Circulating microRNA profiling in patients with advanced non-squamous NSCLC receiving bevacizumab/erlotinib followed by platinum-based chemotherapy at progression (SAKK 19/05).

OBJECTIVES Molecular subclassification of non small-cell lung cancer (NSCLC) is essential to improve clinical outcome. This study assessed the prognostic and predictive value of circulating micro-RNA (miRNA) in patients with non-squamous NSCLC enrolled in the phase II SAKK (Swiss Group for Clinical Cancer Research) trial 19/05, receiving uniform treatment with first-line bevacizumab and erlotinib followed by platinum-based chemotherapy at progression. MATERIALS AND METHODS Fifty patients with baseline and 24 h blood samples were included from SAKK 19/05. The primary study endpoint was to identify prognostic (overall survival, OS) miRNA's. Patient samples were analyzed with Agilent human miRNA 8x60K microarrays, each glass slide formatted with eight high-definition 60K arrays. Each array contained 40 probes targeting each of the 1347 miRNA. Data preprocessing included quantile normalization using robust multi-array average (RMA) algorithm. Prognostic and predictive miRNA expression profiles were identified by Spearman's rank correlation test (percentage tumor shrinkage) or log-rank testing (for time-to-event endpoints). RESULTS Data preprocessing kept 49 patients and 424 miRNA for further analysis. Ten miRNA's were significantly associated with OS, with hsa-miR-29a being the strongest prognostic marker (HR=6.44, 95%-CI 2.39-17.33). Patients with high has-miR-29a expression had a significantly lower survival at 10 months compared to patients with a low expression (54% versus 83%). Six out of the 10 miRNA's (hsa-miRN-29a, hsa-miR-542-5p, hsa-miR-502-3p, hsa-miR-376a, hsa-miR-500a, hsa-miR-424) were insensitive to perturbations according to jackknife cross-validation on their HR for OS. The respective principal component analysis (PCA) defined a meta-miRNA signature including the same 6 miRNA's, resulting in a HR of 0.66 (95%-CI 0.53-0.82). CONCLUSION Cell-free circulating miRNA-profiling successfully identified a highly prognostic 6-gene signature in patients with advanced non-squamous NSCLC. Circulating miRNA profiling should further be validated in external cohorts for the selection and monitoring of systemic treatment in patients with advanced NSCLC.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3D imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic Resonance Imaging (MRI) is nowadays utilized in clinical practice for the diagnosis confirmation and treatment planning of retinoblastoma in infants, where it serves as a source of information, complementary to the Fundus or Ultrasound imaging. Here we present a framework to fully automatically segment the eye anatomy in the MRI based on 3D Active Shape Models (ASM), we validate the results and present a proof of concept to automatically segment pathological eyes. Material and Methods: Manual and automatic segmentation were performed on 24 images of healthy children eyes (3.29±2.15 years). Imaging was performed using a 3T MRI scanner. The ASM comprises the lens, the vitreous humor, the sclera and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens and the optic nerve, then aligning the model and fitting it to the patient. We validated our segmentation method using a leave-one-out cross validation. The segmentation results were evaluated by measuring the overlap using the Dice Similarity Coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90±2.12% for the sclera and the cornea, 94.72±1.89% for the vitreous humor and 85.16±4.91% for the lens. The mean distance error was 0.26±0.09mm. The entire process took 14s on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor and the lens using MRI. We additionally present a proof of concept for fully automatically segmenting pathological eyes. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

Monitoring of tumor response to Cisplatin using optical spectroscopy

INTRODUCTION Anatomic imaging alone is often inadequate for tuning systemic treatment for individual tumor response. Optically based techniques could potentially contribute to fast and objective response monitoring in personalized cancer therapy. In the present study, we evaluated the feasibility of dual-modality diffuse reflectance spectroscopy-autofluorescence spectroscopy (DRS-AFS) to monitor the effects of systemic treatment in a mouse model for hereditary breast cancer. METHODS Brca1(-/-); p53(-/-) mammary tumors were grown in 36 mice, half of which were treated with a single dose of cisplatin. Changes in the tumor physiology and morphology were measured for a period of 1 week using dual-modality DRS-AFS. Liver and muscle tissues were also measured to distinguish tumor-specific alterations from systemic changes. Model-based analyses were used to derive different optical parameters like the scattering and absorption coefficients, as well as sources of intrinsic fluorescence. Histopathologic analysis was performed for cross-validation with trends in optically based parameters. RESULTS Treated tumors showed a significant decrease in Mie-scattering slope and Mie-to-total scattering fraction and an increase in both fat volume fraction and tissue oxygenation after 2 days of follow-up. Additionally, significant tumor-specific changes in the fluorescence spectra were seen. These longitudinal trends were consistent with changes observed in the histopathologic analysis, such as vital tumor content and formation of fibrosis. CONCLUSIONS This study demonstrates that dual-modality DRS-AFS provides quantitative functional information that corresponds well with the degree of pathologic response. DRS-AFS, in conjunction with other imaging modalities, could be used to optimize systemic cancer treatment on the basis of early individual tumor response.

FACTS: Fully Automatic CT Segmentation of a Hip Joint

Extraction of surface models of a hip joint from CT data is a pre-requisite step for computer assisted diagnosis and planning (CADP) of periacetabular osteotomy (PAO). Most of existing CADP systems are based on manual segmentation, which is time-consuming and hard to achieve reproducible results. In this paper, we present a Fully Automatic CT Segmentation (FACTS) approach to simultaneously extract both pelvic and femoral models. Our approach works by combining fast random forest (RF) regression based landmark detection, multi-atlas based segmentation, with articulated statistical shape model (aSSM) based fitting. The two fundamental contributions of our approach are: (1) an improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the multi-atlas based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 6-fold cross validation. When the present approach was compared to manual segmentation, a mean segmentation accuracy of 0.40, 0.36, and 0.36 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. When the models derived from both segmentations were used to compute the PAO diagnosis parameters, a difference of 2.0 ± 1.5°, 2.1 ± 1.6°, and 3.5 ± 2.3% were found for anteversion, inclination, and acetabular coverage, respectively. The achieved accuracy is regarded as clinically accurate enough for our target applications.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

Modern diatom, cladocera, chironomid, and chrysophyte cyst assemblages as quantitative indicators for the reconstruction of past environmental conditions in the Alps. II. Nutrients

Surface sediments from 68 small lakes in the Alps and 9 well-dated sediment core samples that cover a gradient of total phosphorus (TP) concentrations of 6 to 520 μg TP l-1 were studied for diatom, chrysophyte cyst, cladocera, and chironomid assemblages. Inference models for mean circulation log10 TP were developed for diatoms, chironomids, and benthic cladocera using weighted-averaging partial least squares. After screening for outliers, the final transfer functions have coefficients of determination (r2, as assessed by cross-validation, of 0.79 (diatoms), 0.68 (chironomids), and 0.49 (benthic cladocera). Planktonic cladocera and chrysophytes show very weak relationships to TP and no TP inference models were developed for these biota. Diatoms showed the best relationship with TP, whereas the other biota all have large secondary gradients, suggesting that variables other than TP have a strong influence on their composition and abundance. Comparison with other diatom – TP inference models shows that our model has high predictive power and a low root mean squared error of prediction, as assessed by cross-validation.

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.