A blended learning approach for teaching thoracic radiology to medical students: a proof-of-concept study

IntroductionThe best way to impart knowledge to medical students is still unclear. Therefore, we designed a blended learning course in thoracic radiology including both “traditional” in-class time as well as online learning modules. The aims were (1) to investigate students’ attitudes toward this blended learning approach; and (2) to test whether it improved their knowledge about thoracic radiology.MethodsA prospective study was conducted at the local medical center; 156 fourth-year medical students completed this study. Before and after the course, students had to complete (1) questionnaires to investigate their attitudes (7-point Likert scale); and (2) an objective test to assess their knowledge (multiple-choice/free text questions; results as % of correct answers).ResultsRegarding (1), the course led to an improvement in all items compared to baseline, exemplary: interest in thoracic radiology (precourse 4.2 vs. 5.4 postcourse) and the fulfillment of students’ expressed requirements regarding the teaching content (4.5 precourse vs. 6.2 postcourse). Furthermore, the great majority (88%) of our participants wished for more online learning offerings in the future. Regarding (2), the course led to improved knowledge on the objective test (precourse: 40% vs. postcourse: 63% correct answers).ConclusionThis feasibility study showed the successful design and implementation of a blended learning approach in thoracic radiology. Furthermore, it revealed medical students’ positive attitudes toward this approach and showed an increased knowledge in thoracic radiology. Thus, such approaches might be used to enrich the teaching armamentarium in medical education and to further enhance interest and knowledge in thoracic diseases among medical students

Textbook Outcome After Trans-arterial Chemoembolization for Hepatocellular Carcinoma

PurposeTextbook Outcome (TO) is inclusive of quality indicators and it not been provided for trans-arterial chemoembolization (TACE) for hepatocellular carcinoma (HCC).Materials and methodsData on treatment-naïve HCC patients receiving TACE from 10 centers were reviewed. TO was defined as "no post-TACE grade 3-4 complications, no prolonged hospital stay (defined as a post-procedure stay ≤ 75th percentile of the median values from the total cohort), no 30-day mortality/readmission and the achievement of an objective response (OR) at post-TACE imaging." Grade of adverse event was classified according to the Common Terminology Criteria for Adverse Events and short-term efficacy was assessed by response. Pooled estimates were calculated to account for hospital's effect and risk-adjustment was applied to allow for diversity of patients in each center.ResultsA total of 1124 patients (2014-2018) fulfilling specific inclusion criteria were included. Baseline clinical features showed considerable heterogeneity (I2 > 0.75) across centers. TACE-related mortality was absent in 97.6%, readmission was not required after 94.9% of procedures, 91.5% of patients had no complication graded 3-4, 71.8% of patients did not require prolonged hospitalization, OR of the target lesion was achieved in 68.5%. Risk-adjustment showed that all indicators were achieved in 43.1% of patients, and this figure was similar across centers. The median overall survival for patients who achieved all indicators was 33.1 months, 11.9 months longer than for patients who did not.ConclusionsA useful benchmark for TACE in HCC patients has been developed, which provides an indication of survival and allows for a comparison of treatment quality across different hospitals

The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures-the number of 'corona' words in each report and the average text negativity of the sentences mentioning corona in each industry-that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.ISSN:2662-999

The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis

Outcome after Resection for Hepatocellular Carcinoma in Noncirrhotic Liver—A Single Centre Study

Liver cirrhosis is the most common risk factor for the development of hepatocellular carcinoma (HCC). However, 10 to 15% of all HCC arise in a non-cirrhotic liver. Few reliable data exist on outcome after liver resection in a non-cirrhotic liver. The aim of this single-centre study was to evaluate the outcome of resection for HCC in non-cirrhotic liver (NC-HCC) and to determine prognostic factors for overall (OS) and intrahepatic recurrence-free (RFS) survival. From 2008 to 2020, a total of 249 patients were enrolled in this retrospective study. Primary outcome was OS and RFS. Radiological and pathological findings, such as tumour size, number of nodules, Tumour-, Nodes-, Metastases- (TNM) classification and vascular invasion as well as extent of surgical resection and laboratory liver function were collected. Here, 249 patients underwent liver resection for NC-HCC. In this case, 50% of patients underwent major liver resection, perioperative mortality was 6.4%. Median OS was 35.4 months (range 1–151 months), median RFS was 10.5 months (range 1–128 moths). Tumour diameter greater than three centimetres, multifocal tumour disease, vascular invasion, preoperative low albumin and increased alpha-fetoprotein (AFP) values were associated with significantly worse OS. Our study shows that resection for NC-HCC is an acceptable treatment approach with comparatively good outcome even in extensive tumours

