LM
L. Middeldorp
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2 records found
1
Master thesis
(2022)
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L. Middeldorp, H.N. Kekkonen, G. Jongbloed, H.P. Lopuhaa, M.J. Hooning, M. Smid
When a second tumor arises in the contralateral breast in a patient with a previous or synchronous breast cancer, it is of clinical importance to determine if this tumor is a new unrelated tumor or a metastasis, i.e. clone, of the primary tumor. A new, unrelated tumor may be treated similarly as the first one since treatment was successful, while a distant metastasis demands a change of therapy and has a more adverse prognosis. In clinic, a second tumor is generally regarded as a new primary. If there is clinical suspicion that the second tumor may be a metastasis, clinico-pathological characteristics of the two tumors are used assess the clonality status. Clinico-pathological characteristics, however, are not reliable predictors to determine if a second tumor is a metastasis. Recent studies have investigated tumor clonality using techniques from molecular genetics. These models appear to perform well, but have several drawbacks.
In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values.
The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available.
The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs. ...
In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values.
The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available.
The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs. ...
When a second tumor arises in the contralateral breast in a patient with a previous or synchronous breast cancer, it is of clinical importance to determine if this tumor is a new unrelated tumor or a metastasis, i.e. clone, of the primary tumor. A new, unrelated tumor may be treated similarly as the first one since treatment was successful, while a distant metastasis demands a change of therapy and has a more adverse prognosis. In clinic, a second tumor is generally regarded as a new primary. If there is clinical suspicion that the second tumor may be a metastasis, clinico-pathological characteristics of the two tumors are used assess the clonality status. Clinico-pathological characteristics, however, are not reliable predictors to determine if a second tumor is a metastasis. Recent studies have investigated tumor clonality using techniques from molecular genetics. These models appear to perform well, but have several drawbacks.
In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values.
The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available.
The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs.
In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values.
The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available.
The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs.
The method of pairwise comparisons is a method that is commonly used in psychology to model human preferences for a set of objects. In a pairwise comparison experiment, the preferences of a sample are obtained by comparing the objects in pairs. Each individual in the sample judges every possible pair of objects and expresses which of the two object he prefers. Thurstone's Pairwise
Comparison Model can then be used on the gathered data in order to obtain rankings of the objects based on the preferences of the sample. This thesis investigates several properties of Thurstone's Pairwise Comparison Model and how it can be used to model human food preferences. In recent years, people have become more aware of the importance of eating healthy food. It may, however, be questioned whether healthy food is generally preferred by the population. This can be examined by means of Thurstone's Pairwise Comparison Model. In order to examine how the model performs, a data study has been carried out using two sets of food products. One set consisted of general food
products, while the other set was comprised of snack products. Both sets of objects consisted of both healthy and unhealthy food products. The influence of an introduction text, emphasizing the importance of healthy food, on the preferences of the individuals has been examined as well. For this, each set of food products was judged by two groups, each group receiving a different introduction text before starting the pairwise comparison experiment. The results of the data study yielded that Thurstone's Pairwise Comparison Model is more suitable for modelling preferences of humans for general food products. Furthermore, the introduction text turned out to be not of influence on the preferences of participants. ...
Comparison Model can then be used on the gathered data in order to obtain rankings of the objects based on the preferences of the sample. This thesis investigates several properties of Thurstone's Pairwise Comparison Model and how it can be used to model human food preferences. In recent years, people have become more aware of the importance of eating healthy food. It may, however, be questioned whether healthy food is generally preferred by the population. This can be examined by means of Thurstone's Pairwise Comparison Model. In order to examine how the model performs, a data study has been carried out using two sets of food products. One set consisted of general food
products, while the other set was comprised of snack products. Both sets of objects consisted of both healthy and unhealthy food products. The influence of an introduction text, emphasizing the importance of healthy food, on the preferences of the individuals has been examined as well. For this, each set of food products was judged by two groups, each group receiving a different introduction text before starting the pairwise comparison experiment. The results of the data study yielded that Thurstone's Pairwise Comparison Model is more suitable for modelling preferences of humans for general food products. Furthermore, the introduction text turned out to be not of influence on the preferences of participants. ...
The method of pairwise comparisons is a method that is commonly used in psychology to model human preferences for a set of objects. In a pairwise comparison experiment, the preferences of a sample are obtained by comparing the objects in pairs. Each individual in the sample judges every possible pair of objects and expresses which of the two object he prefers. Thurstone's Pairwise
Comparison Model can then be used on the gathered data in order to obtain rankings of the objects based on the preferences of the sample. This thesis investigates several properties of Thurstone's Pairwise Comparison Model and how it can be used to model human food preferences. In recent years, people have become more aware of the importance of eating healthy food. It may, however, be questioned whether healthy food is generally preferred by the population. This can be examined by means of Thurstone's Pairwise Comparison Model. In order to examine how the model performs, a data study has been carried out using two sets of food products. One set consisted of general food
products, while the other set was comprised of snack products. Both sets of objects consisted of both healthy and unhealthy food products. The influence of an introduction text, emphasizing the importance of healthy food, on the preferences of the individuals has been examined as well. For this, each set of food products was judged by two groups, each group receiving a different introduction text before starting the pairwise comparison experiment. The results of the data study yielded that Thurstone's Pairwise Comparison Model is more suitable for modelling preferences of humans for general food products. Furthermore, the introduction text turned out to be not of influence on the preferences of participants.
Comparison Model can then be used on the gathered data in order to obtain rankings of the objects based on the preferences of the sample. This thesis investigates several properties of Thurstone's Pairwise Comparison Model and how it can be used to model human food preferences. In recent years, people have become more aware of the importance of eating healthy food. It may, however, be questioned whether healthy food is generally preferred by the population. This can be examined by means of Thurstone's Pairwise Comparison Model. In order to examine how the model performs, a data study has been carried out using two sets of food products. One set consisted of general food
products, while the other set was comprised of snack products. Both sets of objects consisted of both healthy and unhealthy food products. The influence of an introduction text, emphasizing the importance of healthy food, on the preferences of the individuals has been examined as well. For this, each set of food products was judged by two groups, each group receiving a different introduction text before starting the pairwise comparison experiment. The results of the data study yielded that Thurstone's Pairwise Comparison Model is more suitable for modelling preferences of humans for general food products. Furthermore, the introduction text turned out to be not of influence on the preferences of participants.