Differential Distribution of Brain Metastases from Non-Small Cell Lung Cancer Based on Mutation Status.

Non-small cell lung cancer (NSCLC) has a high rate of brain metastasis. The purpose of this study was to assess the differential distribution of brain metastases from primary NSCLC based on mutation status. Brain MRI scans of patients with brain metastases from primary NSCLC were retrospectively analyzed. Brain metastatic tumors were grouped according to mutation status of their primary NSCLC and the neuroimaging features of these brain metastases were analyzed. A total of 110 patients with 1386 brain metastases from primary NSCLC were included in this study. Gray matter density at the tumor center peaked at ~0.6 for all mutations. The median depths of tumors were 7.9 mm, 8.7 mm and 9.1 mm for EGFR, ALK and KRAS mutation groups, respectively (p = 0.044). Brain metastases for the EGFR mutation-positive group were more frequently located in the left cerebellum, left cuneus, left precuneus and right precentral gyrus. In the ALK mutation-positive group, brain metastases were more frequently located in the right middle occipital gyrus, right posterior cingulate, right precuneus, right precentral gyrus and right parietal lobe. In the KRAS mutation-positive patient group, brain metastases were more frequently located in the posterior left cerebellum. Our study showed differential spatial distribution of brain metastases in patients with NSCLC according to their mutation status. Information regarding distribution of brain metastases is clinically relevant as it could be helpful to guide treatment planning for targeted therapy, and for predicting prognosis.

A comprehensive machine-learning model applied to MRI to classify germinomas of the pineal region.

BACKGROUND: Pineal region tumors (PRTs) are highly histologically heterogeneous. Germinoma is the most common PRT and is treatable with radiotherapy and chemotherapy. A non-invasive system that helps identify germinoma in the pineal region could reduce lab exams and traumatic therapies. METHODS: In this retrospective study, 122 patients with histologically confirmed PRTs and pre-operative multi-modal MR images were included. Radiomics features were extracted from different ROIs and image sequences separately. A computational framework that combines a few classification and feature selection algorithms were used to predict histology with radiomics features and demographics. We systemically benchmarked performance of models with feature matrices from all possible combinations of ROIs and image sequences. The Area under the ROC Curve (AUC) was then used to evaluate model performance. RESULTS: Models with demographics and radiomics features outperform radiomics-only or demographics-only models. The best demographical-radiomics model reached the highest AUC of 0.88 (CI95%: 0.81-0.96). Through the comprehensive evaluation of possible sequence combinations in the differential diagnosis of pineal tumor, T1 and T2 emerged as the most informative sequences for the task. There is imbalanced usage of feature classes as we analyze their proportion in all models. CONCLUSIONS: The demographical-radiomics model can accurately and efficiently identify germinomas in the pineal region. The preference for MRI sequences, radiomics feature classes, features selection and classification algorithms provide a valuable reference for future attempts at developing classifiers on medical images.

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review.

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

Fatty acid oxidation fuels glioblastoma radioresistance with CD47-mediated immune evasion.

Glioblastoma multiforme (GBM) remains the top challenge to radiotherapy with only 25% one-year survival after diagnosis. Here, we reveal that co-enhancement of mitochondrial fatty acid oxidation (FAO) enzymes (CPT1A, CPT2 and ACAD9) and immune checkpoint CD47 is dominant in recurrent GBM patients with poor prognosis. A glycolysis-to-FAO metabolic rewiring is associated with CD47 anti-phagocytosis in radioresistant GBM cells and regrown GBM after radiation in syngeneic mice. Inhibition of FAO by CPT1 inhibitor etomoxir or CRISPR-generated CPT1A-/-, CPT2-/-, ACAD9-/- cells demonstrate that FAO-derived acetyl-CoA upregulates CD47 transcription via NF-κB/RelA acetylation. Blocking FAO impairs tumor growth and reduces CD47 anti-phagocytosis. Etomoxir combined with anti-CD47 antibody synergizes radiation control of regrown tumors with boosted macrophage phagocytosis. These results demonstrate that enhanced fat acid metabolism promotes aggressive growth of GBM with CD47-mediated immune evasion. The FAO-CD47 axis may be targeted to improve GBM control by eliminating the radioresistant phagocytosis-proofing tumor cells in GBM radioimmunotherapy.

Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning.

Background: Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia. Methods: This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization. Results: The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors. Conclusions: This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.

Deep Learning Model for Intracranial Hemangiopericytoma and Meningioma Classification.

Background: Intracranial hemangiopericytoma/solitary fibrous tumor (SFT/HPC) is a rare type of neoplasm containing malignancies of infiltration, peritumoral edema, bleeding, or bone destruction. However, SFT/HPC has similar radiological characteristics as meningioma, which had different clinical managements and outcomes. This study aims to discriminate SFT/HPC and meningioma via deep learning approaches based on routine preoperative MRI. Methods: We enrolled 236 patients with histopathological diagnosis of SFT/HPC (n = 144) and meningioma (n = 122) from 2010 to 2020 in Xiangya Hospital. Radiological features were extracted manually, and a radiological diagnostic model was applied for classification. And a deep learning pretrained model ResNet-50 was adapted to train T1-contrast images for predicting tumor class. Deep learning model attention mechanism was visualized by class activation maps. Results: Our study reports that SFT/HPC was found to have more invasion to venous sinus (p = 0.001), more cystic components (p < 0.001), and more heterogeneous enhancement patterns (p < 0.001). Deep learning model achieved a high classification accuracy of 0.889 with receiver-operating characteristic curve area under the curve (AUC) of 0.91 in the validation set. Feature maps showed distinct clustering of SFT/HPC and meningioma in the training and test cohorts, respectively. And the attention of the deep learning model mainly focused on the tumor bulks that represented the solid texture features of both tumors for discrimination.

Multivariate Analysis and Validation of the Prognostic Factors for Skull Base Chordoma.

Background: Skull base chordoma is a rare tumor with low-grade malignancy and a high recurrence rate, the factors affecting the prognosis of patients need to be further studied. For that, we investigated prognostic factors of skull base chordoma through the database of the Surveillance, Epidemiology, and End Results (SEER) program, and validated in an independent data set from the Xiangya Hospital. Methods: Six hundred and forty-three patients diagnosed with skull base chordoma were obtained from the SEER database (606 patients) and the Xiangya Hospital (37 patients). Categorical variables were selected by Chi-square test with a statistical difference. Survival curves were constructed by Kaplan-Meier analysis and compared by log-rank test. Univariate and multivariate Cox regression analyses were used to explore the prognostic factors. Propensity score matching (PSM) analysis was undertaken to reduce the substantial bias between gross total resection (GTR) and subtotal resection (STR) groups. Furthermore, clinical data of 37 patients from the Xiangya Hospital were used as validation cohorts to check the survival impacts of the extent of resection and adjuvant radiotherapy on prognosis. Results: We found that age at diagnosis, primary site, disease stage, surgical treatment, and tumor size was significantly associated with the prognosis of skull base chordoma. PSM analysis revealed that there was no significant difference in the OS between GTR and STR (p = 0.157). Independent data set from the Xiangya Hospital proved no statistical difference in OS between GTR and STR groups (p = 0.16), but the GTR group was superior to the STR group for progression-free survival (PFS) (p = 0.048). Postoperative radiotherapy does not improve OS (p = 0.28), but it can prolong PFS (p = 0.0037). Nomograms predicting 5- and 10-year OS and DSS were constructed based on statistically significant factors identified by multivariate Cox analysis. Age, primary site, tumor size, surgical treatment, and disease stage were included as prognostic predictors in the nomograms with good performance. Conclusions: We identified age, tumor size, surgery, primary site, and tumor stage as main factors affecting the prognosis of the skull base chordoma. Resection of the tumor as much as possible while ensuring safety, combined with postoperative radiotherapy may be the optimum treatment for skull base chordoma.

Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.

Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration. Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration. Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22-85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group. Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.

