Applicability of Business Process Model Analysis Approaches – A Case Study in Financial Services Consulting

The analysis of business process models gains more and more attention in IS research. Several analysis approaches have been developed. All of them provide different features, such as syntax checking or pattern recognition. This paper investigates the applicability and relevance of business process model analysis approaches using a case study from financial services consulting. Two research contributions are provided. First, an overview about common model analysis features and its relevance for consulting processes are provided. Second, the applicability of the automatic business process model analysis approaches is investigated. Results show that the majority of features can raise efficiency of analyses in business process reengineering projects

Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography

Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Inhibiting phosphoglycerate dehydrogenase counteracts chemotherapeutic efficacy against MYCN‐amplified neuroblastoma

Here we sought metabolic alterations specifically associated with MYCN amplification as nodes to indirectly target the MYCN oncogene. Liquid chromatography-mass spectrometry-based proteomics identified seven proteins consistently correlated with MYCN in proteomes from 49 neuroblastoma biopsies and 13 cell lines. Among these was phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in de novo serine synthesis. MYCN associated with two regions in the PHGDH promoter, supporting transcriptional PHGDH regulation by MYCN. Pulsed stable isotope-resolved metabolomics utilizing C-13-glucose labeling demonstrated higher de novo serine synthesis in MYCN-amplified cells compared to cells with diploid MYCN. An independence of MYCN-amplified cells from exogenous serine and glycine was demonstrated by serine and glycine starvation, which attenuated nucleotide pools and proliferation only in cells with diploid MYCN but did not diminish these endpoints in MYCN-amplified cells. Proliferation was attenuated in MYCN-amplified cells by CRISPR/Cas9-mediated PHGDH knockout or treatment with PHGDH small molecule inhibitors without affecting cell viability. PHGDH inhibitors administered as single-agent therapy to NOG mice harboring patient-derived MYCN-amplified neuroblastoma xenografts slowed tumor growth. However, combining a PHGDH inhibitor with the standard-of-care chemotherapy drug, cisplatin, revealed antagonism of chemotherapy efficacy in vivo. Emergence of chemotherapy resistance was confirmed in the genetic PHGDH knockout model in vitro. Altogether, PHGDH knockout or inhibition by small molecules consistently slows proliferation, but stops short of killing the cells, which then establish resistance to classical chemotherapy. Although PHGDH inhibition with small molecules has produced encouraging results in other preclinical cancer models, this approach has limited attractiveness for patients with neuroblastoma

Elimusertib has anti-tumor activity in preclinical patient-derived pediatric solid tumor models

The small molecule inhibitor of ataxia telangiectasia and Rad3-related protein (ATR), elimusertib, is currently being tested clinically in various cancer entities in adults and children. Its preclinical anti-tumor activity in pediatric malignancies, however, is largely unknown. We here assessed the preclinical activity of elimusertib in 38 cell lines and 32 patient-derived xenograft (PDX) models derived from common pediatric solid tumor entities. Detailed in vitro and in vivo molecular characterization of the treated models enabled the evaluation of response biomarkers. Pronounced objective response rates were observed for elimusertib monotherapy in PDX, when treated with a regimen currently used in clinical trials. Strikingly, elimusertib showed stronger anti-tumor effects than some standard of care chemotherapies, particularly in alveolar rhabdomysarcoma PDX. Thus, elimusertib has strong preclinical anti-tumor activity in pediatric solid tumor models, which may translate to clinically meaningful responses in patients

Padronização da pesquisa de linfonodos sentinelas em estômago por métodos combinados

Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas, Pós-Graduação em Ciências da Cirurgia, Campinas, 2012.Introdução - Com os estudos de Gould et al. (1960), Cabanas (1977) e Morton et al. (1992), estabeleceu-se o conceito da pesquisa do linfonodo sentinela. Esse se baseia na teoria de que ao identificar a presença ou ausência de metástase no primeiro linfonodo que recebe a drenagem linfática a partir do tumor (sentinela), poderia representar o estado de acometimento dos outros linfonodos. Isto evitaria a realização desnecessária de linfadenectomias. Com o passar dos anos, foi consagrada para ser aplicada em casos de melanoma e câncer de mama. Nesta última década, tenta-se estender os princípios da utilização da pesquisa de linfonodo sentinela para os cânceres do aparelho digestivo. Entretanto, no caso do estômago, existem algumas dificuldades, como: presença de sistema de drenagem linfática multidirecional, ocorrência de metástases saltatórias e identificação de mais de um linfonodo sentinela por indivíduo. Objetivo - Criar e padronizar um modelo animal para o treinamento de pesquisa de linfonodos sentinelas em estômago. Método - Trinta e dois coelhos, saudáveis, foram submetidos à anestesia exclusivamente intramuscular. Por meio de laparotomia, foi injetado na subserosa da parede anterior do corpo gástrico, 0,1 ml de fitato marcado com tecnécio-99m (0,2 mCi), em seguida pelo mesmo orifício, de 0,2 ml de Azul Patente V® 2,5%. A cavidade abdominal foi avaliada, in vivo , para pesquisa de suspeitas de linfonodos azuis (corados em azul) e com detector manual de radiação gamma aos 5, 10 e 20 minutos para detecção de suspeitas de linfonodos radioativos (radioatividade identificada superior a 10X o valor apresentado pelo fundo). Após 20 minutos, realizou-se ressecção e exérese total do estômago, baço e suspeitas de linfonodos, para posterior avaliação da radioatividade ex vivo . A seguir, encaminharam-se as suspeitas de linfonodos para estudo histológico para identificação de tecido linfóide. Resultados - Foram identificados linfonodos em 30 coelhos (93,75%) com média de 2,2 por animal. Das 90 suspeitas de linfonodos detectadas, em 70 casos (77,77%) obteve-se confirmação histológica para tecido linfóide. Dessas, a maioria foi identificada e localizada na região entre o esôfago e o fundo gástrico durante a avaliação in vivo aos 5 minutos. Dois coelhos faleceram durante os experimentos (Taxa de mortalidade = 6,25%). Conclusão - O modelo experimental em coelhos para pesquisa de linfonodos sentinelas em estômago por métodos combinados foi factível, de fácil execução e baixa mortalidade, podendo ser usado para treinamento.Abstract : Introduction - The concept of sentinel lymph node was established by the studies of Gould et al. (1960), Cabanas (1977) and Morton et al. (1992). It is based on the theory that, whenever the presence or absence of metastasis is identified in the first lymph node that receives the lymphatic drainage from the tumor (sentinel) the status of involvement of other lymph nodes might be infered. This could avoid the performance of unnecessary lymphadenectomies. Over the years, its use was consecrated by its application in melanoma and breast cancer. In the last decade, attempts have been made to extend the principles of sentinel lymph node investigation to cancers of the digestive tract. In the case of stomach cancer, additional difficulties were found, such as multiple and aberrant lymphatic routes, the occurrence of skip metastasis and the possible identification of more than one sentinel lymph node in the same patient. Aim - To develop and evaluate an animal model for training sentinel lymph node navigation in the stomach. Methods - Thirtytwo healthy rabbits, were prepped and given intramuscular anesthesia. Through a formal laparotomy, they received a subserosal injection of 0.1 ml of phytate labeled with technetium-99m (0.2 mCi) in the anterior wall of the gastric corpus, followed by 0.2 ml of Blue Patent ® V 2.5%, through the same puncture site. Suspicious lymph nodes were searched in-vivo at 5, 10 and 20 minutes, both visually (Blue Patent stained lymph nodes) and with a manual gamma radiation detector (to detect suspected radioactive lymph nodes, displaying radioactivity levels over 10X the value displayed by the background). En-block resection of the stomach, spleen, visible limph nodes and local fat tissue was then performed and the specimen was assessed "ex vivo" for radioactivity. Suspected lymph nodes were sent for histological study to evaluate the presence of lymphoid tissue. Results Radiolabeled or stained lymph nodes were identified in 30 rabbits (93.75%) with an average of 2.2 specimens per animal; of the 90 suspicious lymph nodes detected, histology confirmed lymphoid tissue in 70 cases (77.77%). Most lymph nodes were identified at the 5-minute in-vivo evaluation and their most common location was found to be in the region between the esophagus and the gastric fundus. Two rabbits died during the procedure resulting in a 6.25% mortality. Conclusion - The rabbit model proved adequate for training in sentinel node navigation in the stomach by combined methods (dye and radiocolloid) being easy to execute and associated with low mortality