2020 Vision of Digital Pathology in Action

Towards a national strategy for digital pathology in Switzerland

Precision medicine is entering a new era of digital diagnostics; the availability of integrated digital pathology (DP) and structured clinical datasets has the potential to become a key catalyst for biomedical research, education and business development. In Europe, national programs for sharing of this data will be crucial for the development, testing, and validation of machine learning-enabled tools supporting clinical decision-making. Here, the Swiss Digital Pathology Consortium (SDiPath) discusses the creation of a Swiss Digital Pathology Infrastructure (SDPI), which aims to develop a unified national DP network bringing together the Swiss Personalized Health Network (SPHN) with Swiss university hospitals and subsequent inclusion of cantonal and private institutions. This effort builds on existing developments for the national implementation of structured pathology reporting. Opening this national infrastructure and data to international researchers in a sequential rollout phase can enable the large-scale integration of health data and pooling of resources for research purposes and clinical trials. Therefore, the concept of a SDPI directly synergizes with the priorities of the European Commission communication on the digital transformation of healthcare on an international level, and with the aims of the Swiss State Secretariat for Economic Affairs (SECO) for advancing research and innovation in the digitalization domain. SDPI directly addresses the needs of existing national and international research programs in neoplastic and non-neoplastic diseases by providing unprecedented access to well-curated clinicopathological datasets for the development and implementation of novel integrative methods for analysis of clinical outcomes and treatment response. In conclusion, a SDPI would facilitate and strengthen inter-institutional collaboration in technology, clinical development, business and research at a national and international scale, promoting improved patient care via precision medicine.

On the Road to Digital Pathology in Denmark-National Survey and Interviews

Digital pathology (DP) is changing pathology departments dramatically worldwide, yet globally, few departments are presently digitalized for the full diagnostic workflow. Denmark is also on the road to full digitalization countrywide, and this study aim to cover experiences during the implementation process in a national context. Thus, quantitative questionnaires were distributed to all pathology departments in Denmark (n = 13) and distributed to all professions including medical clinical directors, medical doctors (MD) and biomedical laboratory scientists (BLS). For a qualitative perspective, we interviewed four employees representing four professions. Data were collected in 2019–2020. From the questionnaire and interviews, we found strategies differed at the Danish departments with regards to ambitions, technological equipment, workflows, and involvement of type of professions. DP education was requested by personnel. Informants were in general positive toward the digital future but mainly had concerns regarding the political pressure to integrate DP before technological advances are sufficient for maintaining rational budgets, workflows, and for sustaining diagnostic quality. This study is a glance on the Danish implementation process in its early stages from personnel’s point of view. It shows the complexity when large new workflow processes are to be implemented countrywide and with a large diversity of stakeholders like managers, MD, BLS, IT-professionals, and authorities. To ensure best technological and economical solutions and to maintain—or even optimize—diagnostic quality with DP and workflow alignment, we suggest superior inter- and intradepartmental communication. When implementing DP countrywide, a national working group is warranted with the variety of stakeholders represented.

Digital Pathology and Artificial Intelligence Applications in Pathology

Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.

Can artificial intelligence improve cancer treatments?

Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.

Tumor Budding Score Is a Strong and Independent Prognostic Factor in Patients With Pancreatic Ductal Adenocarcinoma: An Evaluation of Whole Slide Pathology Images of Large Sections

OBJECTIVE

We aimed to develop the tumor budding (TB) score and to explore the association between the TB score and overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this retrospective study, 130 consecutive patients with PDAC underwent surgical resection between July 2016 and March 2019. The location and counts of TB were assessed based on the digitalized whole slide hematoxylin and eosin images. The TB score was achieved using the Cox regression equation. The cutoff point for the TB score was determined by X-tile. Univariate and multivariate Cox regression models were used to analyze the association between the TB score and OS.