Natural language processing for automatic evaluation of free-text answers — a feasibility study based on the European Diploma in Radiology examination

Abstract Background Written medical examinations consist of multiple-choice questions and/or free-text answers. The latter require manual evaluation and rating, which is time-consuming and potentially error-prone. We tested whether natural language processing (NLP) can be used to automatically analyze free-text answers to support the review process. Methods The European Board of Radiology of the European Society of Radiology provided representative datasets comprising sample questions, answer keys, participant answers, and reviewer markings from European Diploma in Radiology examinations. Three free-text questions with the highest number of corresponding answers were selected: Questions 1 and 2 were “unstructured” and required a typical free-text answer whereas question 3 was “structured” and offered a selection of predefined wordings/phrases for participants to use in their free-text answer. The NLP engine was designed using word lists, rule-based synonyms, and decision tree learning based on the answer keys and its performance tested against the gold standard of reviewer markings. Results After implementing the NLP approach in Python, F1 scores were calculated as a measure of NLP performance: 0.26 (unstructured question 1, n = 96), 0.33 (unstructured question 2, n = 327), and 0.5 (more structured question, n = 111). The respective precision/recall values were 0.26/0.27, 0.4/0.32, and 0.62/0.55. Conclusion This study showed the successful design of an NLP-based approach for automatic evaluation of free-text answers in the EDiR examination. Thus, as a future field of application, NLP could work as a decision-support system for reviewers and support the design of examinations being adjusted to the requirements of an automated, NLP-based review process. Clinical relevance statement Natural language processing can be successfully used to automatically evaluate free-text answers, performing better with more structured question-answer formats. Furthermore, this study provides a baseline for further work applying, e.g., more elaborated NLP approaches/large language models. Key points • Free-text answers require manual evaluation, which is time-consuming and potentially error-prone. • We developed a simple NLP-based approach — requiring only minimal effort/modeling — to automatically analyze and mark free-text answers. • Our NLP engine has the potential to support the manual evaluation process. • NLP performance is better on a more structured question-answer format. Graphical Abstrac

Quantum iterative reconstruction on a photon-counting detector CT improves the quality of hepatocellular carcinoma imaging

Abstract Background Excellent image quality is crucial for workup of hepatocellular carcinoma (HCC) in patients with liver cirrhosis because a signature tumor signal allows for non-invasive diagnosis without histologic proof. Photon-counting detector computed tomography (PCD-CT) can enhance abdominal image quality, especially in combination with a novel iterative reconstruction algorithm, quantum iterative reconstruction (QIR). The purpose of this study was to analyze the impact of different QIR levels on PCD-CT imaging of HCC in both phantom and patient scans. Methods Virtual monoenergetic images at 50 keV were reconstructed using filtered back projection and all available QIR levels (QIR 1–4). Objective image quality properties were investigated in phantom experiments. The study also included 44 patients with triple-phase liver PCD-CT scans of viable HCC lesions. Quantitative image analysis involved assessing the noise, contrast, and contrast-to-noise ratio of the lesions. Qualitative image analysis was performed by three raters evaluating noise, artifacts, lesion conspicuity, and overall image quality using a 5-point Likert scale. Results Noise power spectra in the phantom experiments showed increasing noise suppression with higher QIR levels without affecting the modulation transfer function. This pattern was confirmed in the in vivo scans, in which the lowest noise levels were found in QIR-4 reconstructions, with around a 50% reduction in median noise level compared with the filtered back projection images. As contrast does not change with QIR, QIR-4 also yielded the highest contrast-to-noise ratios. With increasing QIR levels, rater scores were significantly better for all qualitative image criteria (all p < .05). Conclusions Without compromising image sharpness, the best image quality of iodine contrast optimized low-keV virtual monoenergetic images can be achieved using the highest QIR level to suppress noise. Using these settings as standard reconstruction for HCC in PCD-CT imaging might improve diagnostic accuracy and confidence

