Background: Intraoperative frozen sections (FS) are frequently used to establish the diagnosis of lung cancer when preoperative examinations are not conclusive. The downside of FS is its resource-intensive nature and the risk of tissue depletion when small lesions are assessed. Ex vivo fluorescence confocal microscopy (FCM) is a novel microimaging method for loss-free examinations of native materials. We tested its suitability for the intraoperative diagnosis of lung tumors. Methods: Samples from 59 lung resection specimens containing 45 carcinomas were examined in the FCM. The diagnostic performance in the evaluation of malignancy and histological typing of lung tumors was evaluated in comparison with FS and the final diagnosis. Results: A total of 44/45 (98%) carcinomas were correctly identified as malignant in the FCM. A total of 33/44 (75%) carcinomas were correctly subtyped, which was comparable with the results of FS and conventional histology. Our tests documented the excellent visualization of cytological features of normal tissues and tumors. Compared to FS, FCM was technically less demanding and less personnel intensive. Conclusions: The ex vivo FCM is a fast, effective, and safe method for diagnosing and subtyping lung cancer and is, therefore, a promising alternative to FS. The method preserves the tissue without loss for subsequent examinations, which is an advantage in the diagnosis of small tumors and for biobanking.

Background/Objectives: To determine the inter-rater agreement of visual and AI-based assessments of a renowned semi-quantitative chest CT scoring system (Pan-score) used to evaluate the severity of pulmonary involvement (e.g., ground-glass opacities, consolidations) in patients suffering from COVID-19. Methods: This retrospective study includes patients with PCR-confirmed COVID-19, who received a chest CT scan (not more than three days prior to or after the positive PCR test) between 21 March 2020 and 30 December 2022. The five lung lobes were scored separately on a scale from 0 (no pulmonary involvement) to 5 (>75% pulmonary involvement) by a radiology specialist, an experienced assistant physician, a medical student, and a dedicated AI-based software tool for chest CT. Weighted Cohen’s κ values were calculated to assess the reliability of agreement between the different readers. Results: A total of 569 consecutive patients (381 males [67.0%], 188 females [33.0%]; mean age 68.8 years) with confirmed COVID-19 were evaluated. All of them received at least one chest CT scan. There was a significant difference (p < 0.001) between the mean Pan-score evaluated by the three human readers (9.35 ± 6.03) and the score computed fully automatically by the software (10.44 ± 5.10). However, the inter-rater agreement both between the three different human readers and between the human readers and the AI was high throughout, with κ values of 0.71–0.86 and 0.83, respectively. The slice thickness of the reconstructed CT images did not have an impact on the inter-rater agreement, but the total score was significantly higher when the images were acquired following the administration of i. v. contrast media. Conclusions: The evaluated chest CT scoring system is user-friendly due to its simplicity, though it is generally prone to inaccuracies, since the estimation of the extent of pulmonary involvement is quite subjective. Nevertheless, the inter-rater agreement was high throughout, both between the differently experienced human readers and between the human readers and the AI software. In summary, the Pan-score seems to be a reliable approach to estimate the extent of pulmonary involvement in patients suffering from COVID-19.

Background/Objectives: Investigation of whether visual and AI-based assessments of the severity of COVID-19 pneumonia using an established semi-quantitative chest CT scoring system (Pan score) correlate with laboratory parameters as well as pulmonary function, and of the score’s diagnostic value in predicting the patients’ clinical outcome. Methods: This retrospective analysis comprises patients with PCR-confirmed COVID-19 who received a chest CT scan (not more than three days prior to or after the positive PCR test) between 12 August 2020, and 30 December 2022. Each of the five lung lobes was assessed separately using a scoring system ranging from 0 (no pulmonary involvement) to 5 (>75% pulmonary involvement) by a radiology specialist, an experienced resident physician, a medical student, and a dedicated AI-based chest CT software tool. In addition, pulmonary function and laboratory parameters, the duration of ICU stays and of any required mechanical ventilation, as well as the clinical outcome (discharge vs. death) were recorded, and their correlation with the obtained CT score was analysed. Statistical analyses comprised descriptive baseline comparisons using non-parametric tests, bivariate correlation matrices, and ROC curves to assess diagnostic accuracy. Furthermore, multivariable logistic, ordinal, and age-adjusted spline regression models were constructed to calculate odds ratios and estimate predicted probabilities for cumulative ICU and mechanical ventilation duration thresholds. Results: In total, 351 consecutive patients with confirmed COVID-19 (223 males [63.5%], 128 females [36.5%]; mean age 67.0 years) were included, all of whom underwent at least one chest CT scan. Compared with patients who were discharged, deceased patients had a significantly (p < 0.05) higher mean Pan score (11.7 ± 6.0 vs. 8.8 ± 5.0), higher rates of mechanical ventilation (61.3 vs. 33.0%), and both a higher incidence (42.7 vs. 25.4%) and longer duration (10.5 [6.0–20.0] vs. 6.0 [3.0–10.0] days) of ICU stays. The Pan score showed a moderate and consistent association with the requirement for mechanical ventilation (ρ = 0.49; q < 0.001) and the duration of the ICU stay (ρ = 0.43; q < 0.001). Conclusions: The investigated semi-quantitative CT score is a simple, reliable tool for assessing the extent of COVID-19 pneumonia and can be evaluated both by radiologists and fully automated AI software. While its predictive value for all-cause mortality was only moderate, it showed good performance in predicting the need for and duration of mechanical ventilation, as well as intensive care requirement.

Background/Objectives: Investigation of whether visual and AI-based assessments of the severity of COVID-19 pneumonia using an established semi-quantitative chest CT scoring system (Pan score) correlate with laboratory parameters as well as pulmonary function, and of the score’s diagnostic value in predicting the patients’ clinical outcome. Methods: This retrospective analysis comprises patients with PCR-confirmed COVID-19, who received a chest CT scan (not more than three days prior to or after the positive PCR test) between March 21, 2020, and December 27, 2021. Each of the five lung lobes was assessed separately using a scoring system ranging from 0 (no pulmonary involvement) to 5 (> 75% pulmonary involvement) by a radiology specialist, an experienced resident physician, a medical student, and a dedicated AI-based chest CT software tool. In addition, pulmonary function and laboratory parameters, the duration of ICU stays and of any required mechanical ventilation, as well as the clinical outcome (discharge vs. death) were recorded, and their correlation with the obtained CT score was analysed. Statistical analyses comprised descriptive baseline comparisons using non-parametric tests, bivariate correlation matrices, and ROC curves to assess diagnostic accuracy. Furthermore, multivariable logistic, ordinal, and age-adjusted spline regression models were constructed to calculate odds ratios and estimate predicted probabilities for cumulative ICU and mechanical ventilation duration thresholds. Results: In total, 351 consecutive patients with confirmed COVID-19 (223 males [63.5%], 128 females [36.5%]; mean age 67.0 years) were included, all of whom underwent at least one chest CT scan. Compared with patients who were discharged, deceased patients had a significantly (p < 0.05) higher mean Pan score (11.7 ± 6.0 vs. 8.8 ± 5.0), higher rates of mechanical ventilation (56.0 vs. 32.1 %), and both a higher incidence (42.7 vs. 25.4 %) and longer duration (10.5 [6.0–20.0] vs. 6.0 [3.0–10.0] days) of ICU stays. The Pan score showed a strong and consistent association with the requirement for mechanical ventilation (r = 0.54; p < 0.01) and the duration of the ICU stay (r = 0.39; p < 0.01). Conclusions: The investigated semi-quantitative CT score is a simple, reliable tool for assessing the extent of COVID-19 pneumonia and can be evaluated both by radiologists and fully automated AI software. While its predictive value for all-cause mortality was only moderate, it showed good performance in predicting the need for and duration of mechanical ventilation, as well as intensive care requirement.

Contrast-enhanced CT is the standard imaging technique in oncological objectives. Rates of missed pathologies depend on work experience of the respective radiologists. Thus the aim of this study is to analyze the eye movements of professionals while reading CT images in order to evaluate whether the eye-fixation patterns and search strategies of experienced radiologists could explain higher detection rates of pathologies and whether such patterns can be learned.Anonymized images of 10 patients were presented to three medical students and six radiologists with different levels of work experience. During image analysis, ocular fixation positions were recorded using an eye-tracking software tool. The CT scans were analyzed retrospectively, considering the individual course of disease with the issue of successful detection of all pathologies. Visual attention and dwell time of ocular fixation on clinically important abnormalities or areas with pathological findings, general search patterns, and time efficiency were assessed. For statistical analysis, interobserver variability and accuracy of lesion detection were evaluated taking into account individual experience.The results revealed that observer sensitivity depends on work experience due to a more systematic order of inspection and a well-known course of disease, e.g. in case of metastatic spread. The areas of missed pathologies mostly included secondary findings. Inexperienced readers changed the stratification considerably more often and required more time for reporting or detecting pathologies.Our results suggest that experienced radiological physicians reduce their amount of missed findings by looking more systematically at images and by applying a more targeted inspection of clinically important regions.