Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening.

INTRODUCTION: Physicians spend an ever-rising amount of time to collect relevant information from highly variable medical reports and integrate them into the patient's health condition. OBJECTIVES: We compared synoptic reporting based on data elements to narrative reporting in order to evaluate its capabilities to collect and integrate clinical information. METHODS: We developed a novel system to align medical reporting to data integration requirements and tested it in prostate cancer screening. We compared expenditure of time, data quality, and user satisfaction for data acquisition, integration, and evaluation. RESULTS: In a total of 26 sessions, 2 urologists, 2 radiologists, and 2 pathologists conducted the diagnostic work-up for prostate cancer screening with both narrative reporting and the novel system. The novel system led to a significantly reduced time for collection and integration of patient information (91%, p < 0.001), reporting in radiology (44%, p < 0.001) and pathology (33%, p = 0.154). The system usage showed a high positive effect on evaluated data quality parameters completeness, format, understandability, as well as user satisfaction. CONCLUSION: This study provides evidence that synoptic reporting based on data elements is effectively reducing time for collection and integration of patient information. Further research is needed to assess the system's impact for different patient journeys.

Structured Data Acquisition in Oncology.

BACKGROUND: Demographic changes and improvement in therapy have shifted the focus of treatment towards chronic diseases and multiple health conditions. This has caused a tremendous increase in data per patient that needs to be integrated longitudinally and across departmental silos. The general increase in the volume of data per diagnostic examination and the number of diagnostic procedures per diagnostic pathway additionally accentuate this data integration challenge. SUMMARY: Subspecialization in medicine has led to largely autonomously organized departments with in-dependent IT ecosystems. This patchwork of IT infrastructure is not prepared to meet the data integration challenge. The resulting lack of integrated information makes the treatment of chronically ill patients increasingly difficult and error prone. Key Message: A sustainable method for data ac-quisition is needed to aid multimodal treatment and improve efficiency in healthcare.

Comparative metabolic analysis in head and neck cancer and the normal gingiva.

OBJECTIVES: Chronic accumulation of lactate in malignant tumor tissue is associated with increased malignancy and radioresistance. For this study, biopsies of primary head and neck squamous cell carcinoma (HNSCC) and of the normal gingiva of the same patient were compared via metabolic profiling to the healthy gingiva from cancer-free patients. MATERIALS AND METHODS: Cryobiopsies of 140 HNSCC patients were used to determine ATP, lactate, and glucose concentrations of the tumor and normal gingiva via induced metabolic bioluminescence imaging (imBI). Additionally, these metabolites were quantified in a collective of 79 healthy (non-tumor-bearing) patients. Furthermore, tumor samples were analyzed via immunofluorescence imaging and quantitative real-time PCR for the expression of lactate and glucose transporters. RESULTS: There were significant differences in ATP concentrations detectable between the tumor, normal gingiva of tumor patients, and gingiva from healthy patients. Lactate concentrations were significantly increased in tumor tissue compared to the normal gingiva of tumor patients as well as the gingiva from healthy patients. Concerning glucose, there was a significant decrease in glucose concentrations detectable in the tumor biopsies compared to the normal gingiva of tumor patients. On the other hand, tumor samples from patients revealed significantly elevated relative expression levels of monocarboxylate transporters (MCT-1 and MCT-4), as well as glucose transporters (GLUT-1 and GLUT-3) compared to the corresponding normal gingiva of each patient. CONCLUSIONS: We could demonstrate that the lactate concentration in HNSCC correlates with primary tumor (T) stage. CLINICAL RELEVANCE: The aim of this study was to identify metabolic parameters to improve early cancer diagnosis, allow predictions on the degree of malignancy, and contribute to a personalized tumor therapy.

Utilizing Learning Analytics in Radiology: A Pilot Study of an e-Learning Platform in Medical Student Education.

