Main methods and contributions of the thesis

The thesis workflow can be divided into the steps illustrated in Figure 1.2.

Figure 1.2. Thesis workflow. Step 1 - An initial literature analysis provided to gain knowledge of the current state of DP; Step 2 – A requirements analysis to collect and evaluate all features and characteristics of the DPS for biobank research; Step 3 – The development and set up of the DPS to use digital tools in the pathology workflow; Step 4 - The integration of the DPS into the laboratory management software; Step 5 – The evaluation of the developed solution.

To answer the questions and find a solution to the problem, various methods have been used in each step of the thesis workflow:

Step 1. Analyzing literature in the context of the validation and implementation of a DPS for different applications

Methods: First, the most prevalent international regulations and guidelines centering on the validation and implementation of DP tools in clinical and nonclinical environments were analyzed. The literature this thesis employed was provided by regulatory bodies in Europe, the United States, Canada and Australasia. Furthermore, the existing standards in pathology, proposed by standardization bodies such as DICOM, HL7, and IHE, were evaluated for the biobank research network. In the end, IT frameworks for medical research environments suggested by TMF were explored in terms of image processing, data protection, and specimen identification.

Step 2. Analyzing the requirements for the DPS in the biobank research environment Methods: At the beginning of this phase, a requirements engineering framework for the development of the integrated DP workflow was defined with two components – requirements analysis and requirements management. The complete process of requirements engineering was broken down into several sub-parts. First, the main stakeholders were considered and the system environment for which the DPS should be developed was defined.

Second, requirements elicitation sources and techniques were determined using widely approved methods, such as documentation analysis, meetings with stakeholders, as well as process observation techniques [34]. Third, based on the questions list, requirements were gathered; prioritized as essential, preferred and desirable requirements; categorized into different groups like functional, technical, quality and environmental requirements.

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Additionally, an investigation of microscopic image viewers and handling systems on the market was carried out. Based on this stage, supplementary features of the DPS were identified. In all, 19 web viewers for microscopic images based on various platforms were found and the main features, advantages and disadvantages of the most prevalent microscope imaging systems were assessed. Fourth, requirements specification and modeling techniques were determined. Therefore, five use case scenarios were designed to describe the digital workflow process from different points of view. To manage and balance different concerns effectively, the multiple viewpoints approach was used during the requirements engineering [35]. The following three viewpoints were identified: a process viewpoint to describe the processes of the whole workflow from various user perspectives, an information system viewpoint to define and characterize the appropriate information systems used for the management of processes, and the interfacing viewpoint to determine the interface solution between the different information systems [36]. Finally, the requirements validation was carried out using inspection and testing methods to ensure that the documented requirements met stakeholder needs. Together, these sub-steps were significantly important for requirements elicitation, analysis, and management. A detailed description of the methods used for the requirements engineering are described in Subchapter 3.1.

Step 3. Developing and starting operations for an interoperable solution of the digital image handling system for the biobank research network

Methods: Based on the requirements analysis, fundamental modules of the DPS were evaluated. Prior to the digitization of the samples, methods for labeling and identification of glass slides were determined for the biorepository. Therefore, the key components of the labels were considered based on the current guidelines for the uniform labeling of slides [37].

In addition, label material and printing options were designed and the identification mechanism of specimen-derived assets in the current biospecimen management software was assessed. Most of the LIMS provide the identifiers (IDs) only for the case (brain) and its blocks (specimens) [38]. To improve the identification system for specimen-derived assets, a user-friendly interface was built in the local LIMS by an IT technician. This approach was implemented in the LIMS for two main reasons. First, each glass slide requires a unique ID that identifies not only the glass slide, but also the specimen from which the glass slide is derived.

Therefore, the LIMS is the exact environment in which identifiers for specimen-derived assets should be generated. Second, it is highly important to export all components of the label in a

structured format for label printing directly from the laboratory software in which the specimens are registered.

Once the labeling concept for glass slides of the MS biorepository was determined, a digitization strategy was defined in collaboration with the Department of Neuropathology.

Based on that, five glass slides colored with different standard staining types were selected for each specimen. Furthermore, scanning settings (such as magnification, sample detection sensitivity, size of scan area, etc.) were defined with regard to the normal-appearing, dim, and faint samples. To expedite the digitization of the glass slides, batch scanning modes and metadata entry via comma-separated values (CSV) files were used. Because the digital microscopic images are larger than any other medical images, a sustainable data storage infrastructure was needed to support them [39]. Therefore, the Net Image Server (NIS) was installed and updated to the latest version to support Simple Object Access Protocol (SOAP)-based web service technology. By estimating the storage requirements, an adequate amount of disk space to store multiple digital images on the server during the next five years was determined. A web application was published for the Brain Bank database to access, view, and annotate high-resolution digital microscopic images.

Step 4. Integrating the DP module into the already existing IT-infrastructure of the KKNMS by defining the set of metadata for the scientific data collection process

Methods: Once the DPS was developed, automated and manual tools for integration of the DP module into the LIMS were designed. At the beginning of the implementation phase, interoperability analysis between the DPS and the LIMS was performed to provide efficient mechanisms for interaction between the targeted systems. In addition, a communication scheme was determined between the LIMS and the DPS. In this context, an interfacing solution was developed in the LIMS to link digital images to the corresponding specimen. The SOAP-based web services were used to transfer imaging metadata (such as staining type, a thumbnail preview with the image URL¹, etc.) from the DPS to the LIMS. The query structure and trigger settings were determined and considered during the development of the interfacing solution. Finally, a viewing interface for displaying the imported imaging metadata was developed and implemented in the LIMS. Users can view microscopic image thumbnails

1 URL - A Uniform Resource Locator

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for each specimen and launch a web viewer directly from the LIMS for further investigation.

The viewing interface acts as a bridge between the DPS and the LIMS.

Step 5. Evaluating working prototypes in relation to the up-to-date literature analysis Methods: Finally, the pros and cons of tightly integrated DPS were evaluated. Additionally, the system development life cycle was tested using the W-model, which is a method used in the validation and verification of the system. Finally, an up-to-date scientific literature review was performed regarding the standards, current trends, and future perspectives of DPSs.

Note: The literature analysis was performed two times – at the beginning to understand the existing DP state and at the end of the study in order to evaluate the developed system compared to current DP states. As such, these steps are stated in the thesis as initial and concluding literature analyses, respectively.

In conclusion, the above five steps were significant in the successful development of an interoperable DPS that allows for the management of high resolution digital microscopic images using automated tools for the acquisition of images, and viewing and analyzing them using a fast viewer. In addition, this thesis introduces a new interfacing solution between the DPS and the pathology system that ensures automated links between the images and corresponding specimens.