PURPOSE: To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively. METHODS: This prospective study enrolled consecutive patients who underwent neck MR scans and subsequent thyroidectomy during the study interval. The diagnosis and aggressiveness of PTC were determined by pathological evaluation of thyroidectomy specimens. Thyroid nodules were segmented manually on the MR images, and radiomic features were then extracted. Predictive machine learning modelling was used to evaluate the prediction of PTC aggressiveness. Area under the receiver operating characteristic curve (AUC) values for the model performance were obtained for radiomic features, clinical characteristics, and combinations of radiomic features and clinical characteristics. RESULTS: The study cohort included 120 patients with pathology-confirmed PTC (training cohort: n = 96; testing cohort: n = 24). A total of 1393 features were extracted from T2-weighted, apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted MR images for each patient. The combination of Least Absolute Shrinkage and Selection Operator for radiomic feature selection and Gradient Boosting Classifier for classifying PTC aggressiveness achieving the AUC of 0.92. In contrast, clinical characteristics alone poorly predicted PTC aggressiveness, with an AUC of 0.56. CONCLUSIONS: Our study showed that machine learning-based multiparametric MR imaging radiomics could accurately distinguish aggressive from non-aggressive PTC preoperatively. This approach may be helpful for informing treatment strategies and prognosis of patients with aggressive PTC.

Effects of chemotherapy on aging white matter microstructure: A longitudinal diffusion tensor imaging study.

OBJECTIVE: We aimed to use diffusion tensor imaging (DTI) to detect alterations in white matter microstructure in older patients with breast cancer receiving chemotherapy. METHODS: We recruited women age ≥60 years with stage I-III breast cancer (chemotherapy [CT] group; n = 19) to undergo two study assessments: at baseline and within one month after chemotherapy. Each assessment consisted of a brain magnetic resonance imaging scan with DTI and neuropsychological (NP) testing using the National Institutes of Health (NIH) Toolbox Cognition Battery. An age- and sex-matched group of healthy controls (HC, n = 14) underwent the same assessments at matched intervals. Four DTI parameters (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], and radial diffusivity [RD]) were calculated and correlated with NP testing scores. RESULTS: For CT group but not HCs, we detected statistically significant increases in MD and RD in the genu of the corpus callosum from time point 1 to time point 2 at p < 0.01, effect size:0.3655 and 0.3173, and 95% confidence interval: from 0.1490 to 0.5821, and from 0.1554 to 0.4792, for MD and RD respectively. AD values increased for the CT group and decreased for the HC group over time, resulting in significant between-group differences (p = 0.0056, effect size:1.0215, 95% confidence interval: from 0.2773 to 1.7657). There were no significant correlations between DTI parameters and NP scores (p > 0.05). CONCLUSIONS: We identified alterations in white matter microstructures in older women with breast cancer undergoing chemotherapy. These findings may potentially serve as neuroimaging biomarkers for identifying cognitive impairment in older adults with cancer.

Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients.

Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.

Assessing Cerebral White Matter Microstructure in Children With Congenital Sensorineural Hearing Loss: A Tract-Based Spatial Statistics Study.

