Comparison of voxel-wise and histogram analyses of glioma ADC maps for prediction of early therapeutic change

Noninvasive imaging methods are sought to objectively predict early response to therapy for high-grade glioma tumors. Quantitative metrics derived from diffusion-weighted imaging, such as apparent diffusion coefficient (ADC), have previously shown promise when used in combination with voxel-based analysis reflecting regional changes. The functional diffusion mapping (fDM) metric is hypothesized to be associated with volume of tumor exhibiting an increasing ADC owing to effective therapeutic action. In this work, the reference fDM-predicted survival (from previous study) for 3 weeks from treatment initiation (midtreatment) is compared to multiple histogram-based metrics using Kaplan-Meier estimator for 80 glioma patients stratified to responders and nonresponders based on the population median value for the given metric. The ADC histogram metric reflecting reduction in midtreatment volume of solid tumor (ADC 8% population-median with respect to pretreatment is found to have the same predictive power as the reference fDM of increasing midtreatment ADC volume above 4%. This study establishes the level of correlation between fDM increase and low-ADC tumor volume shrinkage for prediction of early response to radiation therapy in patients with glioma malignancies

Development of a multiparametric voxel-based magnetic resonance imaging biomarker for early cancer therapeutic response assessment

Quantitative magnetic resonance imaging (MRI)-based biomarkers, which capture physiological and functional tumor processes, were evaluated as imaging surrogates of early tumor response following chemoradiotherapy in glioma patients. A multiparametric extension of a voxel-based analysis, referred as the parametric response map (PRM), was applied to quantitative MRI maps to test the predictive potential of this metric for detecting response. Fifty-six subjects with newly diagnosed high-grade gliomas treated with radiation and concurrent temozolomide were enrolled in a single-site prospective institutional review board-approved MRI study. Apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps were acquired before therapy and 3 weeks after therapy was initiated. Multiparametric PRM (mPRM) was applied to both physiological MRI maps and evaluated as an imaging biomarker of patient survival. For comparison, single-biomarker PRMs were also evaluated in this study. The simultaneous analysis of ADC and rCBV by the mPRM approach was found to improve the predictive potential for patient survival over single PRM measures. With an array of quantitative imaging parameters being evaluated as biomarkers of therapeutic response, mPRM shows promise as a new methodology for consolidating physiologically distinct imaging parameters into a single interpretable and quantitative metric

Gene expression profiling of bronchial brushes is associated with the level of emphysema measured by computed tomography-based parametric response mapping

Parametric response mapping (PRM) is a computed tomography (CT)-based method to phenotype patients with chronic obstructive pulmonary disease (COPD). It is capable of differentiating emphysema-related air trapping with nonemphysematous air trapping (small airway disease), which helps to identify the extent and localization of the disease. Most studies evaluating the gene expression in smokers and COPD patients related this to spirometric measurements, but none have investigated the relationship with CT-based measurements of lung structure. The current study aimed to examine gene expression profiles of brushed bronchial epithelial cells in association with the PRM-defined CT-based measurements of emphysema (PRM(Emph)) and small airway disease (PRM(fSAD)). Using the Top Institute Pharma (TIP) study cohort (COPD = 12 and asymptomatic smokers = 32), we identified a gene expression signature of bronchial brushings, which was associated with PRM(Emph) in the lungs. One hundred thirty-three genes were identified to be associated with PRM(Emph). Among the most significantly associated genes, CXCL11 is a potent chemokine involved with CD8(+) T cell activation during inflammation in COPD, indicating that it may play an essential role in the development of emphysema. The PRM(Emph) signature was then replicated in two independent data sets. Pathway analysis showed that the PRM(Emph) signature is associated with proinflammatory and notch signaling pathways. Together these findings indicate that airway epithelium may play a role in the development of emphysema and/or may act as a biomarker for the presence of emphysema. In contrast, its role in relation to functional small airways disease is less clear

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/2/ajt15814_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156134/1/ajt15814.pd

Parametric Response Mapping as an Indicator of Bronchiolitis Obliterans Syndrome after Hematopoietic Stem Cell Transplantation

AbstractThe management of bronchiolitis obliterans syndrome (BOS) after hematopoietic cell transplantation presents many challenges, both diagnostically and therapeutically. We developed a computed tomography (CT) voxel-wise methodology termed parametric response mapping (PRM) that quantifies normal parenchyma, functional small airway disease (PRMfSAD), emphysema, and parenchymal disease as relative lung volumes. We now investigate the use of PRM as an imaging biomarker in the diagnosis of BOS. PRM was applied to CT data from 4 patient cohorts: acute infection (n = 11), BOS at onset (n = 34), BOS plus infection (n = 9), and age-matched, nontransplant control subjects (n = 23). Pulmonary function tests and bronchoalveolar lavage were used for group classification. Mean values for PRMfSAD were significantly greater in patients with BOS (38% ± 2%) when compared with those with infection alone (17% ± 4%, P < .0001) and age-matched control subjects (8.4% ± 1%, P < .0001). Patients with BOS had similar PRMfSAD profiles, whether a concurrent infection was present or not. An optimal cut-point for PRMfSAD of 28% of the total lung volume was identified, with values >28% highly indicative of BOS occurrence. PRM may provide a major advance in our ability to identify the small airway obstruction that characterizes BOS, even in the presence of concurrent infection

Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms