Tumor mutation burden (TMB) is an important biomarker for the prediction of response to anti-PD-1 immunotherapies. Studies have shown that higher level of TMB (TMB-H) is associated with higher response rate to immunotherapies in patients with various types of advanced solid tumors. However, the measurement of TMB depends on whole exome sequencing (WES) which is an expensive assay and not always available in standard clinical oncology settings. In this work, we assess the feasibility of predicting TMB-H based upon hematoxylin and eosin (H&E)-stained histopathology images, which is a routinely conducted assay in clinical oncology. Using an Inception-V3 convolutional neural network (CNN) as a baseline feature extractor, we compare adding a multi-layer perceptron (MLP) and a squeeze-and-excitation (SE) network on top of the baseline CNN. Training from random initialization and tuning with pretrained weights are also compared. Experiments are conducted on the H&E whole-slide images (WSI) of the melanoma dataset of The Cancer Genome Atlas (TCGA). Results from a 4-fold cross-validation show that the highest average area under the receiver operating characteristic curve (AUC) is 0.589, which implies that the prediction of TMB based on H&E WSI for melanoma remains a challenging problem that will warrant further investigations.
Bias Field correction is a crucial step in MRI preprocessing. The bias field affects the intensity uniformity in MRI images. This effect is mostly due to the in-homogeneity in the magnetic fields or variation in magnetic susceptibility during acquisition. The presence of bias field affects the tissue classification stage, as most of the common methods assume uniform intensities across same tissue. We present a deep learning approach that uses an autoencoding architecture to predict the bias field. The performance of the method is evaluated based on tissue classification accuracy compared to the ground truth result. The proposed method outperforms a traditional histogram based method and results in a more accurate tissue classification.
Due to the spontaneous nature of resting fMRI (rs-fMRI) signals, cross-subject comparison and group studies of rs-fMRI are challenging. Existing group comparison methods typically reduce the fMRI time series either to lower-dimensional connectivity features or use ICA to reduce dimensionality. We previously developed BrainSync, an orthogonal transformation that allows direct comparison of fMRI time-series across subjects.1 This orthogonal transform performs a temporal alignment of time-series at homologous locations across subjects allowing a direct comparison of scans. In contrast with existing fMRI analysis methods, this transform does not involve dimensionality reduction and preserves the rich functional connectivity information in fMRI data. BrainSync Alignment (BSA) is an extension of this approach that jointly synchronizes fMRI data across time-series data for multiple subjects.2 Point-wise distance measures, or Pearson correlations, can be computed between the reference and synchronized time-series as measures of inter-subject differences in functional connectivity at each location in the brain. In group studies, especially in the case of spectrum disorders, distances to a single atlas do not fully reflect the differences between subjects that may lie on a multi-dimensional spectrum. Here we describe an approach that measures the distances between pairs of subjects instead of to a single reference point.3 We present novel pairwise statistical methods for fMRI that can be used for regression and also for identifying group differences. We demonstrate the effectiveness of our method in two studies: (i) pairwise comparisons of fMRI data in subjects for performing regression to an ADHD index, and (ii) an F-test using pairwise statistical analysis to compare traumatic brain injury (TBI) subjects that develop post-traumatic epilepsy (PTE) to those that do not.
Post-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlatedPost-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlated.
Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores and ADHD indices in a group of ADHD and control subjects. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128x128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and input to a standard input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed significant correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.
Spontaneous brain activity is an important biomarker for various neurological and psychological conditions and can be measured using resting functional Magnetic Resonance Imaging (rfMRI). Since brain activity during resting is spontaneous, it is not possible to directly compare rfMRI time-courses across subjects. Moreover, the spatial configuration of functionally specialized brain regions can vary across subjects throughout the cortex limiting our ability to make precise spatial comparisons. We describe a new approach to jointly align and synchronize fMRI data in space and time, across a group of subjects. We build on previously described methods for inter-subject spatial “Hyper-Alignment” and temporal synchronization through the “BrainSync” transform. We first describe BrainSync Alignment (BSA), a group-based extension of the pair-wise BrainSync transform, that jointly synchronizes resting or task fMRI data across time for multiple subjects. We then explore the combination of BSA with Response Hyper-Alignment (RHA) and compare with Connectivity Hyper-Alignment (CHA), an alternative approach to spatial alignment based on resting fMRI. The result of applying RHA and BSA is both to produce improved functional spatial correspondence across a group of subjects, and to align their time-series so that, even for spontaneous resting data, we see highly correlated temporal dynamics at homologous locations across the group. These spatiotemporally aligned data can then be used as an atlas in future applications. We validate these transfer functions by applying them to z-score maps of an independent dataset and calculating inter-subject correlation. The results show that RHA can be calculated from rfMRI and have comparable output with CHA by leveraging BSA. Moreover, through calculation and application to task fMRI-based spatial transformations on an independent dataset, we show that the combination of RHA and BSA produces improved spatial functional alignment significantly relative to either RHA or CHA alone.
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