Background: Histopathological growth patterns (HGP) are a biomarker for predicting survival and systemic treatment effectiveness in colorectal liver metastasis (CRLM). Currently, HGP assessment in CRLM requires the resection specimen. Predicting the HGP from preoperative medical imaging could allow more personalised care and better outcomes. Methods: 252 patients underwent CRLM resection between 2004 and 2018 without receiving any systemic treatment. Patients were characterised as having either pure desmoplastic growth (dHGP) or any other type of growth pattern combination (non-dHGP) (21% dHGP; 79% non-dHGP). These categories were chosen because pure desmoplastic growth is predictive of better overall survival. regions of interest were automatically extracted using a UNet based segmentation model. These ROIs were passed to a radiomics model and a deep learning model to classify between dHGP/non-dHGP and predict the fraction of dHGP. Results: The best-performing classification method was the radiomic approach achieving an AUC of 0.67 (95% CI: 0.58-0.78), whereas the best-performance deep learning model achieved an average AUC value of 0.59 (95% CI: 0.53-0.65). Additionally, regression predicting the fraction of dHGP failed, with the predicted values showing no significant correlation with the actual value. Conclusions: Radiomics can be used to assess HGP, however further improvements in predictive performance are needed before these methods can be applied.

Primary liver cancer is a commonly diagnosed cancer and accurate diagnosis is crucial for treatment planning. To differentiate between malignant and benign liver tumors, contrast-enhanced MRI is typically used as it provides information over multiple contrast phases. However, diagnosis based on MRI is challenging. In this study, automatic classification is used to distinguish common primary liver tumors.

Imaging data from 102 patients with malignant (hepatocellular carcinoma) and benign (focal nodular hyperplasia and hepatocellular adenoma) primary liver tumors was used for binary classification through radiomics and deep learning approaches. The radiomics method was applied with the use of the open-source toolbox WORC. The deep learning model was based on the ResNet-10 architecture. The data input consisted of individual and combined phases of contrast-enhanced T1-weighted and T2-weighted MRI.

The highest performance values were found for the radiomics approach that combined the precontrast, arterial, portal venous, and delayed contrast phases together with T2-weighted MRI, with an AUC of 0.92. The deep learning model scored an AUC of 0.83 with this data input, however substantial overfitting occurred due to the limited sample size.

In conclusion, the radiomics classifiers based on combined contrast-enhanced T1-weighted and T2-weighted MRI can differentiate malignant from benign primary liver tumors with limited data samples. The classification task is too complex with the given data when using a ResNet-10 model and should be applied to an extended dataset.

Purpose Manufacturer’s predictions of ablation zone dimensions are the current directives for treatment planning in thermal ablation, while they are mostly based on ex vivo experiments making its reliability questionable. The aim of this study is to determine the correspondence in dimensions, volume, shape and overlap of the manufactures’ predicted with the clinically realized ablation zones following thermal ablation of hepatocellular carcinoma (HCC). The secondary objective is to determine the effect of tumor- and liver characteristics on this correspondence.
Methods Data was retrospectively collected from two prospective studies. A registered pre-ablation and post-ablation computed tomography scan with liver, tumor and ablation zone segmentations were available for analysis. Needle position reconstruction was performed based on image visual assessment (e.g., gas formation, tumor location and subcapsular hemorrhage) using in-house developed software. The dimensions of the predicted ablation zone were derived from the manufacturer’s chart corresponding to treatment settings used during ablation. The long axis diameter (LAD), short axis diameter (SAD) and volume of the realized and predicted ablation zone were compared. The overlap was determined using the Dice similarity coefficient (DSC) and the average surface deviation between the realized and predicted ablation zone. The effect of tumor location, vascular proximity and liver cirrhosis on the overlap was quantified using the DSC and average surface deviation.
Results Nineteen patients and 21 ablations were included for analysis. The median realized volume did not significantly differ from the predicted volume, 25.7 cm3 and 22.6 cm3 respectively (p = 0.526). The median LAD and SAD of the realized ablation zone differed significantly from the manufacturer’s prediction (51.9 mm vs 40.0 mm, p<0.001 and 36.9 mm vs 35.0 mm, p<0.001 respectively). The predicted ablation zone corresponds to the realized ablation zone with a mean DSC of 0.73 and mean average surface deviation of 3.04 mm. Tumor location and vascular proximity did not affect the overlap between the realized and predicted ablation zone. The effect of liver cirrhosis was not assessed due to a low sample size of HCC in a non-cirrhotic liver (n=2).
Conclusion The manufacturer’s predicted volume of liver ablation zones corresponds well to the clinically realized ablation volume. However, the LAD and SAD are underestimated by the manufacturers. The shape and overlap of the predicted and realized ablation zone were sufficient. Further studies evaluating the effect of tumor- and liver characteristics on the correspondence of the predicted with the realized ablation zone with a larger patient cohort is needed.

Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML) algorithm called Bayesian Optimization with HyperBand (BOHB). We found several hyperparameters significantly influencing the performance and confirmed that BOHB is applicable to these kind of problems. However, we did not find a significant difference between BOHB and Random Search on a small search, stressing the need of matching the search space to the search strategy. Finally, our approach was only slightly worse than the current state-of-the-art, which shows that AutoML has potential in this field.

Glioma is a kind of slow-growing brain tumor which may result in severe seizures. Currently a major tool used to detect and diagnose the glioma is MRI scan. To better analyze the medical image, segmentation is usually conducted as a basic step for further processing, which partitions an integrate image into multiple physically meaningful regions by annotating objects and boundaries. Deep learning based segmentation methods have attracted significant interest due to their high efficiency and strong generalization ability. With the increasing demands of high-quality segmentation of bio-tissues in medical region, plenty of innovative approaches were proposed to expand the boundary of segmentation capability of deep learning models by taking the spatial or temporal constraints of bio-structure into consideration. Although longitudinal segmentation in 2D natural image sequences has made a lot of success, the potential of deep learning network in segmenting a series of chronological 3DMRI images in terms of improving consistency remains unclear. This thesis aims to investigate whether deep learning models are able to increase segmentation accuracy as well as consistency in longitudinal 3D images, specifically focusing on introducing Recurrent Neural Network(RNN) to 3D Convolutional Neural Network(CNN) for 4D segmentation. In addition to the implementation of several U-Net variants as CNN backbone, three types of longitudinal connection strategies are proposed. A hierarchical workflow is followed to create the optimal version of longitudinal network based on combining multiple CNN variants and connection strategies. The evaluation of the 4D network shows that segmentation accuracy of the longitudinal model is limited by its CNN backbone and temporal information can partially improve the segmentation consistency with regard to maintaining the highest proportion of normal tissue unchangeable over time.