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Diagnostics
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Advances in Chest Imaging Diagnostics
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Advances in Chest Imaging Diagnostics

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8016

Special Issue Editor

Dr. Stefano Palmucci

E-Mail Website
Guest Editor
Department of Medical Surgical Sciences and Advanced Technologies “GF Ingrassia”, Radiology I Unit, University Hospital “Policlinico-Vittorio Emanuele”, University of Catania, 95123 Catania Italy
Interests: chest imaging; HRCT; interstitial lung diseases; quantitative HRCT; functional MRI of the abdomen; liver and pancreatic diseases on imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Imaging has always played a very important role in the diagnosis and monitoring of lung diseases, and the coronavirus disease outbreak (COVID-19) in 2019 has demonstrated the importance of chest radiographs and HRCT in disease evaluation. However, the sensitivity and specificity of detection and diagnosis greatly depend on the imaging modality used.

HCRT represents an important imaging modality for the evaluation of chest diseases: therefore, main technical skills and tips are proposed in this Special Issue, which emphasizes the capability of HRCT in disease detection and characterization. Main indications for pulmonary disease assessment are proposed, with special reference to interstitial lung diseases, chronic obstructive pulmonary diseases, lung cancer screening, lung nodule management, and lung cancer staging. Practical recommendations regarding the imaging of chest emergencies—such as pulmonary embolism—are also discussed.

For each disease category, this Special Issue will emphasize a multidisciplinary approach based on the involvement of diagnostic radiologists, pneumologists, thoracic surgeons, pathologists, rheumatologists, and interventional radiologists to achieve disease detection, characterization, and management.

Articles based on quantitative imaging and AI applications are encouraged to improve the diagnostic value of an integrated approach (qualitative and quantitative evaluations).

Therefore, in this Special Issue, we focus on analytical studies and reviews of various imaging modalities for the diagnosis of chest diseases in real-time clinical settings to assess the role of chest imaging in daily clinical practice.

Dr. Stefano Palmucci
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.

Published Papers (5 papers)

