scholarly journals Association of functional magnetic resonance imaging indices with postoperative language outcomes in patients with primary brain tumors

2013 ◽  
Vol 34 (4) ◽  
pp. E6 ◽  
Author(s):  
Bornali Kundu ◽  
Amy Penwarden ◽  
Joel M. Wood ◽  
Thomas A. Gallagher ◽  
Matthew J. Andreoli ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Clinical Setting ◽  
Tumor Location ◽  
Postoperative Outcomes ◽  
Language Mapping ◽  
Patient Age ◽  
Activation Area ◽  
Presurgical Planning ◽  
Patient Âgé

Object Functional MRI (fMRI) has the potential to be a useful presurgical planning tool to treat patients with primary brain tumor. In this study the authors retrospectively explored relationships between language-related postoperative outcomes in such patients and multiple factors, including measures estimated from task fMRI maps (proximity of lesion to functional activation area, or lesion-to-activation distance [LAD], and activation-based language lateralization, or lateralization index [LI]) used in the clinical setting for presurgical planning, as well as other factors such as patient age, patient sex, tumor grade, and tumor volume. Methods Patient information was drawn from a database of patients with brain tumors who had undergone preoperative fMRI-based language mapping of the Broca and Wernicke areas. Patients had performed a battery of tasks, including word-generation tasks and a text-versus-symbols reading task, as part of a clinical fMRI protocol. Individually thresholded task fMRI activation maps had been provided for use in the clinical setting. These clinical imaging maps were used to retrospectively estimate LAD and LI for the Broca and Wernicke areas. Results There was a relationship between postoperative language deficits and the proximity between tumor and Broca area activation (the LAD estimate), where shorter LADs were related to the presence of postoperative aphasia. Stratification by tumor location further showed that for posterior tumors within the temporal and parietal lobes, more bilaterally oriented Broca area activation (LI estimate close to 0) and a shorter Wernicke area LAD were associated with increased postoperative aphasia. Furthermore, decreasing LAD was related to decreasing LI for both Broca and Wernicke areas. Preoperative deficits were related to increasing patient age and a shorter Wernicke area LAD. Conclusions Overall, LAD and LI, as determined using fMRI in the context of these paradigms, may be useful indicators of postsurgical outcomes. Whereas tumor location may influence postoperative deficits, the results indicated that tumor proximity to an activation area might also interact with how the language network is affected as a whole by the lesion. Although the derivation of LI must be further validated in individual patients by using spatially specific statistical methods, the current results indicated that fMRI is a useful tool for predicting postoperative outcomes in patients with a single brain tumor.

scholarly journals Chart review of diagnostic methods, baseline characteristics and symptoms for European patients with pancreatic cancer

2021 ◽  
Vol 17 (15) ◽  
pp. 1843-1854
Author(s):  
Alfredo Carrato ◽  
Davide Melisi ◽  
Gerald Prager ◽  
Christoph B Westphalen ◽  
Anabel Ferreras ◽  
...  
Keyword(s):  
Chart Review ◽  
Diagnostic Technique ◽  
Tumor Location ◽  
Diagnostic Methods ◽  
Ductal Adenocarcinoma ◽  
Inclusion Criteria ◽  
First Line ◽  
Patient Age ◽  
Patient Âgé ◽  
Baseline Characteristics

Aim: To survey European physicians managing patients with metastatic pancreatic ductal adenocarcinoma (PDAC) and understand differences in baseline characteristics, diagnostic methods, symptoms and co-morbidities. Materials & methods: Patient record inclusion criteria were: ≥18 years old, metastatic PDAC diagnosis and completion of first-line treatment between July 2014 and January 2016. Records were grouped by patient age, gender and primary tumor location. Results: Records (n = 2565) were collected from nine countries. Baseline characteristics varied between subgroups. Computed tomography was the most frequently used diagnostic technique. Symptoms at diagnosis included abdominal and/or mid-back pain (72% of patients) and weight loss (61.5%). Co-morbidities varied with patient age. Conclusion: Greater awareness of symptoms, diagnostic methods and co-morbidities present at PDAC diagnosis may support better patient management decisions.

Postoperative outcomes following closed head injury and craniotomy for evacuation of hematoma in patients older than 80 years

2012 ◽  
Vol 116 (1) ◽  
pp. 234-245 ◽  
Author(s):  
Darryl Lau ◽  
Abdulrahman M. El-Sayed ◽  
John E. Ziewacz ◽  
Priya Jayachandran ◽  
Farhan S. Huq ◽  
...  
Keyword(s):  
Head Injury ◽  
Hospital Stay ◽  
Intracranial Hemorrhage ◽  
Closed Head Injury ◽  
Older Age ◽  
Postoperative Outcomes ◽  
Medical Attention ◽  
Patient Age ◽  
Patient Âgé ◽  
Closed Head

Object Advances in the management of trauma-induced intracranial hematomas and hemorrhage (epidural, subdural, and intraparenchymal hemorrhage) have improved survival in these conditions over the last several decades. However, there is a paucity of research investigating the relation between patient age and outcomes of surgical treatment for these conditions. In this study, the authors examined the relation between patient age over 80 years and postoperative outcomes following closed head injury and craniotomy for intracranial hemorrhage. Methods A consecutive population of patients undergoing emergent craniotomy for evacuation of intracranial hematoma following closed head trauma between 2006 and 2009 was identified. Using multivariable logistic regression models, the authors assessed the relation between age (> 80 vs ≤ 80 years) and postoperative complications, intensive care unit stay, hospital stay, morbidity, and mortality. Results Of 103 patients, 27 were older than 80 years and 76 patients were 80 years of age or younger. Older age was associated with longer length of hospital stay (p = 0.014), a higher rate of complications (OR 5.74, 95% CI 1.29–25.34), and a higher likelihood of requiring rehabilitation (OR 3.28, 95% CI 1.13–9.74). However, there were no statistically significant differences between the age groups in 30-day mortality or ability to recover to functional baseline status. Conclusions The findings suggest that in comparison with younger patients, patients over 80 years of age may be similarly able to return to preinjury functional baselines but may require increased postoperative medical attention in the forms of rehabilitation and longer hospital stays. Prospective studies concerned with the relation between older age, perioperative parameters, and postoperative outcomes following craniotomy for intracranial hemorrhage are needed. Nonetheless, the findings of this study may allow for more informed decisions with respect to the care of elderly patients with intracranial hemorrhage.

scholarly journals Initial symptoms and diagnostic delay in children with brain tumors at a single institution in Japan

2020 ◽  
Author(s):  
Yuji Yamada ◽  
Daiki Kobayashi ◽  
Keita Terashima ◽  
Chikako Kiyotani ◽  
Ryuji Sasaki ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Diagnostic Delay ◽  
Visual Outcome ◽  
Neurological Impairment ◽  
Tumor Location ◽  
Medical Consultation ◽  
Low Grade ◽  
Initial Symptoms ◽  
Diagnostic Interval

Abstract Background A prolonged interval between onset of symptoms and diagnosis of childhood brain tumor is associated with worse neurological outcomes. The objectives of this study are to determine factors contributing to diagnostic delay and to find an interventional focus for further reduction in the interval between symptom onset and diagnosis in Japan. Methods We retrospectively analyzed 154 patients younger than 18 years with newly diagnosed brain tumors who visited our institution from January 2002 to March 2013. Results The median age at diagnosis was 6.2 years and the median total diagnostic interval (TDI) was 30 days. Patients with low-grade tumors and cerebral midline tumor location had significantly long TDI. Durations between the first medical consultation and diagnosis (diagnostic interval, DI) were exceedingly longer for patients with visual, hearing, or smelling abnormalities as the first symptom (median, 303 days). TDI and DI of patients who visited ophthalmologists or otolaryngologist for the first medical consultation were significantly longer. Among these patients, longer DI was associated with worse visual outcome. Conclusion Raising awareness of brain tumor diagnosis among ophthalmologists and otolaryngologists may reduce diagnostic delay and may improve the neurological impairment of children with brain tumors in Japan.

scholarly journals The methylation of SDC2 and TFPI2 defined three methylator phenotypes of colorectal cancer

2021 ◽  
Author(s):  
Lei Ruixue ◽  
Zhao Yanteng ◽  
Huang Kai ◽  
Wan Kangkang ◽  
Li Tingting ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cpg Island ◽  
Methylation Status ◽  
Tumor Location ◽  
Functional Enrichment ◽  
Superior Performance ◽  
Regulation Of Transcription ◽  
Patient Age ◽  
Patient Âgé