RESULTS

The TB score was 0.49 (range = 0-1.08), and the best cutoff for the TB score was 0.62. The duration of survival in individuals with a low TB score [median = 21.8 months, 95% confidence interval (CI) = 15.43-25.50] was significantly longer than that in those with a high TB score (median = 11.33 months, 95% CI = 9.8-14.22). Univariate analysis revealed that the TB score was significantly associated with OS [hazard ratio (HR) = 2.71, 95% CI = 1.48-4.96, p = 0.001]. Multivariate analysis revealed a strong and independent association between the TB score and OS (HR = 2.35, 95% CI = 1.27-4.33, p = 0.03). The high TB score group had a 2.14 times higher mortality than the low TB score group.

CONCLUSION

The TB score is strongly and independently associated with the risk of OS in PDAC.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Tumor Size on Microscopy, CT, and MRI Assessments Versus Pathologic Gross Specimen Analysis of Pancreatic Neuroendocrine Tumors

OBJECTIVE. The purpose of the present study was to assess the consistency of measurements of pancreatic neuroendocrine tumor (PNET) tumor size obtained using pre-operative imaging, pathologic gross specimen analysis, and microscopic examination of large pathologic sections; evaluate the impact of differences in pathologic and radiologic measurements of size on T categorization; and investigate the exact relationships among tumor size measurements obtained from microscopic analysis, CT, MRI, and pathologic gross specimen analysis. MATERIALS AND METHODS. We enrolled 64 patients with pathologically confirmed PNETs who underwent radiologic examination between December 2016 and September 2019. Tumor sizes were measured by CT, MRI, pathologic gross specimen analysis, and microscopic examination. The relationship between the tumor sizes calculated by MRI and microscopy was analyzed using univariate and multivariate logistic regression models. RESULTS. The measurements of tumor sizes calculated by pathologic and radiologic assessments and CT and MRI assessments showed good concordance, but measurements calculated by microscopic analysis and other methods showed poor concordance. When T categories from pathologic gross specimen analysis were considered the reference, alterations in T category were found in the microscopic assessments of 12 of 64 patients (18.75%), CT assessments of 15 of 64 patients (23.44%), and MRI assessments of 13 of 64 patients (20.31%). In the fully adjusted model, microscopic size (β, 1.05; 95% CI, 0.98-1.12; p < .001), CT size (β, 0.90; 95% CI, 0.78-1.02; p < .001), and MRI size (β, 0.92; 95% CI, 0.81-1.04; p < .001) were significantly correlated with gross tumor size. CONCLUSION. Tumor sizes measured by microscopy, CT, and MRI were significantly associated with the gross size of PNETs. This finding provides physicians with new tools for rapid identification of gross tumor size.

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.

The relationship between microscopic tumor size and CT tumor size in pancreatic ductal adenocarcinoma

OBJECTIVES

To investigate the exact relationship between CT tumor size and the microscopic tumor size in PDAC.

MATERIALS AND METHODS

We enrolled 310 patients with pathologically confirmed PDAC without preoperative adjuvant therapies who underwent CT examination from June 2016 and December 2018. Smooth curve fitting and a segmented regression model were used to analyze the threshold effect between CT tumor size and the microscopic tumor size.

RESULTS

The tumor size was 2.93±1.15 cm under the microscope and 3.00±1.23 cm in CT. The mean bias was 0.067 cm between CT and microscopic assessments. The accuracy of CT T stages was 61.02% (36/59), 79.41% (162/204) and 57.45% (27/47) in T1, T2 and T3, respectively. A non-linear relationship was detected between CT tumor size and the microscopic tumor size, with a turning point of 4.3 cm. On the left of the inflection point, the effect size, 95% confidence interval, and p value were 0.51, 0.40 to 0.63, and <0.0001, respectively. However, we observed no relationship between CT size and microscopic tumor size on the right of the inflection point (0.22, 0 to 0.44, 0.053).

CONCLUSIONS

The relationship between CT and the microscopic tumor size is non-linear. When the CT tumor size was <4.3 cm, every 1-cm increase in CT tumor size was associated with a 0.56 cm increase in microscopic tumor size. When the CT tumor size was >4.3 cm, every 1-cm increase in CT tumor size was associated with a 0.91 cm increase in microscopic tumor size.

Digital Pathology: Advantages, Limitations and Emerging Perspectives

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist's profession.