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Applied Sciences
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Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and...
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Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 5007

Special Issue Editors

Dr. Federica Vernuccio

E-Mail Website
Guest Editor
Department of Radiology, University Hospital of Padova, Via Nicolò Giustiniani 2, 35128 Padova, Italy
Interests: liver imaging; pancreatic imaging; hepatocellular carcinoma; radiomics; texture analysis; diffuse liver diseases; emergency radiology
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Cannella

E-Mail Website
Guest Editor
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127 Palermo, Italy
Interests: abdominal imaging; emergency radiology; liver imaging; pancreatic imaging; radiomics
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Albano

E-Mail Website
Guest Editor
IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161 Milan, Italy
Interests: muscoloskeletal imaging; radiomics; advanced imaging; whole body MRI

Special Issue Information

Dear Colleagues,

Recently, there has been great scientific interest in improving imaging technology to improve CT/MRI imaging acquisition, early diagnosis of tumors, and precision and quantitative imaging.

We are pleased to announce a Special Issue entitled “Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and Artificial Intelligence”, to be published by the journal Applied Sciences (MDPI).

The goal of this Special Issue is to disseminate emerging techniques and innovative solutions, including radiomics and artificial intelligence techniques, that comprehensively address unmet needs in diagnostic medical imaging in order to improve diagnostic accuracy and precision.

We invite research contributions from cross-disciplinary scientists and professionals involved in diagnostic medical imaging. Clinically oriented studies, pictorial reviews, systematic reviews, and metanalyses mainly on quantitative imaging and the application of radiomics and artificial intelligence in CT and MRI are welcome. We also invite research contributions focused on developing deep-learning models and 3D model printing.

Research areas may include (but are not limited to) the following:

  • Studies on quantitative CT/MRI imaging;
  • Radiomics studies;
  • Dual-energy CT and photon-counting CT;
  • Advanced MRI sequences;
  • Perfusion CT;
  • Artificial intelligence in diagnostic imaging;
  • 3D model printing;
  • Texture analysis and machine learning in CT and MRI.

We look forward to receiving your contributions.

Dr. Federica Vernuccio
Dr. Roberto Cannella
Dr. Domenico Albano
Guest Editors

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. Applied Sciences 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 2400 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

  • image analysis
  • machine learning
  • diagnostic imaging
  • radiomics
  • artificial intelligence
  • precision medicine
  • MRI
  • CT
  • quantitative imaging
  • 3D model
  • MRI sequences
  • photon-counting CT
  • dual-energy CT
  • systematic reviews
  • metanalysis
  • perfusion CT

Published Papers (3 papers)

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Research

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12 pages, 1714 KiB  
Article
Epicardial Adipose Tissue Changes during Statin Administration in Relation to the Body Mass Index: A Longitudinal Cardiac CT Study
by Patrizia Toia, Ludovico La Grutta, Salvatore Vitabile, Bruna Punzo, Carlo Cavaliere, Carmelo Militello, Leonardo Rundo, Domenica Matranga, Clarissa Filorizzo, Erica Maffei, Massimo Galia, Massimo Midiri, Roberto Lagalla, Luca Saba, Eduardo Bossone and Filippo Cademartiri
Appl. Sci. 2023, 13(19), 10709; https://doi.org/10.3390/app131910709 - 26 Sep 2023
Viewed by 707
Abstract
The epicardial adipose tissue (EAT) is the visceral fat located between the myocardium and the pericardium. We aimed to perform a longitudinal evaluation of the epicardial adipose tissue using an advanced computer-assisted approach in a population of patients undergoing Cardiac CT (CCT) during [...] Read more.
The epicardial adipose tissue (EAT) is the visceral fat located between the myocardium and the pericardium. We aimed to perform a longitudinal evaluation of the epicardial adipose tissue using an advanced computer-assisted approach in a population of patients undergoing Cardiac CT (CCT) during statin administration, in relation to their body mass index (BMI). We retrospectively enrolled 95 patients [mean age 62 ± 10 years; 68 males (72%) and 27 females (28%)] undergoing CCT for suspected coronary artery disease during statin administration. CCT was performed at two subsequent time points. At the second CCT, EAT showed a mean density increase (−75.59 ± 7.0 HU vs. −78.18 ± 5.3 HU, p < 0.001) and a volume decrease (130 ± 54.3 cm3 vs.142.79 ± 56.9 cm3, p < 0.001). Concerning coronary artery EAT thickness, a reduction was found at the origin of the right coronary artery (13.26 ± 5.2 mm vs. 14.94 ± 5.8, p = 0.001) and interventricular artery (8.22 ± 3.7 mm vs. 9.13 ± 3.9 mm, p = 0.001). The quartile (Q) attenuation percentage (%) distribution of EAT changed at the second CCT. The EAT % distribution changed by the BMI in Q1 (p = 0.015), Q3 (p = 0.001) and Q4 (p = 0.043) at the second CCT, but the normal-BMI and overweight/obese patients showed a similar response to statin therapy in terms of quartile distribution changes. In conclusion, statins may determine significant changes in EAT quantitative and qualitative characteristics detected by CCT; the BMI influences the EAT composition, but statins determine a similar response in quartile distribution’s variation, irrespective of the BMI. Full article
(This article belongs to the Special Issue Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and Artificial Intelligence)
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Review

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22 pages, 411 KiB  
Review
Radiomics in Gynaecological Imaging: A State-of-the-Art Review
by Paolo Niccolò Franco, Federica Vernuccio, Cesare Maino, Roberto Cannella, Milagros Otero-García and Davide Ippolito
Appl. Sci. 2023, 13(21), 11839; https://doi.org/10.3390/app132111839 - 29 Oct 2023
Viewed by 1042
Abstract
Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible [...] Read more.
Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies. Full article
(This article belongs to the Special Issue Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and Artificial Intelligence)
27 pages, 1143 KiB  
Review
Artificial Intelligence in Symptomatic Carotid Plaque Detection: A Narrative Review
by Giuseppe Miceli, Giuliana Rizzo, Maria Grazia Basso, Elena Cocciola, Andrea Roberta Pennacchio, Chiara Pintus and Antonino Tuttolomondo
Appl. Sci. 2023, 13(7), 4321; https://doi.org/10.3390/app13074321 - 29 Mar 2023
Cited by 2 | Viewed by 2687
Abstract
Identifying atherosclerotic disease is the mainstay for the correct diagnosis of the large artery atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in acute ischemic stroke. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential [...] Read more.
Identifying atherosclerotic disease is the mainstay for the correct diagnosis of the large artery atherosclerosis ischemic stroke subtype and for choosing the right therapeutic strategy in acute ischemic stroke. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. The difficulties in a “vulnerability” definition and the methodical issues concerning its detectability and quantification are still subjects of debate. Non-invasive imaging studies commonly used to detect arterial plaque are computed tomographic angiography, magnetic resonance imaging, and ultrasound. Characterization of a carotid plaque type using the abovementioned imaging modalities represents the basis for carotid atherosclerosis management. Classification into symptomatic and asymptomatic plaque and estimation of the cardiovascular risk are essential to select patients eligible for pharmacological and/or surgical therapy in order to prevent future cerebral ischemic events. In this setting, artificial intelligence (AI) can offer suggestive solutions for tissue characterization and classification concerning carotid artery plaque imaging by analyzing complex data and using automated algorithms to obtain a final output. The aim of this review is to provide overall knowledge about the role of AI models applied to non-invasive imaging studies for the detection of symptomatic and vulnerable carotid plaques. Full article
(This article belongs to the Special Issue Emerging Techniques in Diagnostic Medical Imaging: Quantitative Imaging, Radiomics, and Artificial Intelligence)
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