Automated HER2 Scoring of Breast Cancer Tissue using Upconverting Nanoparticle Images
(2022) In Master's Theses in Mathematical Sciences FMAM05 20221Mathematics (Faculty of Engineering)
- Abstract
- Computer aided pathology is becoming more and more of a requirement within pathology
due to increased demand of individualised treatments and personalised medicine. Because
of the advance of digital pathology in recent years, where a high resolution camera acquire
images of microscope slides, pathologists can now assess tissue samples in digital images.
This has enabled automatic assessment of pathological images. A specific area of interest is
the quantification of HER2-receptors in breast cancer tissue which decides if targeted therapy
can be used or not. The standard staining within the area obstructs cell morphology and
the images are difficult to analyse and classify. Existing automated HER2-classification
methods in the field... (More) - Computer aided pathology is becoming more and more of a requirement within pathology
due to increased demand of individualised treatments and personalised medicine. Because
of the advance of digital pathology in recent years, where a high resolution camera acquire
images of microscope slides, pathologists can now assess tissue samples in digital images.
This has enabled automatic assessment of pathological images. A specific area of interest is
the quantification of HER2-receptors in breast cancer tissue which decides if targeted therapy
can be used or not. The standard staining within the area obstructs cell morphology and
the images are difficult to analyse and classify. Existing automated HER2-classification
methods in the field rely heavily on colour consistency or are neural networks which are
difficult to interpret. Lumito AB has developed a reagent kit that, via laser and upconverting
nanoparticles, demonstrates the HER2-expression in separate images that does not interfere
with cell-morphology. These images are potentially more suitable for traditional image
analysis and could potentially enable the possibility to develop simple, fast and interpretable
algorithms that could quantify the HER2-expression and classify tissue samples. In this
project, two algorithms were developed for classification of the upconverting nanoparticle
based images. They were considered to be simple in the sense that the bases of classifications
would be easy to explain to a pathologist due to the fact that they were inspired by the
guidelines that pathologists use for HER2-classification. The algorithms performed on par
with a pathologist and could be used as a screening tool, reducing the pathologist’s workload.
The algorithms were also accurate in classification of the HER2 positive and equivocal tissue
samples but fail to classify these unambiguously, and a pathologist would still have to assess
these samples manually. It is difficult to say how well the algorithms performed in reality
due to the relatively small data-set. This project should be seen as a proof of concept and
future work would have to be done to further validate and improve the results even though
the start is promising. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9086647
- author
- Belfrage, Adam LU and Wik, Alexander LU
- supervisor
-
- Ida Arvidsson LU
- Klas Berggren LU
- organization
- course
- FMAM05 20221
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- HER2-scoring, image analysis, interpretability, digital pathology, computer aided pathology, whole slide imaging, ASCO-guidelines, singular value decomposition, shape models, Bayesian classification
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3471-2022
- ISSN
- 1404-6342
- other publication id
- 2022:E22
- language
- English
- id
- 9086647
- date added to LUP
- 2022-08-10 18:29:36
- date last changed
- 2022-08-10 18:29:36
@misc{9086647, abstract = {{Computer aided pathology is becoming more and more of a requirement within pathology due to increased demand of individualised treatments and personalised medicine. Because of the advance of digital pathology in recent years, where a high resolution camera acquire images of microscope slides, pathologists can now assess tissue samples in digital images. This has enabled automatic assessment of pathological images. A specific area of interest is the quantification of HER2-receptors in breast cancer tissue which decides if targeted therapy can be used or not. The standard staining within the area obstructs cell morphology and the images are difficult to analyse and classify. Existing automated HER2-classification methods in the field rely heavily on colour consistency or are neural networks which are difficult to interpret. Lumito AB has developed a reagent kit that, via laser and upconverting nanoparticles, demonstrates the HER2-expression in separate images that does not interfere with cell-morphology. These images are potentially more suitable for traditional image analysis and could potentially enable the possibility to develop simple, fast and interpretable algorithms that could quantify the HER2-expression and classify tissue samples. In this project, two algorithms were developed for classification of the upconverting nanoparticle based images. They were considered to be simple in the sense that the bases of classifications would be easy to explain to a pathologist due to the fact that they were inspired by the guidelines that pathologists use for HER2-classification. The algorithms performed on par with a pathologist and could be used as a screening tool, reducing the pathologist’s workload. The algorithms were also accurate in classification of the HER2 positive and equivocal tissue samples but fail to classify these unambiguously, and a pathologist would still have to assess these samples manually. It is difficult to say how well the algorithms performed in reality due to the relatively small data-set. This project should be seen as a proof of concept and future work would have to be done to further validate and improve the results even though the start is promising.}}, author = {{Belfrage, Adam and Wik, Alexander}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Automated HER2 Scoring of Breast Cancer Tissue using Upconverting Nanoparticle Images}}, year = {{2022}}, }