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Applied Sciences
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Novel Technologies in Radiology: Diagnosis, Prediction and Treatment
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Novel Technologies in Radiology: Diagnosis, Prediction and Treatment

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2568

Special Issue Editors

Dr. Youfang Lai

E-Mail Website
Guest Editor
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
Interests: Monte Carlo simulation; DNA damage; PET
Dr. Yuting Peng

E-Mail Website
Guest Editor
Department of Radiation Oncology, Washington University at St Louis, 4511 Forest Park Avenue, St. Louis, MO 63108, USA
Interests: low-dimensional semiconductor; heterostructures; MR imaging reconstruction; ultra-low-field MRI system

Special Issue Information

Dear Colleagues,

With its roots traced back to the discovery of X-rays in 1895, radiology has undergone profound advancements, evolving into a cornerstone of modern medicine. Yet, it is still an active field. On the one side, emerging technologies such as artificial intelligence (AI) have provided insights to improve the quality of diagnosis and prediction by radiology. On the other side, the emphasis on precision therapy and personalized medicine brings challenges to radiology, prompting the field to innovate and develop new protocols or technologies. We therefore set up this Special Issue, which aims to collect cutting-edge advancements in technology and protocol design that may significantly enhance our diagnostic and predictive capabilities and eventually benefit the clinic to facilitate researchers.

We are pleased to invite researchers from various fields within this journal’s scope to contribute to this Special Issue or invite relevant experts and colleagues to do so. Both original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel design of devices, algorithms, and protocols.
  • Radiology and AI.
  • Radiology and personalized medicine.
  • Interventional radiology.
  • Molecular imaging.
  • Radiology and predictive medicine.

We look forward to receiving your contributions.

Dr. Youfang Lai
Dr. Yuting Peng
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

  • deep learning
  • imaging guidance
  • PET
  • molecular imaging
  • imaging-based biomarkers

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Published Papers (3 papers)

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Research

13 pages, 4313 KiB  
Article
Assessment of a Patient Dose Monitoring System for Average Glandular Dose (AGD) Estimate in Mammography
by Giuseppina Rita Borzì, Elisa Bonanno, Nina Cavalli, Alessia D’Anna, Martina Pace, Giuseppe Stella, Lucia Zirone and Carmelo Marino
Appl. Sci. 2025, 15(6), 3338; https://doi.org/10.3390/app15063338 - 19 Mar 2025
Viewed by 245
Abstract
This study assessed the accuracy of average glandular dose (AGD) calculations for two Selenia Dimensions mammography systems using data from the online dose management DoseWatch software version 3.3.5.1. Mammographic images acquired between January 2021 and December 2022 were retrospectively analyzed. The AGD values [...] Read more.
This study assessed the accuracy of average glandular dose (AGD) calculations for two Selenia Dimensions mammography systems using data from the online dose management DoseWatch software version 3.3.5.1. Mammographic images acquired between January 2021 and December 2022 were retrospectively analyzed. The AGD values displayed by the systems were compared with those independently calculated using the Dance and Boone methods. Additionally, real glandular composition of breast was estimated using LIBRA (Laboratory for Individualized Breast Radiodensity Assessment) software version 1.0.4 for a selected subgroup of patients. Results showed that the AGD values displayed by the systems were generally consistent with those calculated using the Dance method, but discrepancies emerged when applying the Boone method, especially when using estimated glandular composition. Most mammograms fell within acceptable and achievable dose limits according to European guidelines, though a small percentage exceeded these thresholds. The findings suggest that the Dance method, using glandular composition estimated through LIBRA, provides a reliable and accurate AGD calculation, offering a simpler alternative to more complex individualized calculations. The study highlights the importance of accurate glandularity estimation for proper dose management in mammography. Full article
(This article belongs to the Special Issue Novel Technologies in Radiology: Diagnosis, Prediction and Treatment)
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21 pages, 7071 KiB  
Article
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
by Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell’Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I. Qureshi, Santosh B. Murthy, Shahram Majidi, Guido J. Falcone, Kevin N. Sheth, Jawed Nawabi and Seyedmehdi Payabvash
Appl. Sci. 2025, 15(1), 111; https://doi.org/10.3390/app15010111 - 27 Dec 2024
Viewed by 705
Abstract
Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale [...] Read more.
Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate. Full article
(This article belongs to the Special Issue Novel Technologies in Radiology: Diagnosis, Prediction and Treatment)
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13 pages, 1130 KiB  
Article
Radiological Reporting Systems in Multiple Sclerosis
by Alessandra Scaravilli, Mario Tranfa, Giuseppe Pontillo, Antonio Carotenuto, Caterina Lapucci, Riccardo Nistri, Elisabetta Signoriello, Marcello Moccia, Carla Tortorella, Ruggero Capra, Giacomo Lus, Matilde Inglese, Claudio Gasperini, Roberta Lanzillo, Carlo Pozzilli, Vincenzo Brescia Morra, Arturo Brunetti, Maria Petracca and Sirio Cocozza
Appl. Sci. 2024, 14(13), 5626; https://doi.org/10.3390/app14135626 - 27 Jun 2024
Cited by 1 | Viewed by 1133
Abstract
(1) Background: Although MRI is a well-established tool in Multiple Sclerosis (MS) diagnosis and management, neuroradiological reports often lack standardization and/or quantitative information, with possible consequences in clinical care. The aim of this study was to evaluate the impact of information provided by [...] Read more.
(1) Background: Although MRI is a well-established tool in Multiple Sclerosis (MS) diagnosis and management, neuroradiological reports often lack standardization and/or quantitative information, with possible consequences in clinical care. The aim of this study was to evaluate the impact of information provided by neuroradiological reports and different reporting systems on the clinical management of MS patients. (2) Methods: An online questionnaire was proposed to neurologists working in Italian tertiary care level MS centers. Questions assessed the impact of different MRI-derived biomarkers on clinical choices, the preferred way of receiving radiological information, and the neurologists’ opinions about different reporting systems and the use of automated software in clinical practice. (3) Results: The online survey was completed by 62 neurologists. New/enlarging (100%) lesions, the global T2w/FLAIR lesion load (96.8%), and contrast-enhancing (95.2%) lesions were considered the most important biomarkers for therapeutic decision, while new/enlarging lesions (98.4%), global T2w/FLAIR lesion load (96.8%), and cerebral atrophy (90.3%) were relevant to prognostic evaluations. Almost all participants (98.4%) considered software for medical imaging quantification helpful in clinical management, mostly in relation to prognostic evaluations. (4) Conclusions: These data highlight the impact of providing accurate and reliable data in neuroradiological reports. The use of software for medical imaging quantification in MS can be helpful to standardize radiological reports and to provide useful clinical information to neurologists. Full article
(This article belongs to the Special Issue Novel Technologies in Radiology: Diagnosis, Prediction and Treatment)
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