Abstract:Objectives: The need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia. Methods: A team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the r… Show more
“…Consequently, overall quality of radiologic reports, especially in terms of clarity and completeness of contents, is crucial to guarantee optimal patient care. Several studies have proposed the adoption of structured reporting of HRCT and radiographs in patients with suspected or known COVID-19 pneumonia [ 15 , [22] , [23] , [24] ]. Indeed, due to its standardized and schematic nature, embracing structured reporting could prove particularly advantageous in the setting of novel disease with high volume clinical demand.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, there is the risk, with conventional reporting, to omit important key features as well as not to address clinical question [ 13 , 14 ]. Thus, the need of a uniform and standardized reporting scheme and language to describe CT findings of COVID-19 pneumonia has been widely accepted [ 15 , 16 ]. In particular, structured reports (SRs) use a template with standardized headings targeted to provide a complete evaluation of all key features that are relevant to the disease.…”
Purpose
To assess clinician satisfaction with structured (SR) and conventional (CR) radiological reports for chest CT exams in coronavirus disease 2019 (COVID-19) patients, objectively comparing both reporting strategies.
Method
We retrospectively included 68 CTs (61 patients) with COVID-19. CRs were collected from the digital database while corresponding SRs were written by an expert radiologist, including a sign checklist, severity score index and final impressions. New CRs were prepared for a random subset (n = 10) of cases, to allow comparisons in reporting time and word count. CRs were analyzed to record severity score and final impressions inclusion. A random subset of 40 paired CRs and SRs was evaluated by two clinicians to assess, using a Likert scale, readability, comprehensiveness, comprehensibility, conciseness, clinical impact, and overall quality.
Results
Overall, 19/68 (28%) and 9/68 (13%) of CRs included final impressions and severity score, respectively. SR writing required significantly (
p
< 0.001) less time (mean = 308 s; SD ± 60 s) compared to CRs (mean = 458 s; SD ± 72 s). On the other hand, word count was not significantly different (
p
= 0.059, median = 100 and 106, range = 106-139 and 88-131 for SRs and CRs, respectively). Both clinicians expressed significantly (all
p
< 0.01) higher scores for SRs compared to CRs in all categories.
Conclusions
Our study supports the use of chest CT SRs in COVID-19 patients to improve referring physician satisfaction, optimizing reporting time and provide a greater amount and quality of information within the report.
“…Consequently, overall quality of radiologic reports, especially in terms of clarity and completeness of contents, is crucial to guarantee optimal patient care. Several studies have proposed the adoption of structured reporting of HRCT and radiographs in patients with suspected or known COVID-19 pneumonia [ 15 , [22] , [23] , [24] ]. Indeed, due to its standardized and schematic nature, embracing structured reporting could prove particularly advantageous in the setting of novel disease with high volume clinical demand.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, there is the risk, with conventional reporting, to omit important key features as well as not to address clinical question [ 13 , 14 ]. Thus, the need of a uniform and standardized reporting scheme and language to describe CT findings of COVID-19 pneumonia has been widely accepted [ 15 , 16 ]. In particular, structured reports (SRs) use a template with standardized headings targeted to provide a complete evaluation of all key features that are relevant to the disease.…”
Purpose
To assess clinician satisfaction with structured (SR) and conventional (CR) radiological reports for chest CT exams in coronavirus disease 2019 (COVID-19) patients, objectively comparing both reporting strategies.
Method
We retrospectively included 68 CTs (61 patients) with COVID-19. CRs were collected from the digital database while corresponding SRs were written by an expert radiologist, including a sign checklist, severity score index and final impressions. New CRs were prepared for a random subset (n = 10) of cases, to allow comparisons in reporting time and word count. CRs were analyzed to record severity score and final impressions inclusion. A random subset of 40 paired CRs and SRs was evaluated by two clinicians to assess, using a Likert scale, readability, comprehensiveness, comprehensibility, conciseness, clinical impact, and overall quality.
Results
Overall, 19/68 (28%) and 9/68 (13%) of CRs included final impressions and severity score, respectively. SR writing required significantly (
p
< 0.001) less time (mean = 308 s; SD ± 60 s) compared to CRs (mean = 458 s; SD ± 72 s). On the other hand, word count was not significantly different (
p
= 0.059, median = 100 and 106, range = 106-139 and 88-131 for SRs and CRs, respectively). Both clinicians expressed significantly (all
p
< 0.01) higher scores for SRs compared to CRs in all categories.
Conclusions
Our study supports the use of chest CT SRs in COVID-19 patients to improve referring physician satisfaction, optimizing reporting time and provide a greater amount and quality of information within the report.
“…Our study reveals that transfer learning from the non-biological tasks can substantially enhance current meth-ods of classification for radiology images while also separately yielding feasible performances for a small dataset as shown in Table 4 . Some notable investiga-tions [ 84 , 78 , 79 ] by researchers produce confirmation that transfer learning can generate outstanding outcomes, especially in the case of small datasets[ 95 , 78 ]. Fine-tuning is utilized on multiple pre-trained deep models using Chest X-ray images, to help deep models converge swiftly and acquire features related to a specific domain.…”
Section: Discussionmentioning
confidence: 99%
“…X-ray images of bacterial, viral and normal categories were collected from the Kaggle dataset [ 76 ]. As far as the images of COVID-19 are concerned, 900 images were gathered from Mendeley dataset [ 77 ] and the remaining hundred images were collected from two open-source repositories namely (i) Italian Society of Medical and Interventional Radiology (SIRM) [ 78 ] and (ii) Radiopaedia [ 79 ]. …”
Novel Coronavirus is deadly for humans and animals. The ease of its disper-sion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radi-ologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community.
BackgroundCOVID-19 infection is particularly aggressive in frail patients, as cancer patients. Therefore, the more suitable management of the oncological patient requires a multidisciplinary assessment, to identify which patients should be treated, as inpatients or outpatients, and which treatments can be procrastinated.ConclusionsThe role of radiologist is crucial, and, all cancer patients who need an imaging evaluation will need to be studied, using the most appropriate imaging tools related to the clinical question and paying a special attention to preserve public health. Guidelines are necessary in the correct organization of a radiology unit to manage patients with suspected or confirmed COVID-19 infection, and whenever possible, a satellite radiography center with dedicated equipment should be used to decrease the transmission risk.
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