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Diagnostics
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Advances in Lung Imaging
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Advances in Lung Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (25 November 2022) | Viewed by 4274

Special Issue Editor

Dr. Anna Pecorelli

E-Mail Website
Guest Editor
Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, Bologna, Italy
Interests: lung cancer diagnosis; lung cancer screening; SARS-CoV-2 infection; low-dose CT; artificial intelligence

Special Issue Information

Dear Colleagues,

Radiological imaging of the lungs plays an important role not only in the diagnosis of pulmonary disease, which in certain cases replaces biopsy, but also in the assessment of treatment response and in the setting of screening/surveillance. The available techniques range from conventional x-ray to computed tomography (CT) to magnetic resonance imaging (MRI) and positron emission tomography (PET). In recent years, many efforts have been made to improve radiological study of the chest by applying the novel technology artificial intelligence (AI). The two main fields of studies have been neoplastic diseases, especially the development of an imaging screening test and improvements in standard imaging techniques, with the intent to offer precision lung cancer therapy, and infectious illnesses, the focus of which was strengthened due to the pandemic caused by the spread of the SARS-CoV-2 infection in the last two years. This Special Issue will focus on new advances in lung imaging, with a particular emphasis on lung cancer and infectious diseases. I invite authors to contribute original research or review articles, providing their knowledge in this field.

Dr. Anna Pecorelli
Guest Editor

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. Diagnostics 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 2600 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

  • lung cancer diagnosis
  • lung cancer screening
  • SARS-CoV-2 infection
  • low-dose CT
  • artificial intelligence

Published Papers (3 papers)

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Research

11 pages, 1462 KiB  
Article
The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images
by Jiu-Ming Jiang, Lei Miao, Xin Liang, Zhuo-Heng Liu, Li Zhang and Meng Li
Diagnostics 2022, 12(10), 2560; https://doi.org/10.3390/diagnostics12102560 - 21 Oct 2022
Cited by 3 | Viewed by 1620
Abstract
This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients [...] Read more.
This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening. Full article
(This article belongs to the Special Issue Advances in Lung Imaging)
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13 pages, 1867 KiB  
Article
Prediction of the Presence of Targetable Molecular Alteration(s) with Clinico-Metabolic 18 F-FDG PET Radiomics in Non-Asian Lung Adenocarcinoma Patients
by Nicolas Aide, Kathleen Weyts and Charline Lasnon
Diagnostics 2022, 12(10), 2448; https://doi.org/10.3390/diagnostics12102448 - 10 Oct 2022
Cited by 2 | Viewed by 949
Abstract
This study aimed to investigate if combining clinical characteristics with pre-therapeutic 18 F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET) radiomics could predict the presence of molecular alteration(s) in key molecular targets in lung adenocarcinoma. This non-interventional monocentric study included patients with newly [...] Read more.
This study aimed to investigate if combining clinical characteristics with pre-therapeutic 18 F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET) radiomics could predict the presence of molecular alteration(s) in key molecular targets in lung adenocarcinoma. This non-interventional monocentric study included patients with newly diagnosed lung adenocarcinoma referred for baseline PET who had tumour molecular analyses. The data were randomly split into training and test datasets. LASSO regression with 100-fold cross-validation was performed, including sex, age, smoking history, AJCC cancer stage and 31 PET variables. In total, 109 patients were analysed, and it was found that 63 (57.8%) patients had at least one molecular alteration. Using the training dataset (n = 87), the model included 10 variables, namely age, sex, smoking history, AJCC stage, excessKustosis_HISTO, sphericity_SHAPE, variance_GLCM, correlation_GLCM, LZE_GLZLM, and GLNU_GLZLM. The ROC analysis for molecular alteration prediction using this model found an AUC equal to 0.866 (p < 0.0001). A cut-off value set to 0.48 led to a sensitivity of 90.6% and a positive likelihood ratio (LR+) value equal to 2.4. After application of this cut-off value in the unseen test dataset of patients (n = 22), the test presented a sensitivity equal to 90.0% and an LR+ value of 1.35. A clinico-metabolic 18 F-FDG PET phenotype allows the detection of key molecular target alterations with high sensitivity and negative predictive value. Hence, it opens the way to the selection of patients for molecular analysis. Full article
(This article belongs to the Special Issue Advances in Lung Imaging)
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15 pages, 457 KiB  
Article
The Additional Value of Lower Respiratory Tract Sampling in the Diagnosis of COVID-19: A Real-Life Observational Study
by Luca Morandi, Francesca Torsani, Giacomo Forini, Mario Tamburrini, Aldo Carnevale, Anna Pecorelli, Melchiore Giganti, Marco Piattella, Ippolito Guzzinati, Alberto Papi and Marco Contoli
Diagnostics 2022, 12(10), 2372; https://doi.org/10.3390/diagnostics12102372 - 29 Sep 2022
Cited by 1 | Viewed by 1228
Abstract
Background: Since December 2019, SARS-CoV-2 has been causing cases of severe pneumonia in China and has spread all over the world, putting great pressure on health systems. Nasopharyngeal swab (NPS) sensitivity is suboptimal. When the SARS-CoV-2 infection is suspected despite negative NPSs, other [...] Read more.
Background: Since December 2019, SARS-CoV-2 has been causing cases of severe pneumonia in China and has spread all over the world, putting great pressure on health systems. Nasopharyngeal swab (NPS) sensitivity is suboptimal. When the SARS-CoV-2 infection is suspected despite negative NPSs, other tests may help to rule out the infection. Objectives: To evaluate the yield of the lower respiratory tract (LRT) isolation of SARS-CoV-2. To evaluate the correlations between SARS-CoV-2 detection and clinical symptoms, and laboratory values and RSNA CT review scores in suspect patients after two negative NPSs. To assess the safety of bronchoscopy in this scenario. Method: A retrospective analysis of data from LRT sampling (blind nasotracheal aspiration or bronchial washing) for suspected COVID-19 after two negative NPS. Chest CT scans were reviewed by two radiologists using the RSNA imaging classification. Results: SARS-CoV-2 was detected in 14/99 patients (14.1%). A correlation was found between SARS-CoV2 detection on the LRT and the presence of a cough as well as with typical CT features. Typical CT resulted in 57.1% sensitivity, 80.8% accuracy and 92.3% NPV. Neither severe complications nor infections in the personnel were reported. Conclusions: In suspect cases after two negative swabs, CT scan revision can help to rule out COVID-19. In selected cases, with consistent CT features above all, LRT sampling can be of help in confirming COVID-19. Full article
(This article belongs to the Special Issue Advances in Lung Imaging)
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