CoRad-19 - Modular Digital Teaching during the SARS-CoV-2 Pandemic

PURPOSE

 During the SARS-CoV-2 pandemic, higher education worldwide had to switch to digital formats. The purpose of this study was to evaluate CoRad-19, a digital teaching tool created by the German Radiological Society for medical students during the COVID-19 pandemic.

MATERIALS AND METHODS

 A total of 13 German-speaking universities implemented CoRad-19 in their curriculum and partially or completely replaced their classes with the online courses. Previous experience and contact with radiology and the participants' opinions regarding the medium of e-learning were surveyed using a custom questionnaire. The subjective level of knowledge regarding the individual modules was also surveyed before and after participation to measure learning effects. The data of 994 medical students from the participating sites were analyzed and compared intraindividually using the Friedman test.

RESULTS

 From 4/1/2020-10/1/2020, 451 complete data sets from a total of 994 surveys were included. E-learning was rated "very useful" both before and after course participation (4 [IQR 3-4], p = 0.527, r = 0.16). E-learning as a method was also rated as a "very good" medium both before and after participation (4 [IQR 3-4], p = 0.414, r = 0.17). After participation, participants rated radiology as particularly suitable for digital teaching (before: 3 [IQR 3-4] vs. after 4 [IQR 3-4], p = 0.005, r = 0.6). Significant learning gains were measurable in all course modules (p ≤ 0.009). Post-hoc analysis showed interest in radiology to increase significantly after course participation (p = 0.02).

CONCLUSION

 In the representative survey, significant learning effects were observed in all course modules. In addition, it should be particularly emphasized that the students' interest in radiology was increased by course participation. Thus, the German Radiological Society provided significant support to German-speaking medical faculties with respect to maintaining excellent education using CoRad-19.

KEY POINT

· Co-Rad-19 course participation results in measurable subjective learning effects and increases student interest in radiology..

CITATION FORMAT

· Brendlin AS, Molwitz I, Oechtering TH et al. CoRad-19 - Modular Digital Teaching during the SARS-CoV-2 Pandemic. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1752-0624.

Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage

PURPOSE

 To investigate reduction of radiation exposure in unenhanced CT in suspicion of renal calculi using a tin-filtered high tube voltage protocol compared to a standard low-dose protocol without spectral shaping.

MATERIALS AND METHODS

 A phantom study using 7 human renal calculi was performed to test both protocols. 120 consecutive unenhanced CT examinations performed due to suspicion of renal calculi were included in this retrospective, monocentric study. 60 examinations were included with the standard-dose protocol (SP) (100 kV/130 mAs), whereas another 60 studies were included using a low-dose protocol (LD) applying spectral shaping with tin filtration of high tube voltages (Sn150 kV/80 mAs). Image quality was assessed by two radiologists in consensus blinded to technical parameters using an equidistant Likert scale ranging from 1-5 with 5 being the highest score. Quantitative image quality was assessed using regions of interest in abdominal organs, muscles, and adipose tissue to analyze image noise and signal-to-noise ratios (SNR). Commercially available dosimetry software was used to determine and compare effective dose (ED) and size-specific dose estimates (SSDEmean).

RESULTS

 All seven renal calculi of the phantom could be detected with both protocols. There was no difference regarding calcluli size between the two protocols except for the smallest one. The smallest concretion measured 1.5 mm in LD and 1.0 mm in SP (ground truth 1.5 mm). CTDIvol was 3.36 mGy in LD (DLP: 119.3 mGycm) and 8.27 mGy in SP (DLP: 293.6 mGycm). The mean patient age in SP was 47 ± 17 years and in LD 49 ± 13 years. Ureterolithiasis was found in 33 cases in SP and 32 cases in LD. The median concretion size was 3 mm in SP and 4 mm in LD. The median ED in LD was 1.3 mSv (interquartile range (IQR) 0.3 mSv) compared to 2.3 mSv (IQR 0.9 mSv) in SP (p < 0.001). The SSDEmean of LD was also significantly lower compared to SP with 2.4 mGy (IQR 0.4 mGy) vs. 4.8 mGy (IQR 2.3 mGy) (p < 0.001). The SNR was significantly lower in LD compared to SP (p < 0.001). However, there was no significant difference between SP and LD regarding the qualitative assessment of image quality with a median of 4 (IQR 1) for both groups (p = 0.648).

CONCLUSION

Tin-filtered unenhanced abdominal CT for the detection of renal calculi using high tube voltages leads to a significant reduction of radiation exposure and yields high diagnostic image quality without a significant difference compared to the institution's standard of care low-dose protocol without tin filtration.

KEY POINTS

  · Tin-filtered CT for the detection of renal calculi significantly reduces radiation dose.. · The application of tin filtration provides comparable diagnostic image quality to that of SP protocols.. · An increase in image noise does not hamper diagnostic image quality..

CITATION FORMAT

· Gassenmaier S, Winkelmann MT, Magnus J et al. Low-Dose CT for Renal Calculi Detection Using Spectral Shaping of High Tube Voltage. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1752-0472.

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma

BACKGROUND

To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.

MATERIAL AND METHODS

A total of 140 consecutive patients with melanoma (58 female, 63±16 years) for whom baseline DECT tumor load assessment revealed stage IV and who were subsequently treated with immunotherapy were included. Best response was determined using the clinical reports (81 responders: 27 complete response, 45 partial response, 9 stable disease). Individual lesion response was classified manually analogous to RECIST 1.1 through 1291 follow-up examinations on a total of 776 lesions (6.7±7.2 per patient). The patients were sorted chronologically into a study and a validation cohort (each n=70). The baseline DECT was examined using specialized tumor segmentation prototype software, and radiomic features were analyzed for response predictors. Significant features were selected using univariate statistics with Bonferroni correction and multiple logistic regression. The area under the receiver operating characteristic curve of the best subset was computed (AUROC). For each combination (SECT/DECT and patient response/lesion response), an individual random forest classifier with 10-fold internal cross-validation was trained on the study cohort and tested on the validation cohort to confirm the predictive performance.

RESULTS

We performed manual RECIST 1.1 response analysis on a total of 6533 lesions. Multivariate statistics selected significant features for patient response in SECT (min. brightness, R²=0.112, padj. ≤0.001) and DECT (textural coarseness, R²=0.121, padj. ≤0.001), as well as lesion response in SECT (mean absolute voxel intensity deviation, R²=0.115, padj. ≤0.001) and DECT (iodine uptake metrics, R²≥0.12, padj. ≤0.001). Applying the machine learning models to the validation cohort confirmed the additive predictive power of DECT (patient response AUROC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001).

CONCLUSION

The new method of DECT-specific radiomic analysis provides a significant additive value over SECT radiomics approaches for response prediction in patients with metastatic melanoma preceding immunotherapy, especially on a lesion-based level. As mixed tumor response is not uncommon in metastatic melanoma, this lends a powerful tool for clinical decision-making and may potentially be an essential step toward individualized medicine.