Recurrent novel HMGA2-NCOR2 fusions characterize a subset of keratin-positive giant cell-rich soft tissue tumors

Giant cell tumors of soft tissue (GCT-ST) are rare low-grade neoplasms that were at one time thought to represent the soft tissue counterparts of GCT of bone (GCT-B) but are now known to lack the H3F3 mutations characteristic of osseous GCT. We present six distinctive giant cell-rich soft tissue neoplasms that expressed keratins and carried a recurrent HMGA2-NCOR2 gene fusion. Patients were five females and one male aged 14–60 years (median, 29). All presented with superficial (subcutaneous) masses that were removed by conservative marginal (3) or wide (2) local excision. The tumors originated in the upper extremity (2), lower extremity (2), head/neck (1), and trunk (1). Five patients with follow-up (median, 21 months; range, 14–168) remained disease-free. Grossly, all tumors were well-demarcated but not encapsulated with variable lobulation. Histologically, they were composed of bland plump epithelioid or ovoid to spindled mononuclear cells admixed with evenly distributed multinucleated osteoclast-type giant cells. Foci of stromal hemorrhage and hemosiderin were seen in all cases. The mitotic activity ranged from 2 to 14/10 high power fields (median: 10). Foci of necrosis and vascular invasion were seen in one case each. The mononuclear cells were immunoreactive with the AE1/AE3 keratin cocktail and less frequently/less diffusely for K7 and K19 but lacked expression of other lineage-associated markers. RNA-based next-generation sequencing revealed an HMGA2-NCOR2 fusion in all tumors. None of the keratin-negative conventional GCT-ST showed the HMGA2-NCOR2 fusion (0/7). Metaplastic bone (4/9) and SATB2 expression (3/4) were frequent in keratin-negative conventional GCT-ST but were lacking in keratin-positive HMGA2-NCOR2 fusion-positive tumors. The distinctive immunophenotype and genotype of these tumors strongly suggest that they represent a discrete entity, differing from conventional GCT-ST and other osteoclast-rich morphologic mimics. Their natural history appears favorable, although a study of additional cases and longer follow-up are warranted

Endovascular simulation training: a tool to increase enthusiasm for interventional radiology among medical students

Objectives: Interventional radiology (IR) is a growing field but is underrepresented in most medical school curricula. We tested whether endovascular simulator training improves medical students' attitudes towards IR. Materials and methods: We conducted this prospective study at two university medical centers; overall, 305 fourth-year medical students completed a 90-min IR course. The class consisted of theoretical and practical parts involving endovascular simulators. Students completed questionnaires before the course, after the theoretical and after the practical part. On a 7-point Likert scale, they rated their interest in IR, knowledge of IR, attractiveness of IR, and the likelihood to choose IR as subspecialty. We used a crossover design to prevent position-effect bias. Results: The seminar/simulator parts led to the improvement for all items compared with baseline: interest in IR (pre-course 5.2 vs. post-seminar/post-simulator 5.5/5.7), knowledge of IR (pre-course 2.7 vs. post-seminar/post-simulator 5.1/5.4), attractiveness of IR (pre-course 4.6 vs. post-seminar/post-simulator 4.8/5.0), and the likelihood of choosing IR as a subspecialty (pre-course 3.3 vs. post-seminar/post-simulator 3.8/4.1). Effect was significantly stronger for simulator training compared with that for seminar for all items (p < 0.05). For simulator training, subgroup analysis of students with pre-existing positive attitude showed considerable improvement regarding "interest in IR" (× 1.4), "knowledge of IR" (× 23), "attractiveness of IR" (× 2), and "likelihood to choose IR" (× 3.2) compared with pretest. Conclusion: Endovascular simulator training significantly improves students' attitude towards IR regarding all items. Implementing such courses at a very early stage in the curriculum should be the first step to expose medical students to IR and push for IR. Key points: • Dedicated IR-courses have a significant positive effect on students' attitudes towards IR. • Simulator training is superior to a theoretical seminar in positively influencing students' attitudes towards IR. • Implementing dedicated IR courses in medical school might ease recruitment problems in the field