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.

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.

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.

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.

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.

Harpagoside attenuates local bone Erosion and systemic osteoporosis in collagen-induced arthritis in mice.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease that causes local bone erosion and systemic osteoporosis. Harpagoside (HAR), an iridoid glycoside, has various pharmacological effects on pain, arthritis, and inflammation. Our previous study suggests that HAR is more deeply involved in the mechanism of bone loss caused by inflammatory stimuli than hormonal changes. Here, we identified the local and systemic bone loss inhibitory effects of HAR on RA and its intracellular mechanisms using a type 2 collagen-induced arthritis (CIA) mouse model. METHODS: The anti-osteoporosis and anti-arthritic effects of HAR were evaluated on bone marrow macrophage in vitro and CIA in mice in vivo by obtaining clinical scores, measuring hind paw thickness and inflammatory cytokine levels, micro-CT and histopathological assessments, and cell-based assay. RESULTS: HAR markedly reduced the clinical score and incidence rate of CIA in both the prevention and therapy groups. Histological analysis demonstrated that HAR locally ameliorated the destruction of bone and cartilage and the formation of pannus. In this process, HAR decreased the expression of inflammatory cytokines, such as tumor necrosis factor-α, interleukin (IL)-6, and IL-1ß in the serum of CIA mice. Additionally, HAR downregulated the expression of receptor activator of nuclear factor-κB ligand and upregulated that of osteoprotegerin. HAR suppressed systemic bone loss by inhibiting osteoclast differentiation and osteoclast marker gene expression in a CIA mouse model. CONCLUSIONS: Taken together, these findings show the beneficial effect of HAR on local symptoms and systemic bone erosion triggered by inflammatory arthritis.

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.

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.

Contrast-Enhanced Fluid-Attenuated Inversion Recovery in Neuroimaging: A Narrative Review on Clinical Applications and Technical Advances.

While contrast-enhanced fluid-attenuated inversion recovery (FLAIR) has long been regarded as an adjunct sequence to evaluate leptomeningeal disease in addition to contrast-enhanced T1-weighted imaging, it is gradually being used for more diverse pathologies beyond leptomeningeal disease. Contrast-enhanced FLAIR is known to be highly sensitive to low concentrations of gadolinium within the fluid. Accordingly, recent research has suggested the potential utility of contrast-enhanced FLAIR in various kinds of disease, such as Meniere's disease, seizure, stroke, traumatic brain injury, and brain metastasis, in addition to being used for visualizing glymphatic dysfunction. However, its potential applications have been reported sporadically in an unorganized manner. Furthermore, the exact mechanism for its superior sensitivity to low concentrations of gadolinium has not been fully understood. Rapidly developing magnetic resonance technology and unoptimized parameters for FLAIR may challenge its accurate application in clinical practice. This review provides the fundamental mechanism of contrast-enhanced FLAIR, systematically describes its current and potential clinical application, and elaborates on technical considerations for its optimization. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 5.

The role of apparent diffusion coefficient as a predictive factor for tumor recurrence in patients with cerebellopontine angle epidermoid tumor.

Intracranial epidermoid tumors are slowly growing benign tumors, but due to adjacent critical neurovascular structures, surgical resection is challenging, with the risk of recurrence. The apparent diffusion coefficient (ADC) has been used to evaluate the characteristics of brain tumors, but its utility for intracranial epidermoid tumors has not been specifically explored. This study analyzed the utility of preoperative ADC values in predicting tumor recurrence for patients with intracranial epidermoid tumors. Between 2008 and 2019, 21 patients underwent surgery for cerebellopontine angle (CPA) epidermoid tumor, and their preoperative ADC data were analyzed. The patients were divided into two groups: the recurrence group, defined by regrowth of the remnant tumor or newly developed mass after gross total resection on magnetic resonance imaging (MRI); and the stable group, defined by the absence of growth or evidence of tumor on MRI. Receiver operating characteristic (ROC) analysis was used to obtain the ADC cutoff values for predicting tumor recurrence. The prognostic value of the ADC was assessed using Kaplan-Meier curves. The minimum ADC values were significantly lower in the recurrence group than in the stable tumor group (P = 0.020). ROC analysis showed that a minimum ADC value lower than 804.5 × 10-6 mm2/s could be used to predict higher recurrence risk of CPA epidermoid tumors. Non-total resection and mean and minimum ADC values lower than the respective cutoffs were negative predictors of recurrence-free survival. Minimum ADC values could be useful in predicting the recurrence of CPA epidermoid tumors.

