Publications

3 Publications matching the given criteria: (Clear all filters)

Abstract (Expand)

We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. Our method is deterministic, fully automated, externally repeatable, independent on training data and -- in difference to most commercial software kits -- completely documented. Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Subsampling within tumor subregions is possible with results almost identical to full sampling. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels, thus indicating that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

Authors: Marcus Wagner, René Hänsel, Sarah Reinke, Julia Richter, Michael Altenbuchinger, Ulf-Dietrich Braumann, Rainer Spang, Markus Löffler, Wolfram Klapper

Date Published: No date defined

Publication Type: Not specified

Human Diseases: diffuse large B-cell lymphoma

Abstract (Expand)

A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential. Provided image data comprise a) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at four channels corresponding to CD14, CD163, Pax5 and DAPI; b) cartoon-filtered versions of these images, generated by Rudin-Osher-Fatemi (ROF) denoising; c) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel, and d) automatically generated segmentation masks for macrophages, B-cells and the total of cell nuclei, using information from CD14, CD163, Pax5 and DAPI channels, respectively.

Authors: Marcus Wagner, Sarah Reinke, René Hänsel, Wolfram Klapper, Ulf-Dietrich Braumann

Date Published: 12th Mar 2020

Publication Type: Journal article

Human Diseases: diffuse large B-cell lymphoma

Abstract (Expand)

BACKGROUND: The MInT study was the first to show improved 3-year outcomes with the addition of rituximab to a CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone)-like regimen in young patients with good-prognosis diffuse large-B-cell lymphoma. Extended follow-up was needed to establish long-term effects. METHODS: In the randomised open-label MInT study, patients from 18 countries (aged 18-60 years with none or one risk factor according to the age-adjusted International Prognostic Index [IPI], stage II-IV disease or stage I disease with bulk) were randomly assigned to receive six cycles of a CHOP-like chemotherapy with or without rituximab. Bulky and extranodal sites received additional radiotherapy. Randomisation was done centrally with a computer-based tool and was stratified by centre, bulky disease, age-adjusted IPI, and chemotherapy regimen by use of a modified minimisation algorithm that incorporated a stochastic component. Patients and investigators were not masked to treatment allocation. The primary endpoint was event-free survival. Analyses were by intention to treat. This observational study is a follow-up of the MInT trial, which was stopped in 2003, and is registered at ClinicalTrials.gov, number NCT00400907. FINDINGS: The intention-to-treat population included 410 patients assigned to chemotherapy alone and 413 assigned to chemotherapy plus rituximab. After a median follow-up of 72 months (range 0.03-119), 6-year event-free survival was 55.8% (95% CI 50.4-60.9; 166 events) for patients assigned to chemotherapy alone and 74.3% (69.3-78.6; 98 events) for those assigned to chemotherapy plus rituximab (difference between groups 18.5%, 11.5-25.4, log-rank p<0.0001). Multivariable analyses showed that event-free survival was affected by treatment group, presence of bulky disease, and age-adjusted IPI and that overall survival was affected by treatment group and presence of bulky disease only. After chemotherapy and rituximab, a favourable subgroup (IPI=0, no bulk) could be defined from a less favourable subgroup (IPI=1 or bulk, or both; event-free survival 84.3% [95% CI 74.2-90.7] vs 71.0% [65.1-76.1], log-rank p=0.005). 18 (4.4%, 95% CI 2.6-6.9) second malignancies occurred in the chemotherapy-alone group and 16 (3.9%, 2.2-6.2) in the chemotherapy and rituximab group (Fisher's exact p=0.730). INTERPRETATION: Rituximab added to six cycles of CHOP-like chemotherapy improved long-term outcomes for young patients with good-prognosis diffuse large-B-cell lymphoma. The definition of two prognostic subgroups allows a more refined therapeutic approach to these patients than does assessment by IPI alone. FUNDING: Hoffmann-La Roche.

Authors: M. Pfreundschuh, E. Kuhnt, L. Trumper, A. Osterborg, M. Trneny, L. Shepherd, D. S. Gill, J. Walewski, R. Pettengell, U. Jaeger, P. L. Zinzani, O. Shpilberg, S. Kvaloy, P. de Nully Brown, R. Stahel, N. Milpied, A. Lopez-Guillermo, V. Poeschel, S. Grass, M. Loeffler, N. Murawski

Date Published: 24th Sep 2011

Publication Type: Not specified

Human Diseases: non-Hodgkin lymphoma, diffuse large B-cell lymphoma

Powered by
(v.1.13.0-master)
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies