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Advances in Diagnostic Radiology
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Advances in Diagnostic Radiology

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2453

Special Issue Editors

Dr. Elisabeta Antonescu

E-Mail Website
Guest Editor
Faculty of Medicine, Lucian Blaga University of Sibiu, 2A Lucian Blaga Str., 550169 Sibiu, Romania
Interests: medical activity; teaching activities; activities regarding labor protection
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Totan

E-Mail Website
Guest Editor
Faculty of Medicine, Lucian Blaga University of Sibiu, 2A Lucian Blaga Str., 550169 Sibiu, Romania
Interests: medical activity; medical analyses; medical imaging; promotion and relations of the medical staff
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern imaging has revolutionised medicine through its ability to provide detailed and accurate images of the internal structures of the human body. Technological advances in this field have led to the development of advanced methods that not only improve diagnosis but also significantly improve the treatment and monitoring of patients.

These technologies have a profound impact on public health by helping to prevent and monitor disease.

In an era of personalized medicine, advanced imaging provides indispensable tools to ensure optimal patient care and promote population health.

Advanced medical imaging methods, such as magnetic resonance imaging, computed tomography, and positron emission tomography, are capable of detecting, with unprecedented clarity and accuracy, early-stage conditions allowing for the rapid initiation of treatment and improving patient prognosis.

In addition, interventional radiology, which combines imaging methods with the use of minimally invasive medical procedures, can be used to treat conditions without the need for major surgery, reducing costs and recovery time for patients.

Advanced radiology also plays a vital role in assessing the effectiveness of cancer therapy, allowing physicians to adjust treatment strategies in real time, which is crucial in improving survival rates and quality of life for patients.

This Special Issue, “Advances in Diagnostic Radiology”, welcomes recent research in medical imaging. We invite a wide range of thematic papers discussing the current status and future prospects of the development of diagnostic imaging facilities to provide specialists with modern diagnostic tools.

Potential topics include, but are not limited to, the following:

  • The use of artificial intelligence algorithms and machine learning for medical image analysis;
  • Techniques that enable the visualisation and measurement of biological processes at the molecular level in the human body;
  • Safer and more effective contrast agents for the improved visualisation of biological structures and processes;
  • Hybrid imaging by integrating multiple imaging techniques to obtain complementary information;
  • Reducing exposure to ionising radiation without compromising image quality;
  • Challenges in paediatric imaging;
  • Four-dimensional imaging technologies that capture three-dimensional data in real time;
  • The development of portable and fast imaging devices for use in emergency rooms and intensive care units;
  • Affordable imaging devices for use in clinics and resource-constrained environments;
  • Identifying the causes of and resolutions for errors that occur in image interpretation;
  • Managing and analysing large volumes of data generated by advanced imaging technologies.

Original papers highlighting the latest research and technical developments are encouraged, but review papers and comparative studies are also welcome.

Dr. Elisabeta Antonescu
Dr. Maria Totan
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

  • radiology
  • diagnosis
  • MRI
  • CT
  • PET
  • US
  • DXA
  • body composition
  • dosimetry
  • contrast agents
  • image interpretation
  • medical imaging
  • neuroradiology

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Published Papers (3 papers)

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Research

12 pages, 1844 KiB  
Article
Lymph Node Involvement Prediction Using Machine Learning: Analysis of Prostatic Nodule, Prostatic Gland, and Periprostatic Adipose Tissue (PPAT)
by Eliodoro Faiella, Giulia D’amone, Raffaele Ragone, Matteo Pileri, Elva Vergantino, Bruno Beomonte Zobel, Rosario Francesco Grasso and Domiziana Santucci
Appl. Sci. 2025, 15(10), 5426; https://doi.org/10.3390/app15105426 - 13 May 2025
Viewed by 148
Abstract
Background: Prostate cancer is a major cause of cancer-related mortality among men, with approximately 15% of newly diagnosed patients having pelvic lymph node metastasis (PLNM). For this reason, PLNM identification before localized PCa treatment would significantly impact treatment planning, clinical judgment, and patient [...] Read more.
Background: Prostate cancer is a major cause of cancer-related mortality among men, with approximately 15% of newly diagnosed patients having pelvic lymph node metastasis (PLNM). For this reason, PLNM identification before localized PCa treatment would significantly impact treatment planning, clinical judgment, and patient outcome prediction. Radiomics has gained popularity for its ability to predict tumor behavior and prognosis without invasive procedures. Magnetic resonance imaging (MRI) is widely used in radiomic workups, particularly for prostate cancer. This study aims to predict lymph node invasion in prostate cancer patients using clinical information and mp-MRI radiomics features extracted from the suspicious nodule, prostate gland, and periprostatic adipose tissue (PPAT). Methods: A retrospective review of 85 patients who underwent mp-MRI at our radiology department between 2016 and 2022 was conducted. This study included patients who underwent prostatectomy and lymphadenectomy with complete histological examination and previous staging mp-MRI and were divided into two groups based on lymph node status (positive/negative). Data were collected from each patient, including clinical information, radiomics, and semantic data (such as tumor MRI characteristics, histological tumor details, and lymph node status (LNS)). MRI exams were conducted using a 1.5-T system and were used to study the prostate gland. A three-year resident manually segmented the prostate nodule, prostatic gland, and periprostatic tissue using an open-source segmentation program. A random forest (RF) machine learning model was developed and tested using Chat-GPT version 4.0 software. The model’s performance in predicting LNS was assessed using accuracy, precision, recall, F1 score, and area under the curve (AUC) receiver operating characteristic (ROC), with sensitivity and specificity evaluated using DeLong’s test. Results: Random forest demonstrated the best performance in prediction considering features extracted from DWI nodules (67% of accuracy, 0.83 AUC), from T2 fat (78% of accuracy, 0.86 AUC), and from T2 glands (78% of accuracy, 0.97 AUC). The combination of the three sequences in the nodule evaluation was more accurate compared with the single sequences (88%). Combining all the nodule features with gland and PPAT features, an accuracy of 89% with AUC near 1 was obtained. Compared with the analysis of the nodule and the PPAT, the whole-gland evaluation had the best performance (p ≤ 0.05) in predicting LNS when compared with the nodule. Conclusions: Precise nodal staging is essential for PCa patients’ prognosis and therapeutic strategy. When compared with a radiologist’s assessment, radiomics models enhance the diagnostic accuracy of lymph node staging for prostate cancer. Although data are still lacking, deep learning models may be able to further improve on this. Full article
(This article belongs to the Special Issue Advances in Diagnostic Radiology)
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14 pages, 1657 KiB  
Article
An Efficient Method for Lung Lesions Classification Using Automatic Vascularization Evaluation on Color Doppler Ultrasound
by Roxana Rusu-Both, Adrian Satmari, Romeo-Ioan Chira, Alexandra Chira and Camelia Avram
Appl. Sci. 2025, 15(5), 2851; https://doi.org/10.3390/app15052851 - 6 Mar 2025
Viewed by 540
Abstract
Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network [...] Read more.
Lung cancer still represents one of the main causes of cancer-related mortality, highlighting the necessity for precise, effective, and minimally intrusive diagnostic methods. This research presents an innovative approach to classifying lung lesions using Doppler ultrasound imagery combined with a feed-forward neural network (FNN). This study integrates Doppler mode ultrasound vascularization features—blood vessel area, tortuosity index, and orientation—into an FNN to classify lung lesions as benign or malignant. A dataset of 565 Doppler ultrasound pictures was extended using augmentation techniques to enhance robustness, yielding a training dataset of 3390 images. The FNN architecture was trained utilizing the Levenberg–Marquardt algorithm, achieving a classification accuracy of 98%, demonstrating its potential as a diagnostic aid. The results indicate that integrating all three vascularization factors significantly improves diagnosis accuracy compared with individual modules. This method offers a non-invasive and cost-effective complementary tool to conventional techniques such as CT scans, with the potential to improve early detection and treatment planning for lung cancer patients. Full article
(This article belongs to the Special Issue Advances in Diagnostic Radiology)
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11 pages, 1142 KiB  
Article
An Age-Based Size-Specific Dose Estimate for Pediatric Computed Tomography Head Examinations Performed at Songklanagarind Hospital, Thailand, from 2017 to 2019
by Saowapark Poosiri, Kanokkwan Chuboonlap and Nuttita Kaewlaied
Appl. Sci. 2024, 14(17), 7848; https://doi.org/10.3390/app14177848 - 4 Sep 2024
Cited by 1 | Viewed by 1117
Abstract
Computed tomography (CT) is the primary source of diagnostic radiation in pediatric patients. Patient head size and tissue attenuation are critical factors for estimating CT radiation doses. This study aimed to determine a size-specific dose estimate based on the water-equivalent diameter (SSDEDw [...] Read more.
Computed tomography (CT) is the primary source of diagnostic radiation in pediatric patients. Patient head size and tissue attenuation are critical factors for estimating CT radiation doses. This study aimed to determine a size-specific dose estimate based on the water-equivalent diameter (SSDEDw) for pediatric CT head examinations, categorized by age group, and to investigate the parameters influencing the SSDEDw. This retrospective analysis included 274 pediatric patients aged 0 to 15 years who underwent non-contrast CT head examinations using an age-based protocol without automatic exposure control systems. The SSDEDw was calculated using the CTDIvol, and the conversion factor was derived from AAPM Report No. 293, based on the water-equivalent diameter (Dw). We found that the SSDEDw of age groups of 0 to 6 months, 6 months to 3 years, 3 to 6 years, 6 to 12 years, and 12 to 15 years were 15.4 (14.8, 15.8), 20.1 (19.6, 20.6), 25.3 (24.6, 25.7), 28.1 (27.3, 28.8), and 35.1 (34.6, 36) mGy, respectively. Age and body weight significantly affected the SSDEDw, with high R-squared values of 0.87 and 0.63, respectively (p < 0.001). The SSDE, particularly when based on the water-equivalent diameter (SSDEDW), is a valuable supplement to the DLP and the CTDIvol as it closely relates to patient dose, especially for pediatric head scans of different patient sizes. Full article
(This article belongs to the Special Issue Advances in Diagnostic Radiology)
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