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New Insights into Lung Imaging
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New Insights into Lung Imaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Respiratory Medicine".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 2080

Special Issue Editor

Dr. Chiara Romei

E-Mail Website
Guest Editor
2nd Radiology Unit, Department of Radiology, Pisa University Hospital, 56124 Pisa, Italy
Interests: interstitial lung disease; lung cancer; chest-CT; chest X-ray
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been growing interest in lung imaging and the development of new techniques.

In particular, technological innovations have been observed in currently available methods, such as faster sequences with higher resolution in MRI of the lung, to overcome the inherent difficulties they present.

In addition, radiomics and artificial intelligence techniques have been developed and implemented to recognize and characterize lung nodules for the quantification of the progression of lung involvement in interstitial disease using CT.

This Special Issue aims to review major advances in lung imaging, with a focus on possible clinical applications.

Dr. Chiara Romei
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. Journal of Clinical Medicine 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 disease
  • lung cancer
  • chest-CT
  • chest X-ray
  • imaging

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Published Papers (3 papers)

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Research

12 pages, 1300 KiB  
Article
Improving Image Quality of Chest Radiography with Artificial Intelligence-Supported Dual-Energy X-Ray Imaging System: An Observer Preference Study in Healthy Volunteers
by Sung-Hyun Yoon, Jihang Kim, Junghoon Kim, Jong-Hyuk Lee, Ilwoong Choi, Choul-Woo Shin and Chang-Min Park
J. Clin. Med. 2025, 14(6), 2091; https://doi.org/10.3390/jcm14062091 - 19 Mar 2025
Viewed by 518
Abstract
Background/Objectives: To compare the image quality of chest radiography with a dual-energy X-ray imaging system using AI technology (DE-AI) to that of conventional chest radiography with a standard protocol. Methods: In this prospective study, 52 healthy volunteers underwent dual-energy chest radiography. Images were [...] Read more.
Background/Objectives: To compare the image quality of chest radiography with a dual-energy X-ray imaging system using AI technology (DE-AI) to that of conventional chest radiography with a standard protocol. Methods: In this prospective study, 52 healthy volunteers underwent dual-energy chest radiography. Images were obtained using two exposures at 60 kVp and 120 kVp, separated by a 150 ms interval. Four images were generated for each participant: a conventional image, an enhanced standard image, a soft-tissue-selective image, and a bone-selective image. A machine learning model optimized the cancellation parameters for generating soft-tissue and bone-selective images. To enhance image quality, motion artifacts were minimized using Laplacian pyramid diffeomorphic registration, while a wavelet directional cycle-consistent adversarial network (WavCycleGAN) reduced image noise. Four radiologists independently evaluated the visibility of thirteen anatomical regions (eight soft-tissue regions and five bone regions) and the overall image with a five-point scale of preference. Pooled mean values were calculated for each anatomic region through meta-analysis using a random-effects model. Results: Radiologists preferred DE-AI images to conventional chest radiographs in various anatomic regions. The enhanced standard image showed superior quality in 9 of 13 anatomic regions. Preference for the soft-tissue-selective image was statistically significant for three of eight anatomic regions. Preference for the bone-selective image was statistically significant for four of five anatomic regions. Conclusions: Images produced by DE-AI provide better visualization of thoracic structures. Full article
(This article belongs to the Special Issue New Insights into Lung Imaging)
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14 pages, 23439 KiB  
Article
Prognostic Value of Chest CT Volumetric Analysis in Patients with Malignant Pleural Mesothelioma
by Elisa Baratella, Eleonora Ercolani, Antonio Segalotti, Marina Troian, Stefano Lovadina, Fabiola Giudici, Pierluca Minelli, Barbara Ruaro, Francesco Salton and Maria Assunta Cova
J. Clin. Med. 2025, 14(5), 1547; https://doi.org/10.3390/jcm14051547 - 25 Feb 2025
Viewed by 399
Abstract
Background/Objectives: Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer linked to asbestos exposure and with poor overall survival. In recent years, CT volumetric analysis has gained increasing interest as a more accurate method for assessing tumor burden. This study aims to [...] Read more.
Background/Objectives: Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer linked to asbestos exposure and with poor overall survival. In recent years, CT volumetric analysis has gained increasing interest as a more accurate method for assessing tumor burden. This study aims to evaluate the prognostic value of chest CT volumetric analysis in MPM, comparing tumor volume with tumor thickness measurements and survival outcomes. Methods: This is a retrospective, observational analysis of all patients undergoing diagnostic thoracoscopy between 2014 and 2021 at the University Hospital of Cattinara (Trieste, Italy). Inclusion criteria were as follows: age ≥ 18 years, histological diagnosis of MPM, and the availability of at least one contrast-enhanced chest CT scan at the time of diagnosis. For each patient, the tumor thickness was measured on the axial plane at three levels (upper, middle, and lower hemithorax). Tumor and effusion volumes were calculated with the RayStation® software version 11.7.174 (HealthMyne®, Madison, WI, USA). Results: A total of 81 patients were eligible for analysis. Maximum and mean tumor thickness were strongly associated with survival, with higher thicknesses correlating with an increased risk of death (adjusted hazard ratio per doubling (aHR) of 1.97 (95%CI: 1.40–2.77) and of 2.23 (95%CI: 1.56–3.20), p < 0.001)), respectively, while the effect of the tumor volume on survival was nevertheless significant but less impactful (aHR = 1.26 (1.10–1.45, p < 0.001)). The presence and volume of effusion did not correlate with survival (p = 0.48 and p = 0.64, respectively). Conclusions: This study supports the role of quantitative parameters for staging MPM, particularly given the frequent discrepancies between clinical and pathological staging when relying solely on qualitative measures. Full article
(This article belongs to the Special Issue New Insights into Lung Imaging)
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11 pages, 1548 KiB  
Article
Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm
by Salvatore Claudio Fanni, Leonardo Colligiani, Federica Volpi, Lisa Novaria, Michele Tonerini, Chiara Airoldi, Dario Plataroti, Brian J. Bartholmai, Annalisa De Liperi, Emanuele Neri and Chiara Romei
J. Clin. Med. 2024, 13(23), 7308; https://doi.org/10.3390/jcm13237308 - 1 Dec 2024
Viewed by 743
Abstract
Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually [...] Read more.
Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann–Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland–Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. Results: Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (−4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (p = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). Conclusions: The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes. Full article
(This article belongs to the Special Issue New Insights into Lung Imaging)
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