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Diagnostics
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Diagnostic Imaging of Pulmonary Diseases
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Diagnostic Imaging of Pulmonary Diseases

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1867

Special Issue Editor

Prof. Dr. Marcel Koenigkam Santos

E-Mail Website
Guest Editor
Bauru Medical School, Ribeirao Preto Medical School, University of Sao Paulo, São Paulo, Brazil
Interests: radiology; pneumology; lung cancer; COPD and other diffuse bronchopulmonary diseases

Special Issue Information

Dear Colleagues,

Pulmonary diseases remain among the leading causes of morbidity and mortality worldwide, and imaging plays a pivotal role in their diagnosis, management, and follow-up. Continuous advances in imaging technology and quantitative approaches are reshaping the way thoracic disorders are detected and characterized, offering new opportunities for precision medicine.

This Special Issue of Diagnostics aims to gather high-quality contributions focusing on diagnostic imaging of pulmonary diseases, aligned with the journal’s scope of clinical and translational diagnostics.

We welcome original research articles, comprehensive reviews, and illustrative case reports addressing a wide range of topics, including conventional and advanced imaging modalities, quantitative imaging, radiomics, artificial intelligence, and novel applications in clinical practice.

Prof. Dr. Marcel Koenigkam Santos
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 250 words) can be sent to the Editorial Office for assessment.

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

  • diagnosis
  • imaging
  • pulmonology
  • thoracic surgery
  • radiology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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24 pages, 3522 KB  
Article
Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans
by Murat Kılıç, Merve Bıyıklı, Salih Taha Alperen Özçelik, Hüseyin Üzen and Hüseyin Fırat
Diagnostics 2025, 15(24), 3191; https://doi.org/10.3390/diagnostics15243191 - 14 Dec 2025
Viewed by 579
Abstract
Background/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model [...] Read more.
Background/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model designed to improve diagnostic accuracy for both thymoma detection and subtype classification (benign vs. malignant). Methods: The proposed approach integrates a pre-trained VGG16 network for efficient feature extraction—capitalizing on its capacity to capture hierarchical spatial features—and an MLP-Mixer-based feature enhancement module, which effectively models both local and global feature dependencies without relying on conventional convolutional mechanisms. Additionally, customized preprocessing and post-processing methods are employed to enhance image quality and suppress redundant data. The model’s performance was evaluated on two classification tasks: distinguishing thymoma from healthy cases and discriminating between benign and malignant thymoma. Comparative analysis was conducted against state-of-the-art DL models including ResNet50, ResNet34, SEResNeXt50, InceptionResNetV2, MobileNetV2, VGG16, InceptionV3, and DenseNet121 using metrics such as F1 score, accuracy, recall, and precision. Results: The model proposed in this study obtained its best performance in thymoma vs. healthy classification, with an accuracy of 97.15% and F1 score of 80.99%. In the benign vs. malignant task, it attained an accuracy of 79.20% and an F1 score of 78.51%, outperforming all baseline methods. Conclusions: The integration of VGG16’s robust spatial feature extraction and the MLP-Mixer’s effective feature mixing demonstrates superior and balanced performance, highlighting the model’s potential for clinical decision support in thymoma diagnosis. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Pulmonary Diseases)
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24 pages, 766 KB  
Systematic Review
Artificial Intelligence-Based Automated Analysis for Pleural Effusion Detection on Thoracic Ultrasound: A Systematic Review
by Guido Marchi, Luciano Gabbrielli, Marco Gherardi, Massimiliano Serradori, Francesco Baglivo, Salvatore Claudio Fanni, Jacopo Cefalo, Carmine Salerni, Giacomo Guglielmi, Francesco Pistelli, Laura Carrozzi and Michele Mondoni
Diagnostics 2026, 16(1), 147; https://doi.org/10.3390/diagnostics16010147 - 2 Jan 2026
Cited by 1 | Viewed by 1009
Abstract
Background: Pleural effusion (PE) is a common condition where accurate detection is essential for management. Thoracic ultrasound (TUS) is the first-line modality owing to safety, portability, and high sensitivity, but accuracy is operator-dependent. Artificial intelligence (AI)-based automated analysis has been explored as [...] Read more.
Background: Pleural effusion (PE) is a common condition where accurate detection is essential for management. Thoracic ultrasound (TUS) is the first-line modality owing to safety, portability, and high sensitivity, but accuracy is operator-dependent. Artificial intelligence (AI)-based automated analysis has been explored as an adjunct, with early evidence suggesting potential to reduce variability and standardise interpretation. This review evaluates the diagnostic accuracy of AI-assisted TUS for PE detection. Methods: This review was registered with PROSPERO (CRD420251128416) and followed PRISMA guidelines. MEDLINE, Scopus, Google Scholar, IEEE Xplore, Cochrane CENTRAL, and ClinicalTrials.gov were searched through 20 August 2025 for studies assessing AI-based TUS analysis for PE. Eligible studies required recognised reference standards (expert interpretation or chest CT). Risk of bias was assessed with QUADAS-2, and certainty with GRADE. Owing to heterogeneity, structured narrative synthesis was performed instead of meta-analysis. Results: Five studies (7565 patients) published between 2021–2025 were included. All used convolutional neural networks with varied architectures (ResNet, EfficientNet, U-net). Sensitivity ranged 70.6–100%, specificity 67–100%, and AUC 0.77–0.99. Performance was reduced for small, trace, or complex effusions and in critically ill patients. External validation showed attenuation compared with internal testing. All studies had high risk of bias in patient selection and index test conduct, reflecting retrospective designs and inadequate dataset separation. Conclusions: AI-assisted TUS shows promising diagnostic performance for PE detection in curated datasets; however, evidence is inconsistent and limited by key methodological weaknesses. Overall certainty is low-to-moderate, constrained by retrospective designs, limited dataset separation, and scarce external validation. Current evidence is insufficient to support routine clinical use. Robust prospective multicentre studies with rigorous independent validation and evaluation of clinically meaningful outcomes are essential before clinical implementation can be considered. Full article
(This article belongs to the Special Issue Diagnostic Imaging of Pulmonary Diseases)
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