Chest CT in COVID-19 patients: Structured vs conventional reporting

Rethinking radiology reports: A survey of referring physicians' perspectives

BACKGROUND

Radiology reports play a crucial role in clinical decision-making. High report quality and completeness are essential for efficient patient care. However, the clarity and comprehensiveness of radiology reports are often points of contention among referring physicians. This study evaluated referring physicians' views on the quality and clinical utility of radiology reports.

METHODS

A prospective, anonymous online survey was conducted from June 2023 to March 2025, targeting 258 practicing physicians in Germany, including 93 internists, 90 surgeons and 75 general practitioners. The survey included rating scales, multiple-choice questions and net promoter scores (NPS).

RESULTS

Referring physicians' satisfaction with the completeness of radiology reports was moderate, with an average score of 38.4 ± 24.3 on a scale from -100 to + 100. Among the specialties surveyed, surgeons reported the highest level of dissatisfaction (p < 0.05). Internists and surgeons showed significantly stronger preferences for structured reporting compared to general practitioners (p < 0.05). According to the surveyed referring physicians, the most frequently missing information in reports included "determination of a diagnosis" (39 %) and "recommendations for further diagnostic measures or follow-up examinations" (23 %). A large majority of referring physicians (84.9 %) found multidisciplinary case conferences to be valuable (5-7 out of 7 stars) for enhancing their understanding of reports.

CONCLUSION

Reporting preferences vary across specialties and radiologists must address the clinical needs of referring physicians, who are the primary audience for radiology reports. Integrating imaging into multidisciplinary meetings can further improve radiology report comprehension of referring physicians.

Impact of software-assisted structured reporting on radiology residents approaching prostate MRI

PURPOSE

To evaluate the potential advantages of software-assisted structured reporting for radiology residents approaching multiparametric prostate MRI (mpMRI).

METHODS

MpMRI scans from 100 patients, performed for prostate cancer (PCa) detection or staging, were anonymized, and reviewed by six second-year radiology residents without previous experience in prostate MRI, following 6 h of intensive training. The dataset was split into two subsets of 50 cases each. All residents were asked to report scans from the first subset using a basic text processor (narrative reports -NR-). For the second subset, one group used a dedicated software to produce structured reports (SR) while the other continued with NR. Report completeness was assessed using a PI-RADS-based checklist, and statistical analyses, including Wilcoxon rank sum and Pearson's Chi-squared tests, were performed to compare word count, reporting time, and concordance with an expert radiologist's findings.

RESULTS

All readers adopting SR in the second batch demonstrated a significant increase in word count and a decrease in reporting time compared to the first batch. Image quality and final impressions were missing from all NR, while gland size, lesion description, and PI-RADS score were consistently included in nearly all reports (96-100 %). One of the three residents using SR showed a statistically significant improvement in concordance with the expert radiologist on index lesion location and clinically significant PCa presence (p = 0.001), while the other two exhibited positive trends (p = 0.061-0.078).

CONCLUSIONS

The adoption of SR allowed radiology residents to decrease their reporting time and improve the comprehensiveness of their reports, while increasing concordance with an expert radiologist.

Impact of an expert-derived, quick hands-on tool on classifying pulmonary hypertension in chest computed tomography: a study on inexperienced readers using RAPID-CT-PH

PURPOSE

To test the inter-reader agreement in classifying pulmonary hypertension (PH) on chest contrast-enhanced computed tomography (CECT) between a consensus of two cardio-pulmonary-devoted radiologists (CRc) and inexperienced readers (radiology residents, RRs) when using a CECT-based quick hands-on tool built upon PH imaging literature, i.e., the "Rapid Access and Practical Information Digest on Computed Tomography for PH-RAPID-CT-PH".

MATERIAL AND METHODS

The observational study retrospectively included 60 PH patients who underwent CECT between 2015 and 2022. Four RRs independently reviewed all CECTs and classified each case into one of the five PH groups per the 2022 ESC/ERS guidelines. While RR3 and RR4 (RAPID-CT-PH group) used RAPID-CT-PH, RR1 and RR2 (control group) did not. RAPID-CT-PH and control groups' reports were compared with CRc using unweighted Cohen's Kappa (k) statistics. RRs' report completeness and reporting time were also compared using the Wilcoxon-Mann-Whitney test.

RESULTS

The inter-reader agreement in classifying PH between the RAPID-CT-PH group and CRc was substantial (k = 0.75 for RR3 and k = 0.65 for RR4); while, it was only moderate for the control group (k = 0.57 for RR1 and k = 0.49 for RR2). Using RAPID-CT-PH resulted in significantly higher report completeness (all p < 0.0001) and significantly lower reporting time (p < 0.0001) compared to the control group.