RATIONALE AND OBJECTIVES: Learning analytics is a rapidly advancing scientific field that enables data-driven insights and personalized learning experiences. However, traditional methods for teaching and assessing radiology skills do not provide the data needed to leverage this technology in radiology education. MATERIALS AND METHODS: In this paper, we implemented rapmed.net, an interactive radiology e-learning platform designed to utilize learning analytics tools in radiology education. Second-year medical students' pattern recognition skills were evaluated using time to solve a case, dice score, and consensus score, while their interpretation abilities were assessed through multiple-choice questions (MCQs). Assessments were conducted before and after a pulmonary radiology block to examine the learning progress. RESULTS: Our results show that a comprehensive assessment of students' radiological skills using consensus maps, dice scores, time metrics, and MCQs revealed shortcomings traditional MCQs would not have detected. Learning analytics tools allow for a better understanding of students' radiology skills and pave the way for a data-driven educational approach in radiology. CONCLUSION: As one of the most important skills for physicians across all disciplines, improving radiology education will contribute to better healthcare outcomes.

Swiss digital pathology recommendations: results from a Delphi process conducted by the Swiss Digital Pathology Consortium of the Swiss Society of Pathology.

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow-compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for "SDiPath Recommendations for Digital Pathology". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage.

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view.

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices.

Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening.

PURPOSE: Rising complexity of patients and the consideration of heterogeneous information from various IT systems challenge the decision-making process of urological oncologists. Siemens AI Pathway Companion is a decision support tool that provides physicians with comprehensive patient information from various systems. In the present study, we examined the impact of providing organized patient information in comprehensive dashboards on information quality, effectiveness, and satisfaction of physicians in the clinical decision-making process. METHODS: Ten urologists in our department performed the entire diagnostic workup to treatment decision for 10 patients in the prostate cancer screening setting. Expenditure of time, information quality, and user satisfaction during the decision-making process with AI Pathway Companion were recorded and compared to the current workflow. RESULTS: A significant reduction in the physician's expenditure of time for the decision-making process by -59.9% (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated information quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94). CONCLUSION: The software demonstrated that comprehensive organization of information improves physician's effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

COVID-19 Autopsies Reveal Underreporting of SARS-CoV-2 Infection and Scarcity of Co-infections.

Coronavirus disease 2019 (COVID-19) mortality can be estimated based on reliable mortality data. Variable testing procedures and heterogeneous disease course suggest that a substantial number of COVID-19 deaths is undetected. To address this question, we screened an unselected autopsy cohort for the presence of SARS-CoV-2 and a panel of common respiratory pathogens. Lung tissues from 62 consecutive autopsies, conducted during the first and second COVID-19 pandemic waves in Switzerland, were analyzed for bacterial, viral and fungal respiratory pathogens including SARS-CoV-2. SARS-CoV-2 was detected in 28 lungs of 62 deceased patients (45%), although only 18 patients (29%) were reported to have COVID-19 at the time of death. In 23 patients (37% of all), the clinical cause of death and/or autopsy findings together with the presence of SARS-CoV-2 suggested death due to COVID-19. Our autopsy results reveal a 16% higher SARS-CoV-2 infection rate and an 8% higher SARS-CoV-2 related mortality rate than reported by clinicians before death. The majority of SARS-CoV-2 infected patients (75%) did not suffer from respiratory co-infections, as long as they were treated with antibiotics. In the lungs of 5 patients (8% of all), SARS-CoV-2 was found, yet without typical clinical and/or autopsy findings. Our findings suggest that underreporting of COVID-19 contributes substantially to excess mortality. The small percentage of co-infections in SARS-CoV-2 positive patients who died with typical COVID-19 symptoms strongly suggests that the majority of SARS-CoV-2 infected patients died from and not with the virus.

Evaluation of liver fibrosis and cirrhosis on the basis of quantitative T1 mapping: Are acute inflammation, age and liver volume confounding factors?

