The periconceptional period, encompassing the embryonic phase, is a critical window where a majority of reproductive failures, pregnancy complications, and adverse pregnancy outcomes arise. The Carnegie staging system comprises 23 stages which are based on embryonic morphological development. This allows for the assessment of normal and abnormal embryonic development during this critical period. In-utero Carnegie staging using three-dimensional (3D) ultrasound scans visualized with virtual reality offers valuable insights but is currently a time-consuming manual process. To address this, we propose a deep learning approach for Carnegie staging in 3D ultrasound scans.

We used a dataset comprising 1413 3D ultrasound scans from the Rotterdam Periconceptional Cohort, annotated with Carnegie stages spanning from stages 13 to 23, including fetal subjects. Various training strategies were explored. We compared a metric regression approach, which considers the ordered nature of the Carnegie stages by treating the Carnegie stages as a continuous variable, with a multi-class classification approach, treating stages as independent categories. Additionally, we evaluated the influence of using a loss function accommodating the categorical nature of the Carnegie stages in the metric regression approach and examined the impact of incorporating embryonic size in the model input. Ultimately, a regression approach using the Mean Squared Error (MSE) loss function emerged as the optimal choice.

This model achieved a classification accuracy of 0.59 and a Root Mean Squared Error (RMSE) of 0.62 on the test set. This performance is comparable to an intermediate human rater, which achieved an accuracy of 0.63 and a RMSE of 0.65. Our findings represent a significant step towards the development of an automated Carnegie staging method, offering the potential for a more comprehensive evaluation of the critical embryonic phase in the clinic.

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.

Hypertrophic cardiomyopathy (HCM) is known as a frequent, genetic cardiovascular disease, often caused by mutations of sarcomere protein genes. HCM is primarily characterized by the presence of an increased left ventricular wall thickness, i.e. left ventricular hypertrophy (LVH). However, the disease appears to be asymptomatic in some patients, which makes it a diagnostic challenge. Mutation carriers of HCM who have not yet developed LVH are called genotype-positive left ventricular hypertrophy-negative (G+/LVH-) patients. The primary aim of this study was to investigate whether a radiomics model is able to distinguish between G+/LVH- patients and healthy controls, based on cardiac magnetic resonance (CMR) images. In total three datasets are analysed. A development dataset was used to develop different radiomics models and to evaluate the performance of the models. The models were validated on both the prospective validation dataset and external validation dataset. G+/LVH- patients had to be known to carry a class 4 (likely pathogenic) or class 5 (pathogenic) gene mutation for HCM and a maximum left ventricular wall thickness of <13mm. Endocardial and epicardial borders were manually and automatically segmented on long-axis view (2-chamber (2CH), 3-chamber (3CH), and 4-chamber (4CH)) and short-axis (SA) view in both end-diastolic (ED) and end-systolic (ES) phase. From these segmentation 555 features including shape, intensity and texture were extracted. Evaluation of radiomics models was performed through a 100x stratified random-split cross-validation in development dataset. Next, the models were validated on prospective validation dataset and external validation dataset. The radiomics model with best performance developed on development dataset had a mean area under the receiver operating characteristic curve (AUC) of 0.89. A similar performance in prospective validation was found (mean AUC of 0.89), while a lower performance was found in external validation dataset (mean AUC of 0.63). In addition, the radiomics models performed with automatic segmentation showed all a decrease in performance; mean AUC of 0.75, 0.77 and 0.50 in development dataset, prospective validation dataset and external validation dataset, respectively. Our radiomics models using CMR images can non-invasively distinguish between +/LVH- patients and healthy controls on both development dataset and prospective validation dataset. However, it was not able to distinguish on external validation dataset.

Patients with 1p/19q co-deleted low grade glioma (LGGs) have better prognosis and react better to certain treatments than patients with intact 1p/19q LGG. Currently, information about the 1p/19q co-deletion status is obtained by means of an invasive procedure called biopsy. As an alternative, non-invasive techniques to extract this information from medical images are being studied. Recent research suggests that local binary patterns (LBPs), a textural image descriptor, are an important feature which can predict the 1p/19q co-deletion from MRI scans. In this project we report the effect of including LBP information in a convolutional neural network (CNN) to predict the 1p/19q co-deletion status in patients suffering from a presumed LGG using pre-operative MRI scans. A combination of convolutional filters was designed and included in the CNN, resulting into local binary convolutional neural networks (LBCNNs). Three LBP descriptors, each of them representing a different textural scale, were studied, as well as the combination of the three. A default CNN without LBPs was also studied. To validate the designed filters and to study more sophisticated LBPs images like the uniform LBPs, pre-computed LBP images were directly input to the CNN. An in-house multi-institution MRI dataset consisting of 284 patients who had undergone a biopsy or resection before the treatment, and with available pre-operative T1-weighted post contrast and T2-weighted scans was used to train the different network architectures. An independent dataset consisting of 129 patients was used to validate the results. The performance of the LBCNNs was compared to the performance of the CNN. The performance of the CNN and LBCNNs was similar, reporting an area under the receiver operating characteristic curve (AUC) ranging from 0.816 to 0.872 for the different architectures. These findings suggest that the CNN can extract information relative to LBPs by itself. In addition, pre-computed uniform LBPs report similar metrics (AUC: 0.819), suggesting that they do not add new information.