Deep learning is a growing field of research and and so is the application of deep learning to the analysis of medical images. Convolutional neural networks are used to diagnose diseases, determine risk of disease development, finding the exact area of abnormalities and and so on. Mammography is an imaging technique, which aims at the early detection of breast cancer. Invasive Lobular Carcinoma (ILC) is a type of breast cancer with properties that make it less visible on mammography. This study compares six models which apply convolutional neural networks to detect breast cancer, on its ability to detect ILC. Furthermore, transfer learning with ILC and healthy images is applied to one of these models to improve the performance on ILC data. For the evaluated breast cancer detection models, the performance on ILC data is worse than for a dataset which includes all breast cancer types and more healthy images. The model that transfer learning is applied to performs better on ILC data after transfer learning than before, with an increase of 0.09 for the AUC value. Additional analyses of the results show that women with high breast density have a lower chance of getting a correct ILC diagnosis from the model than women with low beast density and this also holds for other types of breast cancer. Lastly, the model outcomes are compared to radiologist reviews, to determine the additional value of models to the routine screening performed by radiologists. Within the images that are labeled as healthy by the radiologists, a model could be applied to detect tumor that have been missed by radiologists. When a specificity of 89% was allowed, 23% of the missed tumors could have been detected by the original GMIC model. In this way, the models used in this study and other deep learning models that are in development now, can contribute to breast cancer detection from mammography, and ILC specifically.

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.