-cranial metastasis was the most influential variable predicting BM occurrence in the HAR (variable importance: 23% and relative influence: 37.38). The estmated BM incidence of patients without extra-cranial metastases in th extended HAR (7.5-mm and 10-mm expansion) did not differ significantly from that in the conventional HAR. In conclusion, NSCLC patients with extra-cranial metastases were more likely to have BMs in the HAR than those without extra-cranial metastases.