Verifying Properties of Large Sets of Processes with Network Invariants

peer reviewe

18F-FDG PET/CT in oncology: contribution to the tumor characterization using quantitative analysis of the signal

Revue de la littérature et résultats/présentation de nos 3 études portant sur la charactértisation tumorale par analyse du signal 18F-FDG PET, en particulier de l'analyse de la texture de l'image

Isolated pelvic bone involvement as a presentation of alveolar echinococcosis.

peer reviewe

Genetic diversity of Echinococcus multilocularis specimens isolated from Belgian patients with alveolar echinococcosis using EmsB microsatellites analysis.

The genetic diversity of Echinococcus multilocularis (E. multilocularis) specimens isolated from patients with alveolar echinococcosis (AE), is a major field of investigation to correlate with sources of infection, clinical manifestations and prognosis of the disease. Molecular markers able to distinguish samples are commonly used worldwide, including the EmsB microsatellite. Here, we report the use of the EmsB microsatellite polymorphism data mining for the retrospective typing of Belgian specimens of E. multilocularis infecting humans. A total of 18 samples from 16 AE patients treated between 2006 and 2021 were analyzed through the EmsB polymorphism. Classification of specimens was performed through a dendrogram construction in order to compare the similarity among Belgian samples, some human referenced specimens on the EWET database (EmsB Website for the Echinococcus Typing) and previously published EmsB profiles from red foxes circulating in/near Belgium. According to a comparison with human European specimens previously genotyped in profiles, the 18 Belgian ones were classified into three EmsB profiles. Four specimens could not be assigned to an already known profile but some are near to EWET referenced samples. This study also highlights that some specimens share the same EmsB profile with profiles characterized in red foxes from north Belgium, the Netherlands, Luxembourg and French department near to the Belgian border. Furthermore, Belgian specimens present a genetic diversity and include one profile that don't share similarities with the ones referenced in the EWET database. However, at this geographical scale, there is no clear correlation between EmsB profiles and geographical location. Further studies including additional clinical samples and isolates from foxes and rodents of south Belgium are necessary to better understand the spatial and temporal circumstances of human infections but also a potential correlation between EmsB profiles and parasite virulence

Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.

peer reviewedOver the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy

Myoferlin controls mitochondrial structure and activity in pancreatic ductal adenocarcinoma, and affects tumor aggressiveness

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death. Therapeutic options remain very limited and are based on classical chemotherapies. Energy metabolism reprogramming appears as an emerging hallmark of cancer and is considered a therapeutic target with considerable potential. Myoferlin, a ferlin family member protein overexpressed in PDAC, is involved in plasma membrane biology and has a tumor-promoting function. In the continuity of our previous studies, we investigated the role of myoferlin in the context of energy metabolism in PDAC. We used selected PDAC tumor samples and PDAC cell lines together with small interfering RNA technology to study the role of myoferlin in energetic metabolism. In PDAC patients, we showed that myoferlin expression is negatively correlated with overall survival and with glycolytic activity evaluated by 18F-deoxyglucose positron emission tomography. We found out that myoferlin is more abundant in lipogenic pancreatic cancer cell lines and is required to maintain a branched mitochondrial structure and a high oxidative phosphorylation activity. The observed mitochondrial fission induced by myoferlin depletion led to a decrease of cell proliferation, ATP production, and autophagy induction, thus indicating an essential role of myoferlin for PDAC cell fitness. The metabolic phenotype switch generated by myoferlin silencing could open up a new perspective in the development of therapeutic strategies, especially in the context of energy metabolism

The uptake of [18F]-fluorodeoxyglucose by the renal allograft correlates with the acute Banff scores of cortex inflammation but not with the 1-year graft outcomes

peer reviewedIntroduction[18F]FDG PET/CT noninvasively disproves acute kidney allograft rejection (AR) in kidney transplant recipients (KTRs) with suspected AR. However, the correlation of biopsy-based Banff vs. PET/CT-based scores of acute inflammation remains unknown, as does the prognostic performance of [18F]FDG PET/CT at one year post suspected AR.MethodsFrom 2012 to 2019, 114 [18F]FDG-PET/CTs were prospectively performed in 105 adult KTRs who underwent per cause transplant biopsies. Ordinal logistic regression assessed the correlation between the extent of histological inflammation and the mean standardized [18F]FDG uptake values (mSUVmean). Functional outcomes of kidney allografts were evaluated at one year post per cause biopsy and correlated to mSUVmean.ResultsA significant correlation between mSUVmean and acute Banff score was found, with an adjusted R2 of 0.25. The mSUVmean was significantly different between subgroups of “total i”, with 2.30 ± 0.71 in score 3 vs. 1.68 ± 0.24 in score 0. Neither the function nor the survival of the graft at one year was statistically related to mSUVmean.Discussion[18F]FDG-PET/CT may help noninvasively assess the severity of kidney allograft inflammation in KTRs with suspected AR, but it does not predict graft outcomes at one year

2022-RA-193-ESGO Cost-effectiveness of molecular profiling for endometrial neoplasia: a single institution experience

peer reviewe

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence.

peer reviewedThe primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

peer reviewedPurpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza