Cardiothoracic Imaging: Recent Techniques and Applications in Diagnostics, Third Edition

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1064

Special Issue Editors


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Guest Editor
Department of Radiology, Ospedale del Mare-ASLNa1 Centro, 80147 Napoli, Italy
Interests: ultrasound; computed tomography; emergency radiology; chest imaging; gastrointestinal imaging; urinary imaging; emergency ultrasound; trauma imaging; bowel imaging
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Special Issue Information

Dear Colleagues,

Cardiothoracic imaging has always attracted enormous interest not only among radiologists but also among clinicians, due to the technological innovations applied to cardiothoracic imaging. In fact, recent technological progress has allowed a significant increase in the diagnostic accuracy of cardiothoracic imaging methods, unthinkable until a few years ago. New CT and MR scanners have reached such high levels of technology and performance as to revolutionize diagnostic imaging of the heart, lungs, and large and small vessels in elective and emergency setting. New CT scanners with clear dose savings and time-saving technology are becoming safer and allow more accurate diagnosis in emergencies by directing clinicians towards the most appropriate therapeutic management, while the latest generation of MR scanners and new postprocessing software make cardiac MRI an increasingly one-stop-shop method in many heart diseases.

Finally, it is necessary to mention how artificial intelligence, radiomics, and deep learning have also become a hot topic in chest imaging, e.g., in lung cancer, pneumonia, or diffuse pulmonary disease.

Therefore, the aim of this Special Issue is to show how new technology applied to radiology can improve imaging diagnostic accuracy in more frequent, often life-threatening, but also less common cardiothoracic diseases.

Dr. Giacomo Sica
Dr. Stefania Tamburrini
Guest Editors

Manuscript Submission Information

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Keywords

  • emergency
  • cardiac imaging
  • thoracic imaging
  • aorta
  • MRI
  • CT
  • radiomics
  • artificial intelligence
  • ILD

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Published Papers (1 paper)

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Review

16 pages, 735 KiB  
Review
Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis
by Giacomo Sica, Vito D’Agnano, Simon Townend Bate, Federica Romano, Vittorio Viglione, Linda Franzese, Mariano Scaglione, Stefania Tamburrini, Alfonso Reginelli and Fabio Perrotta
Diagnostics 2025, 15(3), 278; https://doi.org/10.3390/diagnostics15030278 - 24 Jan 2025
Viewed by 779
Abstract
Interstitial lung diseases (ILDs) are a heterogeneous group of pulmonary disorders characterised by variable degrees of inflammation, interstitial thickening, and fibrosis leading to distortion of the pulmonary architecture and gas exchange impairment. There are approximately 200 different entities in this category. ILDs are [...] Read more.
Interstitial lung diseases (ILDs) are a heterogeneous group of pulmonary disorders characterised by variable degrees of inflammation, interstitial thickening, and fibrosis leading to distortion of the pulmonary architecture and gas exchange impairment. There are approximately 200 different entities in this category. ILDs are commonly classified based on several criteria, including causes, clinical features, and radiological patterns. Chest HRCT is the gold standard for the recognition of lung alteration patterns underlying interstitial lung diseases (ILDs), diagnosing specific patterns, and evaluating radiologic progression. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. The evolving field of radiomics offers a unique and non-invasive approach to extracting quantitative information from medical images, particularly high-resolution computed tomography (HRCT) scans. This comprehensive review explores the burgeoning role of radiomics in unravelling the intricacies of interstitial lung disease. It focuses on its potential applications in diagnosis, prognostication, and treatment response evaluation. Full article
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