Quantitative Photothermal Characterization with Bioprinted 3D Complex Tissue Constructs for Early-Stage Breast Cancer Therapy Using Gold Nanorods.

Plasmonic photothermal therapy (PPTT) using gold nanoparticles (AuNPs) has shown great potential for use in selective tumor treatment, because the AuNPs can generate destructive heat preferentially upon irradiation. However, PPTT using AuNPs has not been added to practice, owing to insufficient heating methods and tissue temperature measurement techniques, leading to unreliable and inaccurate treatments. Because the photothermal properties of AuNPs vary with laser power, particle optical density, and tissue depth, the accurate prediction of heat generation is indispensable for clinical treatment. In this report, bioprinted 3D complex tissue constructs comprising processed gel obtained from porcine skin and human decellularized adipose tissue are presented for characterization of the photothermal properties of gold nanorods (AuNRs) having an aspect ratio of 3.7 irradiated by a near-infrared laser. Moreover, an analytical function is suggested for achieving PPTT that can cause thermal damage selectively on early-stage human breast cancer by regulating the heat generation of the AuNRs in the tissue.

Apparent diffusion coefficient as a prognostic factor in clival chordoma.

Clival chordoma is a rare disease with high recurrence rates even after a combination of surgical resection and radiotherapy. Apparent diffusion coefficient (ADC) has been used to evaluate aggressive features of chordoma, but its utility for clival chordoma has not been explored specifically. In this study, the utility of preoperative ADC values was analyzed for predicting tumor progression and recurrence in patients with clival chordoma. Between 2012 and 2019, a total of 30 operated cases were analyzed with available preoperative ADC data. Receiver operating characteristic (ROC) analysis was used to obtain ADC cutoff values for predicting tumor aggressiveness. The mean and minimum ADC values were significantly lower in the aggressive tumor group than in the stable tumor group (both P < 0.001). ROC analysis showed that a mean cutoff ADC value of 1198 × 10-6 mm2/s and minimum ADC value of 895.5 × 10-6 mm2/s could be used to predict aggressive features of clival chordoma. Subtotal resection, partial resection, and mean and minimum ADC values that were lower than cutoff values were negative predictors of overall survival and progression-free survival. In conclusion, mean and minimum ADC values could be useful in predicting aggressiveness of clival chordoma.

Clinico-radiological features of brain metastases from thyroid cancer.

ABSTRACT: The brain is an unusual site for distant metastases of thyroid cancer. The radiological features of brain metastases (BMs) have rarely been reported. Hemorrhage is frequently noted in BMs from thyroid cancer. This study aimed to investigate the clinico-radiological features of BMs from thyroid cancer and to determine the risk factors to predict BM hemorrhage.We retrospectively evaluated the MR images of 35 patients with BMs from thyroid cancer at our hospital from 2013 to 2020. The number, size, site, presence of extra-cranial metastasis, presence of perilesional edema, intra-tumoral hemorrhage, enhancement pattern, and presence of diffusion restriction on MRI were described. We further classified the thyroid cancers into hemorrhagic and nonhemorrhagic groups to investigate the factors associated with hemorrhage.54.29% of patients with thyroid BMs (19/35) had neurologic symptoms. 94.29% of patients (33/35) had extra-cranial metastases. The most common histology of primary thyroid cancer was papillary thyroid cancer (71.43%, 25/35), followed by anaplastic thyroid cancer (22.86%, 8/35). Thyroid cancer BMs were located mostly in the supra-tentorium (51.43%, 18/35) or both the supra and infra-tentorium (45.71%, 16/35). 60% of patients (21/35) showed hemorrhage within the BMs. The strongest predictor for BM hemorrhage was tumor size (variable importance: 50).Thyroid cancer BMs exhibit a bleeding tendency. Furthermore, larger BMs are more likely to have an intra-tumoral hemorrhage.

