| dc.contributor.advisor | Middel, Peter PD Dr. | |
| dc.contributor.author | Burchhardt, Judith | |
| dc.date.accessioned | 2022-11-18T10:28:25Z | |
| dc.date.available | 2022-11-29T00:50:10Z | |
| dc.date.issued | 2022-11-18 | |
| dc.identifier.uri | http://resolver.sub.uni-goettingen.de/purl?ediss-11858/14348 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-9552 | |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 610 | de |
| dc.title | Image analysis of immunohistochemistry-based biomarkers in breast cancer | de |
| dc.type | doctoralThesis | de |
| dc.contributor.referee | Middel, Peter PD Dr. | |
| dc.date.examination | 2022-11-21 | de |
| dc.description.abstracteng | Breast cancer is the leading form of cancer in women in Germany with about 69.000 new cases
anually and a lifetime risk of 12.9%. One in eight women will develop a malignant neoplasm of the
breast. Early diagnosis measures include regular clinical examinations, mammography and
biopsies of suspicious lesions. Definite diagnosis is based on histopathology. Pathologic work-up
encompasses histomorphology on H&E stained slides and a set of four biomarkers that provide
prognostic information about the expected course of disease and predictive information about
the likeliness to benefit from different clinical treatments. In breast cancer, immunohistochemistry is currently the most common type of biomarker. Immunohistochemistry conventionally relies on manual histological interpretation, but automated techniques based on image analysis have become increasingly available.
In the present study, n = 613 breast cancer core needle biopsies from a single pathological
laboratory (Pathologie Nordhessen, Kassel, Germany) were re-analysed by whole slide scanning of
the histological specimens and image analysis of the biomarkers estrogen receptor, progesterone
receptor, Her2 receptor and Ki-67 by the software package QuantCenter (3D Histech). The results
were compared to manual biomarker interpretation by board-certified pathologists.
Digitisation of the histological slides by a state-of-the-art tile scanner (3D Histech Pannoramic
P250 Flash II) required 82 seconds per slide on average (standard deviation: ± 38s) and seemed
technically mature. Allocation and storage of the large files constitute major issues that require
costumised solutions. Image analysis did not work with out-of-the-box settings but required
optimisation on local cases. After training of the software, satisfying rates of concordance were
achived for estrogen and progesterone receptors with Cohen's kappa coefficients of κ = 0.86 and
κ = 1.0. In Ki-67, systematic differences between manual scoring and image analysis were noticed
and the best concordance achieved was κ = 0.68. Her2 yielded a good concordance of κ = 0.74 in a
training set of n = 19 representative cases but only a moderate concordance of κ = 0.55 in the
complete cohort. Exploratory analysis of Her2 yielded additional information on the physical basis
of manual Her2 scoring.
The findings indicate that image analysis is a mature technique that can be used to supplement
the analysis of biomarkers in breast cancer. Image analysis has potential to decrease
interobserver variance and to allow more precise quantitation. Yet, current software approaches
require specific optimisation on local cases. The achieved concordance results from the
representativeness of these training cases, which raises the question of how to define such
reference standards. A possible solution could be centrally defined testing materials, for example
tissue cultures with fixed levels of biomarker expression, that could be used for standardised local
optimisation. | de |
| dc.contributor.coReferee | Emons, Günter Prof. Dr. | |
| dc.contributor.thirdReferee | Sennhenn-Kirchner, Sabine PD Dr. | |
| dc.subject.eng | image analysis | de |
| dc.subject.eng | breast cancer | de |
| dc.subject.eng | immunohistochemistry (IHC) | de |
| dc.subject.eng | biomarkers | de |
| dc.subject.eng | magnification rule | de |
| dc.subject.eng | digital pathology (DP) | de |
| dc.subject.eng | digital image analysis (DIA) | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-ediss-14348-0 | |
| dc.affiliation.institute | Medizinische Fakultät | de |
| dc.subject.gokfull | Pathologie / Pathologische Anatomie / Histopathologie / Zytopathologie - Allgemein- und Gesamtdarstellungen (PPN619875674) | de |
| dc.description.embargoed | 2022-11-29 | de |
| dc.identifier.ppn | 1822992265 | |
| dc.notes.confirmationsent | Confirmation sent 2022-11-18T10:45:02 | de |