Dynamics of the intratumoral immune response during progression of high-grade serous ovarian cancer

PURPOSE: Tumor-infiltrating lymphocytes (TILs) have an established impact on the prognosis of high-grade serous ovarian carcinoma (HGSOC), however, their role in recurrent ovarian cancer is largely unknown. We therefore systematically investigated TIL densities and MHC class I and II (MHC1, 2) expression in the progression of HGSOC. EXPERIMENTAL DESIGN: CD3+, CD4+, CD8+ TILs and MHC1, 2 expression were evaluated by immunohistochemistry on tissue microarrays in 113 paired primary and recurrent HGSOC. TILs were quantified by image analysis. All patients had been included to the EU-funded OCTIPS FP7 project. RESULTS: CD3+, CD4+, CD8+ TILs and MHC1 and MHC2 expression showed significant correlations between primary and recurrent tumor levels (Spearman rho 0.427, 0.533, 0.361, 0.456, 0.526 respectively; P<.0001 each). Paired testing revealed higher CD4+ densities and MHC1 expression in recurrent tumors (Wilcoxon P=.034 and P=.018). There was also a shift towards higher CD3+ TILs levels in recurrent carcinomas when analyzing platinum-sensitive tumors only (Wilcoxon P=.026) and in pairs with recurrent tumor tissue from first relapse only (Wilcoxon P=.031). High MHC2 expression was the only parameter to be significantly linked to prolonged progression-free survival after first relapse (PFS2, log-rank P=.012). CONCLUSIONS: This is the first study that analyzed the development of TILs density and MHC expression in paired primary and recurrent HGSOC. The level of the antitumoral immune response in recurrent tumors was clearly dependent on the one in the primary tumor. Our data contribute to the understanding of temporal heterogeneity of HGSOC immune microenvironment and have implications for selection of samples for biomarker testing in the setting of immune-targeting therapeutics

No-reference Image Denoising Quality Assessment

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a no-reference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and denoising models alone cannot robustly rank denoising results, they often complement each other. We accordingly design denoising quality features based on these existing metrics and models and then use Random Forests Regression to aggregate them into a more powerful unified metric. Our experiments on images with various types and levels of noise show that our no-reference denoising quality assessment method significantly outperforms the state-of-the-art quality metrics. This paper also provides a method that leverages our quality assessment method to automatically tune the parameter settings of a denoising algorithm for an input noisy image to produce an optimal denoising result.Comment: 17 pages, 41 figures, accepted by Computer Vision Conference (CVC) 201

GEMS: Galaxy fitting catalogues and testing parametric galaxy fitting codes

In the context of measuring structure and morphology of intermediate redshift galaxies with recent HST/ACS surveys, we tune, test, and compare two widely used fitting codes (GALFIT and GIM2D) for fitting single-component Sersic models to the light profiles of both simulated and real galaxy data. We find that fitting accuracy depends sensitively on galaxy profile shape. Exponential disks are well fit with Sersic models and have small measurement errors, whereas fits to de Vaucouleurs profiles show larger uncertainties owing to the large amount of light at large radii. We find that both codes provide reliable fits and little systematic error, when the effective surface brightness is above that of the sky. Moreover, both codes return errors that significantly underestimate the true fitting uncertainties, which are best estimated with simulations. We find that GIM2D suffers significant systematic errors for spheroids with close companions owing to the difficulty of effectively masking out neighboring galaxy light; there appears to be no work around to this important systematic in GIM2D's current implementation. While this crowding error affects only a small fraction of galaxies in GEMS, it must be accounted for in the analysis of deeper cosmological images or of more crowded fields with GIM2D. In contrast, GALFIT results are robust to the presence of neighbors because it can simultaneously fit the profiles of multiple companions thereby deblending their effect on the fit to the galaxy of interest. We find GALFIT's robustness to nearby companions and factor of >~20 faster runtime speed are important advantages over GIM2D for analyzing large HST/ACS datasets. Finally we include our final catalog of fit results for all 41,495 objects detected in GEMS.Comment: Accepted for publication in ApJS October 2007, v172n2; 25 pages, 16 Figures, 9 Tables; for hi-resolution version, see http://www.mpia.de/homes/bhaeussl/galaxy_fitting.pdf. For results, catalogues and files for code-testing, see http://www.mpia.de/GEMS/fitting_paper.htm