CONCLUSION

RRs using RAPID-CT-PH showed a substantial agreement with CRc on CECT-based PH classification. RAPID-CT-PH improved report completeness and reduced reporting time. A quick hands-on tool for classifying PH on chest CECT may help inexperienced radiologists effectively contribute to the PH multidisciplinary team.

Progress in SARS-CoV-2, diagnostic and clinical treatment of COVID-19

Background

Corona Virus Disease 2019(COVID-19)is a global pandemic novel coronavirus infection disease caused by Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Although rapid, large-scale testing plays an important role in patient management and slowing the spread of the disease. However, there has been no good and widely used drug treatment for infection and transmission of SARS-CoV-2.

Key findings

Therefore, this review updates the body of knowledge on viral structure, infection routes, detection methods, and clinical treatment, with the aim of responding to the large-section caused by SARS-CoV-2. This paper focuses on the structure of SARS-CoV-2 viral protease, RNA polymerase, serine protease and main proteinase-like protease as well as targeted antiviral drugs.

Conclusion

In vitro or clinical trials have been carried out to provide deeper thinking for the pathogenesis, clinical diagnosis, vaccine development and treatment of SARS-CoV-2.

Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports

Background

Structured reporting has been demonstrated to increase report completeness and to reduce error rate, also enabling data mining of radiological reports. Still, structured reporting is perceived by radiologists as a fragmented reporting style, limiting their freedom of expression.

Purpose

A deep learning-based natural language processing method was developed to automatically convert unstructured COVID-19 chest CT reports into structured reports.

Methods

Two hundred-two COVID-19 chest CT were retrospectively reviewed by two experienced radiologists, who wrote for each exam a free-form text radiological report and coherently filled the template provided by the Italian Society of Medical and Interventional Radiology, used as ground-truth. A semi-supervised convolutional neural network was implemented to extract 62 categorical variables from the report. Two iterations were carried-out, the first without fine-tuning, the second one performing a fine-tuning. The performance was measured using the mean accuracy and the F1 mean score. An error analysis was performed to identify errors entirely attributable to incorrect processing of the model.

Results

The algorithm achieved a mean accuracy of 93.7% and an F1 score 93.8% in the first iteration. Most of the errors were exclusively attributable to wrong inference (46%). In the second iteration the model achieved for both parameters 95,8% and percentage of errors attributable to wrong inference decreased to 26%.

Conclusions

The convolutional neural network achieved an optimal performance in the automated conversion of free-form text into structured radiological reports, overcoming all the limitation attributed to structured reporting and finally paving the way for data mining of radiological report.

COVID-19 chest CT and laboratory features of B.1.617.2 (Delta variant) vs B.1.1.7 (Alpha variant) surge: a single center case-control study

PURPOSE

To assess clinical, laboratory and radiological differences between Delta and Alpha SARS-CoV-2 variants.

MATERIALS AND METHODS

Twenty SARS-CoV-2 patients admitted from 30th of August to 30th of October 2021 (period with estimated highest prevalence of Delta variant circulation in Italy) were enrolled. Patients were matched in a 1:1 ratio with same gender and same age +/- 2 years controls admitted from 1st of September 2020 to 30th of January 2021 (predominant circulation of Alpha variant). Chest computed tomography (CT) were retrospectively evaluated. Main clinical parameters, radiological and laboratory findings were compared between two groups.

RESULTS

Patients with probable Delta variant had significantly higher CT severity scores, lower PaO2/FiO2 ratio and higher C-reactive protein and lactate dehydrogenase levels at admission. On multivariate analysis, probable Delta variant infection was associated with higher CT severity score. Ground glass opacities and crazy paving patterns were more frequently noticed than consolidation, with the latter being more frequent in Delta cohort, even though not significantly. According to prevalent imaging pattern, the consolidation one was significantly associated with pregnancy (p=0.008).

CONCLUSIONS

Patients admitted during predominance of Delta variant circulation had a more severe lung involvement compared to patients in infected when Alpha variant was predominant. Despite imaging pattern seems to be not influenced by viral variant and other clinical variables, the consolidative pattern was observed more frequently in pregnancy.

CT evaluation of lung infiltrates in the two months preceding the Coronavirus disease 19 pandemic in Canton Ticino (Switzerland): were there suspicious cases before the official first case?

PURPOSE

The main objective of this study was to assess the presence of pulmonary infiltrates with computed tomography (CT) appearance compatible with infection by coronavirus disease 2019 (COVID-19), in Canton Ticino in the 2 months preceding the first official case. Secondary aims were to compare the classification of infiltrates in the same time frame in 2020 and 2019; to compare the number of chest CT scans in the same period; to search for pathological confirmation of the virus.

MATERIALS AND METHODS

Chest CT scans performed between January 1 and February 24 in 2019 and 2020 were collected and classified by COVID-19 Reporting and Data System (CO-RADS). Pathological presence of the virus was searched for when appropriate material was available.

RESULTS

The final cohort included 881 patients. Among the CO-RADS 3 and 4 categories, 30 patients had pneumonitis of unknown etiology. Pathological specimens were available in six patients but they were negative for COVID-19.

CONCLUSION

Before the first official case of COVID-19 infection, in Canton Ticino there were about 30 cases of pneumonitis of uncertain origin, with CT appearance compatible with infection by COVID-19, but with no confirmation of the disease. The number of chest CT scans in the first two months of 2020 was > 12% compared to 2019.

What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists’ written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019–09-01 to 2020–02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1^st principal component summarized the length and lexical diversity of the reports and was significantly influenced by the weekday, time slot, workload, number of examinations previously interpreted by the radiologist during the on-call period, type of examination, emergency level and radiologists’ gender (P value range: < 0.0001–0.0029). The 2^nd principal component summarized negative formulations, polarity and sentence length and was correlated with the number of examination previously interpreted by the radiologist, type of examination, emergency level, imaging modality and radiologists’ experience (P value range: < 0.0001–0.0032). The last principal component summarized questioning, uncertainty and polarity and was correlated with the type of examination and emergency level (all P values < 0.0001). Thus, the length, structure and content of emergency radiological reports were significantly influenced by organizational, radiologist- and examination-related characteristics, highlighting the subjectivity and variability in the way radiologists express themselves during their clinical activity. These findings advocate for more homogeneous practices in radiological reporting and stress the need to consider these influential features when developing models based on natural language processing.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence

Background

Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS).

Methodology

This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis.

Results

The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis.

Conclusions

This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

RSNA and BSTI grading systems of COVID-19 pneumonia: comparison of the diagnostic performance and interobserver agreement

Background

This study aimed to compare the performance and interobservers agreement of cases with findings on chest CT based on the British Society of Thoracic Imaging (BSTI) guideline statement of COVID-19 and the Radiological Society of North America (RSNA) expert consensus statement.

Methods

In this study, 903 patients who had admitted to the emergency department with a pre-diagnosis of COVID-19 between 1 and 18 July 2020 and had chest CT. Two radiologists classified the chest CT findings according to the RSNA and BSTI consensus statements. The performance, sensitivity and specificity values of the two classification systems were calculated and the agreement between the observers was compared by using kappa analysis.

Results

Considering RT-PCR test result as a gold standard, the sensitivity, specificity and positive predictive values were significantly higher for the two observers according to the BSTI guidance statement and the RSNA expert consensus statement (83.3%, 89.7%, 89.0%; % 81.2,% 89.7,% 88.7, respectively). There was a good agreement in the PCR positive group (κ: 0.707; p < 0.001 for BSTI and κ: 0.716; p < 0.001 for RSNA), a good agreement in the PCR negative group (κ: 0.645; p < 0.001 for BSTI and κ: 0.743; p < 0.001 for RSNA) according to the BSTI and RSNA classification between the two radiologists.

Conclusion

As a result, RSNA and BSTI statement provided reasonable performance and interobservers agreement in reporting CT findings of COVID-19. However, the number of patients defined as false negative and indeterminate in both classification systems is at a level that cannot be neglected.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00668-3.

Chest computed tomography as a primary tool in COVID-19 detection: an update meta-analysis

PURPOSE

A growing number of publications have paid close attention to the chest computed tomography (CT) detection of COVID-19 with inconsistent diagnostic accuracy, the present meta-analysis assessed the available evidence regarding the overall performance of chest CT for COVID-19.

METHODS

2 × 2 diagnostic table was extracted from each of the included studies. Data on specificity (SPE), sensitivity (SEN), negative likelihood ratio (LR-), positive likelihood ratio (LR+), and diagnostic odds ratio (DOR) were calculated purposefully.

RESULTS

Fifteen COVID-19 related publications met our inclusion criteria and were judged qualified for the meta-analysis. The following were summary estimates for diagnostic parameters of chest CT for COVID-19: SPE, 0.49 (95% CI 46-52%); SEN, 0.94 (95% CI 93-95%); LR-, 0.15 (95% CI 11-20%); LR+, 1.93 (95% CI 145-256%); DOR, 17.14 (95% CI 918-3199%); and the area under the receiver operating characteristic curve (AUC), 0.93.

CONCLUSION

Chest CT has high SEN, but the SPE is not ideal. It is highly recommended to use a combination of different diagnostic tools to achieve sufficient SEN and SPE. It should be taken into account as a diagnostic tool for current COVID-19 detection, especially for patients with symptoms.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40336-021-00434-z.