Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education

The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP). Transferring medical data "as open as possible" enhances the usability of the data for secondary purposes but poses a risk to patient privacy. At the same time, existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. Generally, these legal regulations require the removal of sensitive data but do not consider the possibility of data linkage attacks due to modern image-matching algorithms. In addition, the lack of standardization in DP makes it harder to establish a single solution for all formats of WSIs. These challenges raise problems for bio-informatics researchers in balancing privacy and progress while developing AI algorithms. This paper explores the legal regulations and terminologies for medical data-sharing. We review existing approaches and highlight challenges from the histopathological perspective. We also present a data-sharing guideline for histological data to foster multidisciplinary research and education.Comment: Accepted to FAIEMA 202

Early Mover Advantages

In this paper we analyze empirically whether and if so to what extent later entrants in the European mobile telephony industry have a disadvantage vis-à-vis incumbents and early mover entrants. To analyze this question we consider a series of static models and a dynamic model of market share development. We find a clear early mover advantage, mainly caused by the influence of the penetration rate: it pays to enter when still few people have acquired a mobile telephone. Another important determining factor is the Herfindahl-Hirschman Index at the moment of entry: it is significantly easier to enter a highly concentrated industry. Finally, there are important differences between countries possibly indicating the relative strength of the national regulators. For example, it turns out that it is relatively difficult to enter the mobile telephony sector and gain market share in the Scandinavian countries

Simultaneous Pooled Auctions with Multiple Bids and Preference Lists

A simultaneous pooled auction with multiple bids and preference lists is a way to auction multiple objects, in which bidders simultaneously express a bid for each object and a preference ordering over which object they would like to get in case they have the highest bid on more than one object. This type of auction has been used in the Netherlands and in Ireland to auction available spectrum. We show that this type of auction does not satisfy elementary desirable properties such as the existence of an efficient equilibrium

Semi-supervised tissue segmentation of histological images

Supervised learning of convolutional neural networks (CNN) used for image classification and segmentation has produced state-of-the art results, including in many medical image applications. In the medical field, making ground truth labels would typically require an expert opin ion, and a common problem is the lack of labeled data. Consequently, the models might not be general enough. Digitized histological microscopy images of tissue biopsies are very large, and detailed truth markings for tissue-type segmentation are scarce or non-existing. However, in many cases, large amounts of unlabeled data that could be exploited are readily accessible. Methods for semi-supervised learning exists, but are hardly explored in the context of computational pathology. This paper deals with semi-supervised learning on the application of tissue-type classifi cation in histological whole-slide images of urinary bladder cancer. Two semi-supervised approaches utilizing the unlabeled data in combination with a small set of labeled data is presented. A multiscale, tile-based segmentation technique is used to classify tissue into six different classes by the use of three individual CNNs. Each CNN is presented tissue at different magnification levels in order to detect different feature types, later fused in a fully-connected neural network. The two self-training ap proaches are: using probabilities and using a clustering technique. The clustering method performed best and increased the overall accuracy of the tissue tile classification model from 94.6% to 96% compared to using supervised learning with labeled data. In addition, the clustering method generated visually better segmentation images.publishedVersio

Deep Learning for Predicting Metastasis on Melanoma WSIs

Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variability among experts is notable, drastically jeopardizing its reproducibility. Thus, the need for faster and more reproducible methods arises. Machine learning has paved its way into digital pathology, but so far, most contributions are on localization, segmentation, and diagnostics, with little emphasis on prognostics. This paper presents a convolutional neural network (CNN) method based on VGG16 to predict melanoma prognosis as the presence of metastasis within five years. Patches are extracted from regions of interest from Whole Slide Images (WSIs) at different magnification levels used in model training and validation. Results infer that utilizing WSI patches at 20x magnification level has the best performance, with an F1 score of 0.7667 and an AUC of 0.81

End-of-life care in a COPD patient awaiting lung transplantation: a case report

COPD is nowadays the main indication for lung transplantation. In appropriately selected patients with end stage COPD, lung transplantation may improve quality of life and prognosis of survival. However, patients with end stage COPD may die while waiting for lung transplantation. Palliative care is important to address the needs of patients with end stage COPD. This case report shows that in a patient with end stage COPD listed for lung transplantation offering palliative care and curative-restorative care concurrently may be problematic. If the requirements to remain a transplantation candidate need to be met, the possibilities for palliative care may be limited. Discussing the possibilities of palliative care and the patient's treatment preferences is necessary to prevent that end-of-life care needs of COPD patients dying while listed for lung transplantation are not optimally addressed. The patient's end-of-life care preferences may ask for a clear distinction between the period in which palliative and curative-restorative care are offered concurrently and the end-of-life care period. This may be necessary to allow a patient to spend the last stage of life according to his or her wishes, even when this implicates that lung transplantation is not possible anymore and the patient will die because of end stage COPD

Combined HER3-EGFR score in triple-negative breast cancer provides prognostic and predictive significance superior to individual biomarkers