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Research

14 pages, 2401 KiB  
Article
Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging
by Xu Jiang, Meng-Wen Liu, Xue Zhang, Ji-Yan Dong, Lei Miao, Zi-Han Sun, Shu-Shan Dong, Li Zhang, Lin Yang and Meng Li
Diagnostics 2023, 13(15), 2560; https://doi.org/10.3390/diagnostics13152560 - 01 Aug 2023
Viewed by 1032
Abstract
Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method [...] Read more.
Background: This study aimed to investigate the natural growth history of peripheral small-cell lung cancer (SCLC) using CT imaging. Methods: A retrospective study was conducted on 27 patients with peripheral SCLC who underwent at least two CT scans. Two methods were used: Method 1 involved direct measurement of nodule dimensions using a calliper, while Method 2 involved tumour lesion segmentation and voxel volume calculation using the “py-radiomics” package in Python. Agreement between the two methods was assessed using the intraclass correlation coefficient (ICC). Volume doubling time (VDT) and growth rate (GR) were used as evaluation indices for SCLC growth, and growth distribution based on GR and volume measurements were depicted. We collected potential factors related to imaging VDT and performed a differential analysis. Patients were classified into slow-growing and fast-growing groups based on a VDT cut-off point of 60 days, and univariate analysis was used to identify factors influencing VDT. Results: Median VDT calculated by the two methods were 61 days and 71 days, respectively, with strong agreement. All patients had continuously growing tumours, and none had tumours that decreased in size or remained unchanged. Eight patients showed possible growth patterns, with six possibly exhibiting exponential growth and two possibly showing Gompertzian growth. Tumours deeper in the lung grew faster than those adjacent to the pleura. Conclusions: Peripheral SCLC tumours grow rapidly and continuously without periods of nongrowth or regression. Tumours located deeper in the lung tend to grow faster, but further research is needed to confirm this finding. Full article
(This article belongs to the Special Issue Advances in Chest Imaging Diagnostics)
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12 pages, 1843 KiB  
Article
Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study
by Feipeng Song, Binjie Fu, Mengxi Liu, Xiangling Liu, Sizhu Liu and Fajin Lv
Diagnostics 2023, 13(13), 2210; https://doi.org/10.3390/diagnostics13132210 - 29 Jun 2023
Viewed by 1847
Abstract
Background: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the [...] Read more.
Background: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. Methods: The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN–vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. Results: Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p < 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). Conclusions: In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022. Full article
(This article belongs to the Special Issue Advances in Chest Imaging Diagnostics)
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13 pages, 4290 KiB  
Article
An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis
by Di Xu, Qifan Xu, Kevin Nhieu, Dan Ruan and Ke Sheng
Diagnostics 2023, 13(9), 1652; https://doi.org/10.3390/diagnostics13091652 - 08 May 2023
Cited by 2 | Viewed by 1779
Abstract
Background: Suppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological [...] Read more.
Background: Suppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological lung details but are impractical due to the extremely long processing time. Machine learning (ML) methods are computationally efficient but are limited by the available ground truth (GT) for effective and robust training, resulting in suboptimal results. Purpose: To improve bone shadow suppression, we propose a generalizable yet efficient workflow for CXR rib suppression by combining physical and ML methods. Materials and Method: Our pipeline consists of two stages: (1) pair generation with GT bone shadows eliminated by a physical model in spatially transformed gradient fields; and (2) a fully supervised image denoising network trained on stage-one datasets for fast rib removal from incoming CXRs. For stage two, we designed a densely connected network called SADXNet, combined with a peak signal-to-noise ratio and a multi-scale structure similarity index measure as the loss function to suppress the bony structures. SADXNet organizes the spatial filters in a U shape and preserves the feature map dimension throughout the network flow. Results: Visually, SADXNet can suppress the rib edges near the lung wall/vertebra without compromising the vessel/abnormality conspicuity. Quantitively, it achieves an RMSE of ~0 compared with the physical model generated GTs, during testing with one prediction in <1 s. Downstream tasks, including lung nodule detection as well as common lung disease classification and localization, are used to provide task-specific evaluations of our rib suppression mechanism. We observed a 3.23% and 6.62% AUC increase, as well as 203 (1273 to 1070) and 385 (3029 to 2644) absolute false positive decreases for lung nodule detection and common lung disease localization, respectively. Conclusion: Through learning from image pairs generated from the physical model, the proposed SADXNet can make a robust sub-second prediction without losing fidelity. Quantitative outcomes from downstream validation further underpin the superiority of SADXNet and the training ML-based rib suppression approaches from the physical model yielded dataset. The training images and SADXNet are provided in the manuscript. Full article
(This article belongs to the Special Issue Advances in Chest Imaging Diagnostics)
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15 pages, 2091 KiB  
Article
Contrast-Enhanced Ultrasound in Distinguishing between Malignant and Benign Peripheral Pulmonary Consolidations: The Debated Utility of the Contrast Enhancement Arrival Time