OBJECTIVES: To assess the microstructural properties of cerebral white matter in children with congenital sensorineural hearing loss (CSNHL). METHODS: Children (>4 years of age) with profound CSNHL and healthy controls with normal hearing (the control group) were enrolled and underwent brain magnetic resonance imaging (MRI) scans with diffusion tensor imaging (DTI). DTI parameters including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were obtained from a whole-brain tract-based spatial statistics analysis and were compared between the two groups. In addition, a region of interest (ROI) approach focusing on auditory cortex, i.e., Heschl's gyrus, using visual cortex, i.e., forceps major as an internal control, was performed. Correlations between mean DTI values and age were obtained with the ROI method. RESULTS: The study cohort consisted of 23 children with CSHNL (11 boys and 12 girls; mean age ± SD: 7.21 ± 2.67 years; range: 4.1-13.5 years) and 18 children in the control group (11 boys and 7 girls; mean age ± SD: 10.86 ± 3.56 years; range: 4.5-15.3 years). We found the axial diffusivity values being significantly greater in the left anterior thalamic radiation, right corticospinal tract, and corpus callosum in the CSHNL group than in the control group (p < 0.05). Significantly higher radial diffusivity values in the white matter tracts were noted in the CSHNL group as compared to the control group (p < 0.05). The fractional anisotropy values in the Heschl's gyrus in the CSNHL group were lower compared to the control group (p = 0.0015). There was significant negative correlation between the mean fractional anisotropy values in Heschl's gyrus and age in the CSNHL group < 7 years of age (r = -0.59, p = 0.004). CONCLUSION: Our study showed higher axial and radial diffusivities in the children affected by CNHNL as compared to the hearing children. We also found lower fractional anisotropy values in the Heschl's gyrus in the CSNHL group. Furthermore, we identified negative correlation between the fractional anisotropy values and age up to 7 years in the children born deaf. Our study findings suggest that myelination and axonal structure may be affected due to acoustic deprivation. This information may help to monitor hearing rehabilitation in the deaf children.

Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study.

PURPOSE: Older cancer patients are at increased risk of cancer-related cognitive impairment. The purpose of this study was to assess the alterations in intrinsic brain activity associated with adjuvant chemotherapy in older women with breast cancer. METHODS: Chemotherapy treatment (CT) group included sixteen women aged ≥ 60 years (range 60-82 years) with stage I-III breast cancers, who underwent both resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing with NIH Toolbox for Cognition before adjuvant chemotherapy, at time point 1 (TP1), and again within 1 month after completing chemotherapy, at time point 2 (TP2). Fourteen age- and sex-matched healthy controls (HC) underwent the same assessments at matched intervals. Three voxel-wise rs-fMRI parameters: amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity, were computed at each time point. The changes in rs-fMRI parameters from TP1 to TP2 for each group, the group differences in changes (the CT group vs. the HC group), and the group difference in the baseline rs-fMRI parameters were assessed. In addition, correlative analysis between the rs-fMRI parameters and neuropsychological testing scores was also performed. RESULTS: In the CT group, one brain region, which included parts of the bilateral subcallosal gyri and right anterior cingulate gyrus, displayed increased ALFF from TP1 to TP2 (cluster p-corrected = 0.024); another brain region in the left precuneus displayed decreased fALFF from TP1 to TP2 (cluster level p-corrected = 0.025). No significant changes in the rs-fMRI parameters from TP1 to TP2 were observed in the HC group. Although ALFF and fALFF alterations were observed only in the CT group, none of the between-group differences in rs-fMRI parameter changes reached statistical significance. CONCLUSIONS: Our study results of ALFF and fALFF alterations in the chemotherapy-treated women suggest that adjuvant chemotherapy may affect intrinsic brain activity in older women with breast cancer.

Gray matter density reduction associated with adjuvant chemotherapy in older women with breast cancer.

PURPOSE: The purpose of this study was to evaluate longitudinal changes in brain gray matter density (GMD) before and after adjuvant chemotherapy in older women with breast cancer. METHODS: We recruited 16 women aged ≥ 60 years with stage I-III breast cancers receiving adjuvant chemotherapy (CT) and 15 age- and sex-matched healthy controls (HC). The CT group underwent brain MRI and the NIH Toolbox for Cognition testing prior to adjuvant chemotherapy (time point 1, TP1) and within 1 month after chemotherapy (time point 2, TP2). The HC group underwent the same assessments at matched intervals. GMD was evaluated with the voxel-based morphometry. RESULTS: The mean age was 67 years in the CT group and 68.5 years in the HC group. There was significant GMD reduction within the chemotherapy group from TP1 to TP2. Compared to the HC group, the CT group displayed statistically significantly greater GMD reductions from TP1 to TP2 in the brain regions involving the left anterior cingulate gyrus, right insula, and left middle temporal gyrus (pFWE(family-wise error)-corrected < 0.05). The baseline GMD in left insula was positively correlated with the baseline list-sorting working memory score in the HC group (pFWE-corrected < 0.05). No correlation was observed for the changes in GMD with the changes in cognitive testing scores from TP1 to TP2 (pFWE-corrected < 0.05). CONCLUSIONS: Our findings indicate that GMD reductions were associated with adjuvant chemotherapy in older women with breast cancer. Future studies are needed to understand the clinical significance of the neuroimaging findings. This study is registered on ClinicalTrials.gov (NCT01992432).

Subcortical brain iron deposition and cognitive performance in older women with breast cancer receiving adjuvant chemotherapy: A pilot MRI study.

As the number of older adults in the U.S. increases, so too will the incidence of cancer and cancer-related cognitive impairment (CRCI). However, the exact underlying biological mechanism for CRCI is not yet well understood. We utilized susceptibility-weighted imaging with quantitative susceptibility mapping, a non-invasive MRI-based technique, to assess longitudinal iron deposition in subcortical gray matter structures and evaluate its association with cognitive performance in women age 60+ with breast cancer receiving adjuvant chemotherapy and age-matched women without breast cancer as controls. Brain MRI scans and neurocognitive scores from the NIH Toolbox for Cognition were obtained before chemotherapy (time point 1) and within one month after the last infusion of chemotherapy for the patients and at matched intervals for the controls (time point 2). There were 14 patients age 60+ with breast cancer (mean age 66.3 ±â€¯5.3 years) and 13 controls (mean age 68.2 ±â€¯6.1 years) included in this study. Brain iron increased as age increased. There were no significant between- or within- group differences in neurocognitive scores or iron deposition at time point 1 or between time points 1 and 2 (p > 0.01). However, there was a negative correlation between iron in the globus pallidus and the fluid cognition composite scores in the control group at time point 1 (r = -0.71; p < 0.01), but not in the chemotherapy group. Baseline iron in the putamen was negatively associated with changes in the oral reading recognition scores in the control group (r = 0.74, p < 0.01), but not in the chemotherapy group. Brain iron assessment did not indicate cancer or chemotherapy related short-term differences, yet some associations with cognition were observed. Studies with larger samples and longer follow-up intervals are warranted.

Clinical Long-Term Follow-Up Evaluation of Functional Neuronavigation in Adult Cerebral Gliomas.

OBJECTIVE: To assess the application of functional neuronavigation in surgeries of adult cerebral gliomas. METHODS: We performed a retrospective analysis of 375 cases of adult cerebral glioma patients who underwent microsurgical treatment between 2011 and 2017 in our department. Among them, 142 patients underwent surgery using functional neuronavigation (group A), and the other 233 patients were operated on without the help of functional neuronavigation (group B). For both groups, we categorized them into several subgroups according to the lesion locations. RESULTS: A significant difference in the gross total resection rate was observed between group A and group B (P = 0.001 for overall; P = 0.036 for EO area; and P = 0.004 for BBT area). The postoperative complication rate of group A was much lower than that of group B (P = 0.003 for overall; and P = 0.016 for BBT area). The postoperative 6-month Karnofsky Performance Scale score of all patients in group A was significantly higher than that of group B. Kaplan-Meier survival analyses showed significant increases in the median survival time of group A compared with that of group B (P < 0.001 for overall; P = 0.012 for EO area; P = 0.006 for BBT area), and the Cox proportional regression analysis estimated the hazard ratio of the functional neuronavigation to be 0.533, helping reduce the risk of death by 46.7%. CONCLUSIONS: This study confirmed that the application of neuronavigation in adult glioma surgery can improve postoperative quality of life and lengthen the survival time of patients, especially in cases involving the brainstem and the eloquent area.

Assessing brain volume changes in older women with breast cancer receiving adjuvant chemotherapy: a brain magnetic resonance imaging pilot study.