© 2020, The Author(s). Epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 3 (HER3) have been investigated as triple-negative breast cancer (TNBC) biomarkers. Reduced EGFR levels can be compensated by increases in HER3; thus, assaying EGFR and HER3 together may improve prognostic value. In a multi-institutional cohort of 510 TNBC patients, we analyzed the impact of HER3, EGFR, or combined HER3-EGFR protein expression in pre-treatment samples on breast cancer-specific and distant metastasis-free survival (BCSS and DMFS, respectively). A subset of 60 TNBC samples were RNA-sequenced using massive parallel sequencing. The combined HER3-EGFR score outperformed individual HER3 and EGFR scores, with high HER3-EGFR score independently predicting worse BCSS (Hazard Ratio [HR] = 2.30, p = 0.006) and DMFS (HR = 1.78, p = 0.041, respectively). TNBCs with high HER3-EGFR scores exhibited significantly suppressed ATM signaling and differential expression of a network predicted to be controlled by low TXN activity, resulting in activation of EGFR, PARP1, and caspases and inhibition of p53 and NFκB. Nuclear PARP1 protein levels were higher in HER3-EGFR-high TNBCs based on immunohistochemistry (p = 0.036). Assessing HER3 and EGFR protein expression in combination may identify which adjuvant chemotherapy-treated TNBC patients have a higher risk of treatment resistance and may benefit from a dual HER3-EGFR inhibitor and a PARP1 inhibitor

Whole-Exome Sequencing Reveals High Mutational Concordance between Primary and Matched Recurrent Triple-Negative Breast Cancers

PURPOSE Triple-negative breast cancer (TNBC) is a molecularly complex and heterogeneous breast cancer subtype with distinct biological features and clinical behavior. Although TNBC is associated with an increased risk of metastasis and recurrence, the molecular mechanisms underlying TNBC metastasis remain unclear. We performed whole-exome sequencing (WES) analysis of primary TNBC and paired recurrent tumors to investigate the genetic profile of TNBC. METHODS Genomic DNA extracted from 35 formalin-fixed paraffin-embedded tissue samples from 26 TNBC patients was subjected to WES. Of these, 15 were primary tumors that did not have recurrence, and 11 were primary tumors that had recurrence (nine paired primary and recurrent tumors). Tumors were analyzed for single-nucleotide variants and insertions/deletions. RESULTS The tumor mutational burden (TMB) was 7.6 variants/megabase in primary tumors that recurred (n = 9); 8.2 variants/megabase in corresponding recurrent tumors (n = 9); and 7.3 variants/megabase in primary tumors that did not recur (n = 15). MUC3A was the most frequently mutated gene in all groups. Mutations in MAP3K1 and MUC16 were more common in our dataset. No alterations in PI3KCA were detected in our dataset. CONCLUSIONS We found similar mutational profiles between primary and paired recurrent tumors, suggesting that genomic features may be retained during local recurrence

Invasive cancerous area detection in non-muscle invasive bladder cancer whole slide images

Bladder cancer patients’ stratification into risk groups relies on grade, stage and clinical factors. For non-muscle invasive bladder cancer, T1 tumours that invade the subepithelial tissue are high-risk lesions with a high probability to progress into an aggressive muscle-invasive disease. Detecting invasive cancerous areas is the main factor for dictating the treatment strategy for the patient. However, defining invasion is often subject to intra/interobserver variability among pathologists, thus leading to over or undertreatment. Computer-aided diagnosis systems can help pathologists reduce overheads and erratic reproducibility. We propose a multi-scale model that detects invasive cancerous areas patterns across the whole slide image. The model extracts tiles of different tissue types at multiple magnification levels and processes them to predict invasive patterns based on local and regional information for accurate T1 staging. Our proposed method yields an F1 score of 71.9, in controlled settings 74.9, and without infiltration 90.0.acceptedVersio

Clustering of 27,525,663 death records from the United States based on health conditions associated with death: an example of big health data exploration

Background: Insight into health conditions associated with death can inform healthcare policy. We aimed to cluster 27,525,663 deceased people based on the health conditions associated with death to study the associations between the health condition clusters, demographics, the recorded underlying cause and place of death. Methods: Data from all deaths in the United States registered between 2006 and 2016 from the National Vital Statistics System of the National Center for Health Statistics were analyzed. A self-organizing map (SOM) was used to create an ordered representation of the mortality data. Results: 16 clusters based on the health conditions associated with death were found showing significant differences in socio-demographics, place, and cause of death. Most people died at old age (73.1 (18.0) years) and had multiple health conditions. Chronic ischemic heart disease was the main cause of death. Most people died in the hospital or at home. Conclusions: The prevalence of multiple health conditions at death requires a shift from disease-oriented towards person-centred palliative care at the end of life, including timely advance care planning. Understanding differences in population-based patterns and clusters of end-of-life experiences is an important step toward developing a strategy for implementing population-based palliative care