Multi-Omic Prediction of Overall Survival in Patients With Glioblastoma: Additive and Synergistic Value of Clinical Measures, Radiomics, and Genomics

Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OS<6 months) vs all, and low-risk (OS≥18 months) vs all. The trained patient stratification model was independently tested in the replication cohort (n=112) and a patient-wise survival prediction index (SPIradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.

Images in Space and Time

Medical imaging diagnosis is mostly subjective, as it depends on medical experts. Hence, the service provided is limited by expert opinion variations and image complexity as well. However, with the increasing advancements in deep learning field, techniques are developed to help in the diagnosis and risk assessment processes. In this article, we survey different types of images in healthcare. A review of the concept and research methodology of Radiomics will highlight the potentials of integrated diagnostics. Convolutional neural networks can play an important role in next generations of automated imaging biomarker extraction and big data analytics systems. Examples are provided of what is already feasible today and also describe additional technological components required for successful clinical implementation.

Efficient method for the global noise filtering in measuring channels of the VVER NPP leak monitoring systems

In accordance with Ref. (GOST R 58328-2018 “Pipelines of Nuclear Power Plants. Leak Before Break Concept”), NPPs with VVER-1200 reactors operate an acoustic leak monitoring system (ALMS) and a humidity leak monitoring system (HLMS), each performing the leak monitoring functions locally, independently of the other. The diagnostics results are conveyed to the upper level control system (LCS) to be further displayed for the main control room (MCR) operating personnel. There is also an integrated diagnostics system (IDS) intended to confirm the diagnosis and to update the leak rate values and coordinates based on analyzing the leak monitoring system readings and I&amp;C signals. The system measuring channel readings are composed of background noise, the source for which are processes on the part of the reactor facility’s key components and auxiliary systems, and the leak signal in response to the leak occurrence. A major factor that affects the capability of leak monitoring systems to detect the leak is the quality of the background noise filtering. A new efficient global noise filtering method is proposed for being used as part of the integrated diagnostics system (IDS).

Temporal characteristics of an integrated diagnostics risk score before and after heart failure hospitalizations in a large real-world population of patients with implantable devices

Background Diagnostic parameters measured in implantable cardioverter defibrillators (ICD) and cardiac resynchronization therapy defibrillators (CRTD) have been shown to change before and after heart failure (HF) events with fluid overload. Purpose We investigated the temporal characteristics of an integrated diagnostic risk score before and after HF events in a large real-world cohort of patients with ICD/CRTD devices. Methods We linked a de-identified database of aggregated electronic health record (EHR) data (2007–2017) to a manufacturer's device database with continuous diagnostic monitoring data. Patients with ICD/CRTD implants with intra-thoracic impedance diagnostic feature were included for this study. The previously defined integrated diagnostic risk score was derived by combining daily diagnostic data, including intra-thoracic impedance, night-time heart rate, activity, heart rate variability, and atrial fibrillation (AF) burden, ventricular rate during AF, CRT pacing, ventricular tachycardia episodes and shocks, in a Bayesian Belief Network framework. HF event was defined as an inpatient, emergency department, or observation unit stay in a hospital with primary diagnosis of HF and intravenous diuretic administration. Temporal average of daily risk score across all patients in the 60 days pre and post HF events were compared in HF events with and without readmission within 60 days and with no HF event during pre and post follow-up days. Results A total of 17,886 patients with 1.8±1.2 years of follow-up met inclusion criteria. The average age of patients was 66.6±12.3 years, with 72% being males, and 51% with ICD devices. The average integrated diagnostics risk score in HF events with and without readmission and with no events is shown in Figure. A total of 1174 patients had 1425 HF events with no readmission for HF within 60 days and 282 patients had 295 HF events which were followed by readmission for HF within 60 days. A total of 17,839 patients had no HF events over 86,858 follow-up months. The average daily risk score across all patients was higher on all 60 days pre and post HF event with readmission compared to HF events with no readmission (p&lt;0.001) and both were higher compared to follow-up period with no events (p&lt;0.001). The risk score recovers less often after HF events which are followed by readmission within 60 days compared to HF events with no readmission. Conclusions In a large real-world population of patients with ICD/CRTD devices, the average integrated diagnostics risk score was higher before and after HF events with readmission compared to HF events with no readmission. Re-admissions are more likely in patients with smaller risk score recovery after HF events. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic Plc

The use of integrated diagnostics to ensure the economic security of the enterprise

Настоящая статья отражает исследование в области определения методов и инструментов диагностики экономической безопасности субъекта предпринимательства. Диагностика являет собой такую концептуальную систему, которая действенно препятствует возникающим угрозам и опасностям функционирования предприятия. Для оптимальной диагностики рекомендуется использование ее многоконтурной модели, дающей возможность нарастить экономическую безопасность. This article reflects a study in the field of determining methods and tools for diagnosing the economic security of a business entity. Diagnostics is such a conceptual system that effectively prevents emerging threats and dangers of the enterprise. For optimal diagnosis, it is recommended to use its multi-circuit model, which makes it possible to increase economic security.

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.