BACKGROUND: Cognitive decline is among the most feared treatment-related outcomes of older adults with cancer. The majority of older patients with breast cancer self-report cognitive problems during and after chemotherapy. Prior neuroimaging research has been performed mostly in younger patients with cancer. The purpose of this study was to evaluate longitudinal changes in brain volumes and cognition in older women with breast cancer receiving adjuvant chemotherapy. METHODS: Women aged ≥ 60 years with stage I-III breast cancer receiving adjuvant chemotherapy and age-matched and sex-matched healthy controls were enrolled. All participants underwent neuropsychological testing with the US National Institutes of Health (NIH) Toolbox for Cognition and brain magnetic resonance imaging (MRI) prior to chemotherapy, and again around one month after the last infusion of chemotherapy. Brain volumes were measured using Neuroreader™ software. Longitudinal changes in brain volumes and neuropsychological scores were analyzed utilizing linear mixed models. RESULTS: A total of 16 patients with breast cancer (mean age 67.0, SD 5.39 years) and 14 age-matched and sex-matched healthy controls (mean age 67.8, SD 5.24 years) were included: 7 patients received docetaxel and cyclophosphamide (TC) and 9 received chemotherapy regimens other than TC (non-TC). There were no significant differences in segmented brain volumes between the healthy control group and the chemotherapy group pre-chemotherapy (p > 0.05). Exploratory hypothesis generating analyses focusing on the effect of the chemotherapy regimen demonstrated that the TC group had greater volume reduction in the temporal lobe (change = - 0.26) compared to the non-TC group (change = 0.04, p for interaction = 0.02) and healthy controls (change = 0.08, p for interaction = 0.004). Similarly, the TC group had a decrease in oral reading recognition scores (change = - 6.94) compared to the non-TC group (change = - 1.21, p for interaction = 0.07) and healthy controls (change = 0.09, p for interaction = 0.02). CONCLUSIONS: There were no significant differences in segmented brain volumes between the healthy control group and the chemotherapy group; however, exploratory analyses demonstrated a reduction in both temporal lobe volume and oral reading recognition scores among patients on the TC regimen. These results suggest that different chemotherapy regimens may have differential effects on brain volume and cognition. Future, larger studies focusing on older adults with cancer on different treatment regimens are needed to confirm these findings. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01992432 . Registered on 25 November 2013. Retrospectively registered.

Proteomic and functional profiles of a follicle-stimulating hormone positive human nonfunctional pituitary adenoma.

Nonfunctional pituitary adenoma (NFPA) is highly heterogeneous with different hormone-expressed subtypes in NFPA tissues including follicle-stimulating hormone (FSH) positive, luteinizing hormone-positive, FSH/luteinizing hormone-positive, and negative types. To analyze in-depth the variations in the proteomes among different NFPA subtypes for our long-term goal to clarify molecular mechanisms of NFPA and to detect tumor biomarker for personalized medicine practice, a reference map of proteome of a human FSH-expressed NFPA tissue was described here. 2DE and PDQuest image analysis were used to array each protein. MALDI-TOF PMF and human Swiss-Prot databases with MASCOT search were used to identify each protein. A good 2DE pattern with high level of between-gel reproducibility was attained with an average positional deviation 1.98 ± 0.75 mm in the IEF direction and 1.62 ± 0.68 mm in the SDS-PAGE direction. Approximately 1200 protein spots were 2DE-detected and 192 redundant proteins that were contained in 141 protein spots were PMF-identified, representing 107 nonredundant proteins. Those proteins were located in cytoplasm, nucleus, plasma membrane, extracellular space, and so on, and those functioned in transmembrane receptor, ion channel, transcription/translation regulator, transporter, enzyme, phosphatase, kinase, and so on. Several important pathway networks were characterized from those identified proteins with DAVID and Ingenuity Pathway Analysis systems, including gluconeogenesis and glycolysis, mitochondrial dysfunction, oxidative stress, cell-cycle alteration, MAPKsignaling system, immune response, TP53-signaling, VEGF-signaling, and inflammation signaling pathways. Those resulting data contribute to a functional profile of the proteome of a human FSH-positive NFPA tissue, and will serve as a reference for the heterogeneity analysis of NFPA proteomes.