Methylation-based noninvasive molecular diagnostics are easy and feasible tools for the early detection of colorectal cancer (CRC). However, many of them have the limitation of low sensitivity with some CRCs detection failed in clinical practice. In this study, the clinical and pathological characteristics, as well as molecular features of three methylator-groups, defined by the promoter methylation status of SDC2 and TFPI2, were investigated in order to improve the performance of CRC detection. The Illumina Infinium 450k Human DNA methylation data and clinical information of CRCs were collected from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) database. CRC samples were divided into three groups, HH (dual-positive), HL (single positive) and LL (dual-negative) according to the methylation status of SDC2 and TFPI2 promoters. Differences in age, tumor location, microsatellite instable status and differentially expressed genes (DEGs) were evaluated among the three groups and these findings were then confirmed in our inner CRC dataset. The combination of methylated SDC2 and TFPI2 showed a superior performance of distinguishing CRCs from normal controls than each alone. Samples of HL group were more often originated from left-side CRCs whereas very few of them were from right-side (P < 0.05). HH grouped CRCs showed a higher level of microsatellite instability and mutation load than other two groups (mean nonsynonymous mutations for HH/HL/LL: 10.55/3.91/7.02, P = 0.0055). All mutations of BRAF, one of the five typical CpG island methylator phenotype (CIMP) related genes, were found in HH group (HH/HL/LL: 51/0/0, P = 0.018). Also there was a significantly older patient age at the diagnosis in HH group. Gene expression analysis identified 37, 84 and 22 group-specific DEGs for HH, HL and LL, respectively. Functional enrichment analysis suggested that HH specific DEGs were mainly related to the regulation of transcription and other processes, while LL specific DEGs were enriched in the biological processes of extracellular matrix interaction and cell migration. The three defined mathylator groups showed great difference in tumor location, patient age, MSI and ECM biological process, which could facilitate the development of more effective biomarkers for CRC detection.

scholarly journals Brain tumor detection and classification using machine learning: a comprehensive survey

2021 ◽  
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Anandakumar Haldorai ◽  
Mussarat Yasmin ◽  
Ramesh Sundar Nayak
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Initial Phase ◽  
Tumor Detection ◽  
Tumor Location ◽  
Future Trends ◽  
Resonance Imaging ◽  
Quantum Machine Learning ◽  
Comprehensive Survey

AbstractBrain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

Expression of leucine-rich repeats and immunoglobulin-like domains (LRIG) proteins in human ependymoma relates to tumor location, WHO Grade, and patient age

2009 ◽  
Vol 28 (01) ◽  
pp. 21-27 ◽  
Author(s):  
W. Yi ◽  
H. Haapasalo ◽  
C. Holmlund S. Järvelä ◽  
O. Raheem ◽  
A.T. Bergenheim ◽  
...  
Keyword(s):  
Tumor Location ◽  
Patient Age ◽  
Who Grade ◽  
Patient Âgé ◽  
Leucine Rich Repeats

scholarly journals Gene Expression Patterns in Ependymomas Correlate with Tumor Location, Grade, and Patient Age

2003 ◽  
Vol 163 (5) ◽  
pp. 1721-1727 ◽  
Author(s):  
Andrey Korshunov ◽  
Kai Neben ◽  
Gunnar Wrobel ◽  
Bjoern Tews ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Tumor Location ◽  
Gene Expression Patterns ◽  
Patient Age ◽  
Patient Âgé

Tumor location and patient age predict biological signatures of high-grade gliomas

2017 ◽  
Vol 41 (2) ◽  
pp. 599-604 ◽  
Author(s):  
Roberto Altieri ◽  
Francesco Zenga ◽  
Alessandro Ducati ◽  
Antonio Melcarne ◽  
Fabio Cofano ◽  
...  
Keyword(s):  
Tumor Location ◽  
High Grade ◽  
Patient Age ◽  
Patient Âgé ◽  
High Grade Gliomas

scholarly journals Brain Tumour Detection Using Deep Learning Techniques

2019 ◽  
pp. 169-175 ◽  
Author(s):  
P. Tamije Selvy ◽  
V. P Dharani ◽  
A. Indhuja
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Probabilistic Neural Network ◽  
Brain Mri ◽  
Histogram Equalization ◽  
Tumor Location ◽  
World Health ◽  
Basic Part

In recent years the occurrence of brain tumor has exaggerated in large amount among the people. Gliomas are one of the most common types of primary brain tumors that represent 30% of all human brain tumors and 80% of all malevolent tumors. The grading system specified by the World Health Organization (WHO) is deployed as a standard mechanism for medical diagnosis, prognosis, and the existence forecast so far. The main ideology of this paper is to propose and develop reliable and typical methods to detect the brain tumor, extract the characteristic of it and classify the glioma using Magnetic Resonance Imaging (MRI). The developed model helps in the detection of brain tumor automatically and it is implemented using image processing and artificial neural network. The most basic part of image processing is the analysis and manipulation of a digitized image, especially in order to improve its quality. In this proposed system, the Histogram Equalization (HE) technique is used to improve the contrast of the original image. Then the pre-processed image is subjected to feature extraction using Gray Level Co-occurrence Matrix (GLCM). The obtained feature is given to Probabilistic Neural Network (PNN) classifier that is used to train and test the performance accuracy in the perception of tumor location in brain MRI images. By implementing this approach, PNN classifier has procured accuracy of about 90.9%.

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