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer.

PURPOSE: To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC). METHODS: A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC). RESULTS: EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively. CONCLUSIONS: Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.

Corrigendum: Brain Metastases From Lung Adenocarcinoma May Preferentially Involve the Distal Middle Cerebral Artery Territory and Cerebellum.

[This corrects the article DOI: 10.3389/fonc.2020.01664.].

Aging Is Positively Associated with Peri-Sinus Lymphatic Space Volume: Assessment Using 3T Black-Blood MRI.

OBJECTIVES: Aging is a major risk factor for many neurological disorders and is associated with dural lymphatic dysfunction. We sought to evaluate the association of aging with the volume of the peri-sinus lymphatic space using contrast-enhanced 3T T1-weighted black-blood magnetic resonance imaging (MRI). METHODS: In this retrospective study, 165 presumed neurologically normal subjects underwent brain MRIs for cancer staging between April and November 2018. The parasagittal peri-sinus lymphatic space was evaluated using contrast-enhanced 3D T1-weighted black-blood MRIs, and volumes were measured with semiautomatic method. We compared the volumes of normalized peri-sinus lymphatic spaces between the elderly (≥65 years, n = 72) and non-elderly (n = 93) groups and performed multivariate logistic regression analyses to assess if aging is independently associated with the volume of normalized peri-sinus lymphatic spaces. RESULTS: The normalized peri-sinus lymphatic space volume was significantly higher in the elderly than in the non-elderly (mean, 3323 ± 758.7 mL vs. 2968.7 ± 764.3 mL, p = 0.047). After adjusting the intracranial volume, age age was the strongest factor independently associated with peri-sinus lymphatic space volume (ß coefficient, 28.4 (5.7-51.2), p = 0.015) followed by male sex (ß coefficient, 672.4 (113.5-1230.8), p = 0.019). CONCLUSIONS: We found that the peri-sinus dural lymphatic space volume was higher in the elderly group than in the non-elderly group, and the increased peri-sinus lymphatic space was independently associated with aging. These findings indicate that the peri-sinus lymphatic space may be related with the aging process and lymphatic system dysfunction as well.

Brain Metastases From Lung Adenocarcinoma May Preferentially Involve the Distal Middle Cerebral Artery Territory and Cerebellum.

Although whole-brain radiation therapy (WBRT) is the mainstay of treatment for brain metastases (BMs), the concept of saving eloquent cortical lesions has been promoted. If BMs from lung cancer are spatially biased to certain regions, this approach can be justified more. We evaluated whether BMs from lung cancer show a preference for certain brain regions and if their distribution pattern differs according to the histologic subtype of the primary lung cancer. In this retrospective study, 562 BMs in 80 patients were analyzed (107 BMs from small cell carcinoma, 432 from adenocarcinoma, and 23 from squamous cell carcinoma). Kernel density estimation was performed to investigate whether BM spatial patterns differed among lung cancer subtypes. Further, we explored more detailed subregions where BMs from adenocarcinomas occur frequently using one-way analysis of variance. Finally, we divided our cohort into those with fewer (≤10) and more (>10) BMs and evaluated whether this biased pattern was maintained across limited and extensive stages. For small cell carcinoma, BMs were biased to the cerebellum, but this did not reach statistical significance. For adenocarcinoma, BMs were found more frequently near the distal middle cerebral artery (MCA) territory and cerebellum than in other arterial territories (p < 0.01). The precentral and postcentral gyri were the most significant subregions within the distal anterior cerebral artery (ACA) and MCA territories (p < 0.01). Crus I and Lobule VI were significant regions within the cerebellum (p < 0.01). Regardless of the number of BMs, the affinity to the distal MCA territory and cerebellum was maintained. The present data confirm that BMs from lung adenocarcinoma may preferentially involve the distal MCA territory and cerebellum.

Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer.

Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.

Author Correction: Apparent diffusion coefficient histogram in breast cancer brain metastases may predict their biological subtype and progression.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.