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Special Issue "The Challenge of Advanced Medical Imaging Data Analysis in COVID-19"

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A special issue of Tomography (ISSN 2379-139X). This special issue belongs to the section "Artificial Intelligence in Medical Imaging".

Deadline for manuscript submissions: 31 October 2023 | Viewed by 7415

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Special Issue Editors

Dr. Pietro Andrea Bonaffini

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Guest Editor

Department of Radiology, Papa Giovanni XXIII Hospital, Bergamo; School of Medicine, University Milano Bicocca, Milan, Italy
Interests: abdominal radiology; hepato-biliary and pancreatic disease; gynecologic imaging; oncologic imaging; emergency radiology; radiomics

Dr. Clarissa Valle

E-Mail Website
Guest Editor

Department of Radiology, Papa Giovanni XXIII Hospital, Bergamo; School of Medicine, University Milano Bicocca, Milan, Italy
Interests: abdominal radiology; pediatric radiology; emergency radiology oncologic imaging

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has profoundly impacted healthcare systems around the world, leading to increased efforts in all steps for patient management. Facing this worldwide emergency, imaging experts have extensively put in play standard validated techniques (X-ray, CT) but also firmly invested in and tested new advanced tools. Namely, COVID-19 has challenged the most recent advances and improvements in Artificial Intelligence (AI) and quantitative and computational imaging. Even straightforward imaging techniques, such as lung ultrasound, have been employed from new perspectives. Data analysis and integration have been implemented over the last decade, allowing the assimilation of radiological, clinical, and laboratory information. COVID-19 has shown the usefulness of these tools in diagnosis, patient stratification, and prognostic evaluation. This Special Issue will focus on pertinent research papers, commentaries, and reviews informing readers about the role and challenges of the leading innovative imaging tools and new employment of strengthened techniques in assessing COVID-19 patients with lung disease. We welcome submissions describing computational and quantitative imaging, application of Artificial Intelligence, and machine learning in the diagnosis, follow-up, and prognosis of COVID-19 lung disease. Accepted modalities will include chest X-ray, CT, and lung ultrasound.

Dr. Pietro Andrea Bonaffini
Dr. Clarissa Valle
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

COVID-19
SARS-CoV-2
Artificial Intelligence
segmentation
computational imaging
CT
lung
ultrasound
imaging data analysis

Published Papers (4 papers)

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Research

Open AccessArticle

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model

Silvia Nerini Molteni

Luca Alessandro Carbonaro

and

Angelo Vanzulli

Tomography 2022, 8(6), 2815-2827; https://doi.org/10.3390/tomography8060235 - 25 Nov 2022

Cited by 2 | Viewed by 1443

Abstract

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet’s AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging. Full article

(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)

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Open AccessArticle

Structural and Functional Pulmonary Assessment in Severe COVID-19 Survivors at 12 Months after Discharge

Andrea Corsi

Anna Caroli

Pietro Andrea Bonaffini

and

Tomography 2022, 8(5), 2588-2603; https://doi.org/10.3390/tomography8050216 - 13 Oct 2022

Cited by 6 | Viewed by 1582

Abstract

Long-term pulmonary sequelae in COVID-19 patients are currently under investigation worldwide. Potential relationships between blood sampling and functional and radiological findings are crucial to guide the follow-up. In this study, we collected and evaluated clinical status, namely symptoms and patients’ reported outcome, pulmonary function tests (PFT), laboratory tests, and radiological findings at 3- and 12-months post-discharge in patients admitted between 25 February and 2 May 2020, and who survived severe COVID-19 pneumonia. A history of chronic pulmonary disease or COVID-19-unrelated complications were used as exclusion criteria. Unenhanced CTs were analyzed quantitatively (compromising lung volume %) and qualitatively, with main patterns of: ground-glass opacity (GGO), consolidation, and reticular configuration. Patients were subsequently divided into groups based on their radiological trends and according to the evolution in the percentage of compromised lung volume. At 12 months post-discharge, seventy-one patients showed significantly improved laboratory tests and PFT. Among them, 63 patients also underwent CT examination: all patients with negative CT findings at three months (n = 14) had negative CT also at 12 months; among the 49/63 patients presenting CT alterations at three months, 1/49 (2%) normalized, 40/49 (82%) improved, 7/49 (14%) remained stably abnormal, and 1/49 (2%) worsened. D-dimer values were low in patients with normal CT and higher in cases with improved or stably abnormal CT (median values 213 vs. 329 vs. 1000 ng/mL, respectively). The overall compromised lung volume was reduced compared with three months post-discharge (12.3 vs. 14.4%, p < 0.001). In stably abnormal CT, the main pulmonary pattern changed, showing a reduction in GGO and an increase in reticular configuration. To summarize, PFT are normal in most COVID-19 survivors 12 months post-discharge, but CT structural abnormalities persist (although sensibly improved over time) and are associated with higher D-dimer values. Full article

(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)

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Open AccessArticle

Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study

Federica Maria Pilar Tordato

and

Tomography 2022, 8(3), 1578-1585; https://doi.org/10.3390/tomography8030130 - 17 Jun 2022

Viewed by 1282

Abstract

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the “first wave” of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51–69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1–4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae. Full article

(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)

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Open AccessArticle

Follow-Up CT Patterns of Residual Lung Abnormalities in Severe COVID-19 Pneumonia Survivors: A Multicenter Retrospective Study

and

Tomography 2022, 8(3), 1184-1195; https://doi.org/10.3390/tomography8030097 - 20 Apr 2022

Cited by 14 | Viewed by 2217

Abstract

Prior studies variably reported residual chest CT abnormalities after COVID-19. This study evaluates the CT patterns of residual abnormalities in severe COVID-19 pneumonia survivors. All consecutive COVID-19 survivors who received a CT scan 5–7 months after severe pneumonia in two Italian hospitals (Reggio Emilia and Parma) were enrolled. Individual CT findings were retrospectively collected and follow-up CT scans were categorized as: resolution, residual non-fibrotic abnormalities, or residual fibrotic abnormalities according to CT patterns classified following standard definitions and international guidelines. In 225/405 (55.6%) patients, follow-up CT scans were normal or barely normal, whereas in 152/405 (37.5%) and 18/405 (4.4%) patients, non-fibrotic and fibrotic abnormalities were respectively found, and 10/405 (2.5%) had post-ventilatory changes (cicatricial emphysema and bronchiectasis in the anterior regions of upper lobes). Among non-fibrotic changes, either barely visible (n = 110/152) or overt (n = 20/152) ground-glass opacities (GGO), resembling non-fibrotic nonspecific interstitial pneumonia (NSIP) with or without organizing pneumonia features, represented the most common findings. The most frequent fibrotic abnormalities were subpleural reticulation (15/18), traction bronchiectasis (16/18) and GGO (14/18), resembling a fibrotic NSIP pattern. When multiple timepoints were available until 12 months (n = 65), residual abnormalities extension decreased over time. NSIP, more frequently without fibrotic features, represents the most common CT appearance of post-severe COVID-19 pneumonia. Full article

(This article belongs to the Special Issue The Challenge of Advanced Medical Imaging Data Analysis in COVID-19)

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Special Issue "The Challenge of Advanced Medical Imaging Data Analysis in COVID-19"

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