Predicting 1p/19q Chromosomal Deletion of Brain Tumors Using Machine Learning

2021 ◽  
Vol 10 (2) ◽  
pp. 1-7
Author(s):  
Gökalp Çinarer ◽  
Bülent Gürsel Emiroğlu ◽  
Ahmet Haşim Yurttakal
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Chromosomal Deletion

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

CNTM-01. Evaluating Traditional and Non-Traditional Eloquent Areas in Patients with Brain Tumors: Large-scale Network Analysis Using a Machine Learning-Based Platform

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi224-vi224
Author(s):  
Alexis Morell ◽  
Daniel Eichberg ◽  
Ashish Shah ◽  
Evan Luther ◽  
Victor Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Networks ◽  
Tumor Resection ◽  
Central Executive ◽  
Default Mode ◽  
Eloquent Areas ◽  
Neurologic Deficits

Abstract BACKGROUND Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSIONS The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.

scholarly journals Intraoperative Detection of Pediatric Brain Tumor Margins Using Raman Spectroscopy.

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Rashad Jabarkheel ◽  
Jonathon J Parker ◽  
Chi-Sing Ho ◽  
Travis Shaffer ◽  
Sanjiv Gambhir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Tissue ◽  
Pediatric Brain Tumor ◽  
Tissue Samples ◽  
Tumor Margins ◽  
Pediatric Brain ◽  
Tumor Debulking

Abstract INTRODUCTION Surgical resection is a mainstay of treatment in patients with brain tumors both for tissue diagnosis and for tumor debulking. While maximal resection of tumors is desired, neurosurgeons can be limited by the challenge of differentiating normal brain from tumor using only microscopic visualization and tactile feedback. Additionally, intraoperative decision-making regarding how aggressively to pursue a gross total resection frequently relies on pathologic preliminary diagnosis using frozen sections which are both time consuming and fallible. Here, we investigate the potential for Raman spectroscopy (RS) to rapidly detect pediatric brain tumor margins and classify brain tissue samples equivalent to histopathology. METHODS Using a first-of-its-kind rapid acquisition RS device we intraoperatively imaged fresh ex vivo pediatric brain tissue samples (2-3 mm × 2-3 mm × 2-3 mm) at the Lucille Packard Children's Hospital. All imaged samples received standard final histopathological analysis, as RS is a nondestructive imaging technique. We curated a labeled dataset of 575 + unique Raman spectra gathered from 160 + brain samples resulting from 23 pediatric patients who underwent brain tissue resection as part of tumor debulking or epilepsy surgery (normal controls). RESULTS To our knowledge we have created the largest labeled Raman spectra dataset of pediatric brain tumors. We are developing an end-to-end machine learning model that can predict final histopathology diagnosis within minutes from Raman spectral data. Our preliminary principle component analyses suggest that RS can be used to classify various brain tumors similar to “frozen” histopathology and can differentiate normal from malignant brain tissue in the context of low-grade glioma resections. CONCLUSION Our work suggests that machine learning approaches can be used to harness the material identification properties of RS for classifying brain tumors and detecting their margins.

scholarly journals Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

2020 ◽  
Vol 196 (10) ◽  
pp. 856-867 ◽  
Author(s):  
Martin Kocher ◽  
Maximilian I. Ruge ◽  
Norbert Galldiks ◽  
Philipp Lohmann
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Brain Metastases ◽  
Imaging Techniques ◽  
Machine Learning Algorithms ◽  
Radiotherapy Planning ◽  
Who Grade ◽  
Large Variability ◽  
Malignant Brain Tumors ◽  
Integrated Diagnosis

Abstract Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

scholarly journals Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1999 ◽  
Author(s):  
Milica M. Badža ◽  
Marko Č. Barjaktarović
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Magnetic Resonance Images ◽  
Generalization Capability ◽  
Data Set ◽  
Fold Cross Validation

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

Application of machine learning algorithms for the diagnosis of primary brain tumors.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 2533-2533
Author(s):  
Vasilii Khammad ◽  
Jose Javier Otero ◽  
Yolanda Cabello Izquierdo ◽  
Francisco Garagorry Guerra ◽  
Aline P. Becker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Who Classification ◽  
Dimensional Space ◽  
Prognostic Significance ◽  
Tumor Location ◽  
Machine Learning Algorithms ◽  
Primary Brain Tumors ◽  
Molecular Features ◽  
Integrated Diagnosis

2533 Background: Primary lesions of the CNS refer to a heterogeneous group of benign or malignant tumors arising in different parts of the brain and spinal cord. According to the 2016 CNS WHO classification, the accurate diagnosis of primary brain tumors requires a layered approach of histologic, anatomic and molecular features to generate an integrated diagnosis with clinical and prognostic significance. However, in the US and worldwide, scarce resources are available to perform all the required tests routinely, so methods that improve pre-test probabilities and decrease false positive results have significant clinical and financial impact. Aims: 1) validate new diagnostic workflows with implementation of modern machine learning/artificial intelligence approaches; 2) design a reliable and interactive computational platform for primary CNS tumor diagnosis. Methods: To achieve these goals we have developed a population model in Rstudio, “La Tabla”, based on the articles from open resources of MEDLINE database and the latest version of WHO classification of CNS tumors. The data of “La Tabla” is comprised of more than 100,000 adult and pediatric cases, including rare brain tumor diagnoses, such as Gangliocytoma, Diffuse Midline Glioma and etc. Results: Boruta package and weights function in R have been used to distinguish the most important features for diagnosis prediction. To visualize correlation between these features (age, ki67 level, tumor location, presence of myxoid areas, calcifications, necrosis and etc.) and all diagnoses in two-dimensional space, we used a t-SNE algorithm. Models trained with decision tree algorithms (randomForest, XGBoost and C5.0) showed high overall accuracy in predicting diagnoses of “La Tabla” (95%, 94% and 92%) and 300 patients at OSUCCC-James (93%, 74% and 87%) in the absence of IHC and molecular data. Neural networks provided by keras and nnet packages predicted diagnoses using just clinical and histological findings with 94% and 88% accuracy on “La Tabla” and James patient databases respectively. Currently, we are building “Shiny” applications with R to deliver easily operated platform for pathologists and physicians. Conclusions: In conclusion, we managed to generate models that are able to diagnose primary brain lesions using basic clinical data (age, gender, tumor location), ki67 levels and distinct features of histological architecture. Most of the models distinguish brain tumors and associated molecular status with high accuracy and will serve as a reliable tool for second opinion in clinical neuro-oncology.

scholarly journals Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

2022 ◽  
Author(s):  
Sahan M. Vijithananda ◽  
Mohan L. Jayatilake ◽  
Badra Hewavithana ◽  
Teresa Gonçalves ◽  
Luis M. Rato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Brain Tumor ◽  
Brain Tumors ◽  
High Performance ◽  
Selection Process ◽  
Parameter Tuning ◽  
Machine Learning Techniques ◽  
F Test ◽  
Machine Learning Classification

Abstract Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients.The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient.At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.Conclusion: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures such as brain biopsies.

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