| 1. |
- Isheden, Gabriel
(författare)
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Statistical models of breast cancer tumour growth and spread
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, we develop statistical methods for studying tumour growth and metastatic lymph node spread in breast cancer. The methods can be used for analysing breast cancer disease progression before diagnosis. They may be used to answer questions such as: For how long does a tumour grow inside of the body before it is detected? How much will the tumour metastasise in the lymph nodes before detection? Or, which women have a high risk of missing breast cancer at mammography screening? These questions are important for studying the effects of mammography screening at an individual level. We work in a framework called continuous growth models. This is an alternative to Markov models, which is the most commonly used approach for modelling breast cancer disease progression. Standard Markov models assume that all women in each disease state are identical, making the model easy to implement and practically useful. Unfortunately, women with breast cancer are not identical, and relaxing this assumption quickly increases the Markov model’s complexity. Continuous growth models are instead more complex at the outset. However, as the number of clinical factors increase, continuous growth models become more flexible and less complex than Markov models. In Study I, we focus on a continuous growth process used for modelling tumour volume at diagnosis. We provide a detailed description of the so called Stable Disease Assumptions that are used for continuous growth modelling. We use them to derive new theoretical results for the model. These are then integrated into our growth model, which helps to simplify and reduce computational complexity of the model. With these results, we were able to greatly reduce the computational time needed for estimating growth parameters. The theoretical results derived in Study I are further used in Studies II and III. In Study II, we extend our continuous growth model to also include a sub-model for metastatic lymph node spread. The result is a joint model of tumour volume and number of lymph node metastases at diagnosis, conditional on mammography screening history and mammographic density. When applied on empirical data on 1860 incident invasive breast cancer cases, our model provides a dramatically better fit than other models in current use. Furthermore, we show that our sub-model of lymph node spread can be estimated independently of the tumour growth process. This property forms the first part of the theoretical basis for study IV. In Study II, we use the property to validate our lymph node spread model on an independent data set, consisting of 3961 women diagnosed with invasive breast cancer. In Study III, we show how to study the effect of other clinical factors on metastatic lymph node spread. Our approach is to regress clinical factors on the proportionality constant in our model for lymph node spread. We illustrate our method by studying the association between hormone replacement therapy (HRT) and tumour growth rate and rate of lymph node spread. Using data from 1631 women diagnosed with breast cancer, we estimate that women using HRT have a 36% lower rate of lymph node spread than non-users (95% confidence interval: 58% to 8%). This can be contrasted with the effect of HRT on tumour growth rate. We estimate growth rates to be 15% slower in HRT users (p = 0.16). We also derive theoretical distributions for metastatic lymph node spread at future points in time. We use them to illustrate the potential consequences of false negative screens, in terms of lymph node spread. In Study IV, we use the method developed in Study III to study the association between clinical factors and rate of breast cancer lymph node spread. We use data on 10950 women to study the associations with grade, estrogen receptor (ER) status, progesteron receptor (PR) status, molecular subtype, and a polygenic risk score. We found that grade 2 and 3 tumours, respectively, were associated with 1.63 and 2.17 times faster rates of lymph node spread than grade 1 tumours (p < 10^-16). ER negative breast cancer was associated with a 1.25 times faster spread than ER positive and PR negative breast cancer was associated with a 1.19 times faster spread than PR positive cancer (p = 0.0011, p = 0.0012, respectively). Her2-enriched breast cancer was associated with a 1.53 times faster spread than luminal A cancer (p = 0.00072).
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| 2. |
- Karlgren, Jussi, et al.
(författare)
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Semantic Topology
- 2014
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Ingår i: Proceedings of the 23d ACM international conference on Conference on information & knowledge management (CIKM '14). - New York : Association for Computing Machinery (ACM). - 9781450325981 ; , s. 1939-1942
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Konferensbidrag (refereegranskat)abstract
- A reasonable requirement (among many others) for a lexical or semantic component in an information system is that it should be able to learn incrementally from the linguistic data it is exposed to, that it can distinguish between the topical impact of various terms, and that it knows if it knows stuff or not.We work with a specific representation framework – semantic spaces – which well accommodates the first requirement; in this short paper, we investigate the global qualities of semantic spaces by a topological procedure – mapper – which gives an indication of topical density of the space; we examine the local context of terms of interest in the semantic space using another topologically inspired approach which gives an indication of the neighbourhood of the terms of interest. Our aim is to be able to establish the qualities of the semantic space under consideration without resorting to inspection of the data used to build it.
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| 3. |
- Strandberg, Rickard, et al.
(författare)
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Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study
- 2023
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.
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