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Automated HER2 Scoring of Breast Cancer Tissue using Upconverting Nanoparticle Images

Belfrage, Adam LU and Wik, Alexander LU (2022) In Master's Theses in Mathematical Sciences FMAM05 20221
Mathematics (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:
author
Belfrage, Adam LU and Wik, Alexander LU
supervisor
organization
course
FMAM05 20221
year
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}},
}