by Carla Maria Irene Quarato, Beatrice Feragalli, Donato Lacedonia, Gaetano Rea, Giulia Scioscia, Evaristo Maiello, Concetta Di Micco, Cristina Borelli, Antonio Mirijello, Paolo Graziano, Lucia Dimitri, Rosanna Villani and Marco Sperandeo
Diagnostics 2023, 13(4), 666; https://doi.org/10.3390/diagnostics13040666 - 10 Feb 2023
Cited by 2 | Viewed by 1365
Abstract
Background. Limited studies and observations conducted on a too small number of patients prevent determining the actual clinical utility of pulmonary contrast-enhanced ultrasound (CEUS). The aim of the present study was to examine the efficacy of contrast enhancement (CE) arrival time (AT) and [...] Read more.
Background. Limited studies and observations conducted on a too small number of patients prevent determining the actual clinical utility of pulmonary contrast-enhanced ultrasound (CEUS). The aim of the present study was to examine the efficacy of contrast enhancement (CE) arrival time (AT) and other dynamic CEUS findings for differentiating between malignant and benign peripheral lung lesions. Methods. 317 inpatients and outpatients (215 men, 102 women; mean age: 52 years) with peripheral pulmonary lesions were included in the study and underwent pulmonary CEUS. Patients were examined in a sitting position after receiving an intravenous injection of 4.8 mL of sulfur hexafluoride microbubbles stabilized by a phospholipid shell as ultrasound contrast agent (SonoVue—Bracco; Milan, Italy). Each lesion was observed for at least 5 min in real-time and the following temporal characteristics of enhancement were detected: the arrival time (AT) of microbubbles in the target lesion; the enhancement pattern; the wash-out time (WOT) of microbubbles. Results were then compared in light of the definitive diagnosis of community acquired pneumonia (CAP) or malignancies, which was not known at the time of CEUS examination. All malignant cases were diagnosed by histological results, while pneumonia was diagnosed on the basis of clinical and radiological follow-up, laboratory findings and, in some cases, histology. Results. CE AT has not been shown to differ between benign and malignant peripheral pulmonary lesions. The overall diagnostic accuracy and sensibility of a CE AT cut-off value < 10 s in discriminating benign lesions were low (diagnostic accuracy: 47.6%; sensibility: 5.3%). Poor results were also obtained in the sub-analysis of small (mean diameter < 3 cm) and large (mean diameter > 3 cm) lesions. No differences were recorded in the type of CE pattern showed between benign and malignant peripheral pulmonary lesions. In benign lesions we observed a higher frequency of delayed CE wash-out time (WOT) > 300 s. Anyhow, a CE WOT cut-off value > 300 s showed low diagnostic accuracy (53.6%) and sensibility (16.5%) in discriminating between pneumonias and malignancies. Similar results were also obtained in the sub-analysis by lesion size. Squamous cell carcinomas showed a more delayed CE AT compared to other histopathology subtypes. However, such a difference was statistically significant with undifferentiated lung carcinomas. Conclusions. Due to an overlap of CEUS timings and patterns, dynamic CEUS parameters cannot effectively differentiate between benign and malignant peripheral pulmonary lesions. Chest CT remains the gold standard for lesion characterization and the eventual identification of other pneumonic non-subpleural localizations. Furthermore, in the case of malignancy, a chest CT is always needed for staging purposes. Full article
(This article belongs to the Special Issue Advances in Chest Imaging Diagnostics)
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17 pages, 3306 KiB  
Article
Feasibility of Dynamic Inhaled Gas MRI-Based Measurements Using Acceleration Combined with the Stretched Exponential Model
by Ramanpreet Sembhi, Tuneesh Ranota, Matthew Fox, Marcus Couch, Tao Li, Iain Ball and Alexei Ouriadov
Diagnostics 2023, 13(3), 506; https://doi.org/10.3390/diagnostics13030506 - 30 Jan 2023
Viewed by 1156
Abstract
Dynamic inhaled gas (3He/129Xe/19F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger’s approach used for analyzing the wash-out data can be substituted with [...] Read more.
Dynamic inhaled gas (3He/129Xe/19F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger’s approach used for analyzing the wash-out data can be substituted with the stretched-exponential-model (SEM) because signal-intensity is attenuated as a function of wash-out-breath in 19F lung imaging. Thirteen normal-rats were studied using 3He/129Xe and 19F MRI and the ventilation measurements were performed using two 3T clinical-scanners. Two Cartesian-sampling-schemes (Fast-Gradient-Recalled-Echo/X-Centric) were used to test the proposed method. The fully sampled dynamic wash-out images were retrospectively under-sampled (acceleration-factors (AF) of 10/14) using a varying-sampling-pattern in the wash-out direction. Mean fractional-ventilation maps using Deninger’s and SEM-based approaches were generated. The mean fractional-ventilation-values generated for the fully sampled k-space case using the Deninger method were not significantly different from other fractional-ventilation-values generated for the non-accelerated/accelerated data using both Deninger and SEM methods (p > 0.05 for all cases/gases). We demonstrated the feasibility of the SEM-based approach using retrospective under-sampling, mimicking AF = 10/14 in a small-animal-cohort from the previously reported dynamic-lung studies. A pixel-by-pixel comparison of the Deninger-derived and SEM-derived fractional-ventilation-estimates obtained for AF = 10/14 (≤16% difference) has confirmed that even at AF = 14, the accuracy of the estimates is high enough to consider this method for prospective measurements. Full article
(This article belongs to the Special Issue Advances in Chest Imaging Diagnostics)
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