Background: Incidence and mortality rates for melanoma continue to rise, necessitating improvements in early diagnostic methods. Hyperspectral Imaging (HSI) offers a rapid, accurate, and cost-effective approach that does not require contact with the skin or the use of contrast agents. Its application could potentially be enhanced even further by overcoming possible lower spatial resolution through the integration of an RGB camera into a dual-imaging system, creating super-resolution images. Furthermore, differentiating surface reflectance from the overall reflectance could potentially offer insight into the lesion’s surface, which could further aid in classification.
Aim: The objective of this project was to build a combined imaging system, consisting of an RGB and HSI cameras with multi-angle illumination, to explore the potential of super resolution imaging and surface roughness maps for automatic classification of skin lesions.
Methods: A combined imaging system was designed and developed, along with automatic acquisition software. Patients coming for consultation at the NKI Center for Early Diagnostics were eligible for study participation. A super-resolution algorithm was applied and a surface roughness model was employed to create maps related to the roughness of the skin lesions. Features of the super-resolution and surface roughness images were extracted, and relevant features were identified. The classification potential for differentiating between action-required and no-action skin lesions was assessed using AUC, sensitivity, specificity, and MCC metrics.
Results: An automated setup was developed and data from 44 patients was collected, totaling 97 lesions. Lesions were categorized into action-required (n=77) and no-action groups (n=20). Super-resolution images with enhanced spatial quality and retained spectral characteristics were created. Surface roughness maps were created for each lesion. Significant identified features included spectral bands at 450 nm (p = 0.044), 474 nm (p = 0.048), 530 nm (p = 0.015), and 538 nm (p = 0.048). Preliminary classification using all features achieved an AUC of 0.82, a sensitivity of 90%, specificity of 74.4%, and an MCC of 0.53.
Discussion: The novel data analysis approach employed in this project could potentially offer more insight into a lesion’s surface. Future efforts should focus on further optimization of the model and applying a deep learning algorithm to make use of the spatial context of pixels in the surface roughness maps and super-resolution images.
Conclusion: The integration of RGB and HSI within a combined imaging system demonstrates potential for automatic classification of skin lesions, using features derived from super-resolution images and surface roughness maps.

Gastrointestinal stromal tumours (GISTs) are rare mesenchymal neoplasms with a worldwide incidence of one or two per 100,000. The tumours affect the entire gastrointestinal tract, but most commonly the stomach and small intestine. Neoadjuvant tyrosine kinase inhibitors (TKI) are administered in a selection of GIST patients to attain size reduction of the primary tumour and improve chances of complete resection. Response monitoring in GISTs is complex due to the presence of intra- and intertumoral heterogeneity and lack of pathological criteria to define response. Nonetheless, it is of importance to evaluate the efficacy of TKI treatment at an early stage in order to optimise treatment. In particular, early cessation of ineffective treatment is of importance in these patients, preventing unnecessary side-effects and healthcare costs. Medical imaging plays an important role to non-invasively predict and monitor treatment response of GIST patients undergoing TKI-treatment. This thesis aims to improve understanding of contrast-enhanced computed tomography (CE-CT) and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT imaging parameters to allow prediction and monitoring of neoadjuvant treatment response in GIST patients.
In Chapter 1, the added value of CE-CT and [18F]FDG-PET imaging for early prediction and monitoring of treatment response in GISTs is investigated by means of a systematic literature review. Results of this study show that heterogeneous enhancement patterns on baseline CE-CT imaging were considered to be predictive for high-risk GISTs, reflecting neovascularisation and the presence of necrosis. Current CE-CT radiographic response criteria (i.e., RECIST 1.1 and Choi) are still lacking sensitivity and are prone to errors when predicting or monitoring treatment response. Metabolic changes on [18F]FDG-PET imaging seem to precede morphological changes in size in GIST lesions and were more strongly correlated with tumour response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.
Chapter 2 evaluates the efficacy of current radiological response criteria (RECIST 1.1, Choi and tumour volumetry) in predicting response to neoadjuvant systemic therapy, by comparing radiological response criteria with the achieved surgical benefit. Results show that size-based criteria (RECIST 1.1 and volumetry) accurately reflect surgical benefit in GIST patients treated with neoadjuvant systemic therapy (accuracy of 76.3% and 86.6% respectively) and are less prone to scanner and imaging protocol variabilities, when compared to the Choi criteria (68.4%).
In addition to volumetry, quantitative radiomic models using CE-CT and [18F]FDG-PET imaging features were trained to predict response at baseline. Preliminary results of this study are described in Chapter 3. The radiomic models presented in this study generally had a poor performance and can therefore not yet be applied in a clinical setting. To improve performance and generalisability, future research should focus on a bigger patient population and harmonisation of acquisition protocols. The conclusion of this thesis supports the utilisation of tumour diameters for radiological response assessment, where RECIST 1.1 response criteria had an accuracy of 80.0% to correctly predict volumetric response after the first response follow-up CE-CT scan. When properly executed, these manual measurements could aid in early surgical decision making.