PURPOSE: To evaluate potential confounding factors in the quantitative assessment of liver fibrosis and cirrhosis using T1 relaxation times. METHODS: The study population is based on a radiology-information-system database search for abdominal MRI performed from July 2018 to April 2019 at our institution. After applying exclusion criteria 200 (59 ±â€¯16 yrs) remaining patients were retrospectively included. 93 patients were defined as liver-healthy, 40 patients without known fibrosis or cirrhosis, and 67 subjects had a clinically or biopsy-proven liver fibrosis or cirrhosis. T1 mapping was performed using a slice based look-locker approach. A ROI based analysis of the left and the right liver was performed. Fat fraction, R2*, liver volume, laboratory parameters, sex, and age were evaluated as potential confounding factors. RESULTS: T1 values were significantly lower in healthy subjects without known fibrotic changes (1.5 T MRI: 575 ±â€¯56 ms; 3 T MRI: 857 ±â€¯128 ms) compared to patients with acute liver disease (1.5 T MRI: 657 ±â€¯73 ms, p < 0.0001; 3 T MRI: 952 ±â€¯37 ms, p = 0.028) or known fibrosis or cirrhosis (1.5 T MRI: 644 ±â€¯83 ms, p < 0.0001; 3 T MRI: 995 ±â€¯150 ms, p = 0.018). T1 values correlated moderately with the Child-Pugh stage at 1.5 T (p = 0.01, ρ = 0.35). CONCLUSION: T1 mapping is a capable predictor for detection of liver fibrosis and cirrhosis. Especially age is not a confounding factor and, hence, age-independent thresholds can be defined. Acute liver diseases are confounding factors and should be ruled out before employing T1-relaxometry based thresholds to screen for patients with liver fibrosis or cirrhosis.

Lethal COVID-19: Radiologic-Pathologic Correlation of the Lungs.

PURPOSE: The purpose of this retrospective study was to correlate CT patterns of fatal cases of coronavirus disease 2019 (COVID-19) with postmortem pathology observations. MATERIALS AND METHODS: The study included 70 lung lobes of 14 patients who died of reverse-transcription polymerase chain reaction-confirmed COVID-19. All patients underwent antemortem CT and autopsy between March 9 and April 30, 2020. Board-certified radiologists and pathologists performed lobewise correlations of pulmonary observations. In a consensus reading, 267 radiologic and 257 histopathologic observations of the lungs were recorded and systematically graded according to severity. These observations were matched and evaluated. RESULTS: Predominant CT observations were ground-glass opacities (GGO) (59/70 lobes examined) and areas of consolidation (33/70). The histopathologic observations were consistent with diffuse alveolar damage (70/70) and capillary dilatation and congestion (70/70), often accompanied by microthrombi (27/70), superimposed acute bronchopneumonia (17/70), and leukocytoclastic vasculitis (7/70). Four patients had pulmonary emboli. Bronchial wall thickening at CT histologically corresponded with acute bronchopneumonia. GGOs and consolidations corresponded with mixed histopathologic observations, including capillary dilatation and congestion, interstitial edema, diffuse alveolar damage, and microthrombosis. Vascular alterations were prominent observations at both CT and histopathology. CONCLUSION: A significant proportion of GGO correlated with the pathologic processes of diffuse alveolar damage, capillary dilatation and congestion, and microthrombosis. Our results confirm the presence and underline the importance of vascular alterations as key pathophysiologic drivers in lethal COVID-19.Supplemental material is available for this article.© RSNA, 2020.

Two distinct immunopathological profiles in autopsy lungs of COVID-19.

Coronavirus Disease 19 (COVID-19) is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has grown to a worldwide pandemic with substantial mortality. Immune mediated damage has been proposed as a pathogenic factor, but immune responses in lungs of COVID-19 patients remain poorly characterized. Here we show transcriptomic, histologic and cellular profiles of post mortem COVID-19 (n = 34 tissues from 16 patients) and normal lung tissues (n = 9 tissues from 6 patients). Two distinct immunopathological reaction patterns of lethal COVID-19 are identified. One pattern shows high local expression of interferon stimulated genes (ISGhigh) and cytokines, high viral loads and limited pulmonary damage, the other pattern shows severely damaged lungs, low ISGs (ISGlow), low viral loads and abundant infiltrating activated CD8+ T cells and macrophages. ISGhigh patients die significantly earlier after hospitalization than ISGlow patients. Our study may point to distinct stages of progression of COVID-19 lung disease and highlights the need for peripheral blood biomarkers that inform about patient lung status and guide treatment.