Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
3.0
Journals
JPM
Special Issues
State-of-the-Art Research on the Imaging in Personalized Medicine
share announcement

State-of-the-Art Research on the Imaging in Personalized Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 9266

Special Issue Editors

Dr. Nicola Maggialetti

E-Mail Website
Guest Editor
Interdisciplinary Department of Medicine, Section of Radiology and Radiation Oncology, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
Interests: CT; MRI; oncological imaging; cardiac imaging; US; artificial intelligence; hybrid imaging
Dr. Antonio Rosario Pisani

E-Mail Website
Co-Guest Editor
Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
Interests: nuclear neurology imaging; nuclear gastroenterology imaging; PET/CT; artificial intelligence; nuclear medical radiotherapy

Special Issue Information

Dear Colleagues,

Imaging is an increasingly important discipline in diagnostic and therapeutic processes. The introduction of new technologies such as high-field magnetic resonance imaging, new-generation CT scans and hybrid scanners have provided new impetus for research. Multimodality imaging involves all the nuclear medicine techniques thanks to the hybrid SPECT/CT, PET/CT and PET/MR scanning methods. The development of new materials for endovascular techniques, as well as the availability of more and more radiopharmaceuticals, allows for innovative approaches to be implemented against oncological and non-oncological pathologies. The increasingly widespread use of artificial intelligence programs is extremely important and opens up new scenarios for clinical and research applications of diagnostic imaging. Realizing personalized imaging and increasingly advanced treatments is the challenge that our discipline is currently facing.

Dr. Nicola Maggialetti
Dr. Antonio Rosario Pisani
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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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

  • MRI
  • CT
  • PET/CT
  • artificial intelligence
  • oncological imaging
  • cardiac imaging
  • hybrid imaging
  • endovascolar radiology
  • radiomics
  • neuroimaging

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 2104 KiB  
Article
Peritumoral Adipose Tissue Features Derived from [18F]fluoro-2-deoxy-2-d-glucose Positron Emission Tomography/Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients
by Jeong Won Lee, Yong Kyun Won, Hyein Ahn, Jong Eun Lee, Sun Wook Han, Sung Yong Kim, In Young Jo and Sang Mi Lee
J. Pers. Med. 2024, 14(9), 952; https://doi.org/10.3390/jpm14090952 - 9 Sep 2024
Viewed by 559
Abstract
This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively [...] Read more.
This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

9 pages, 2795 KiB  
Article
Automated Breast Ultrasound for Evaluating Response to Neoadjuvant Therapy: A Comparison with Magnetic Resonance Imaging
by Michele Telegrafo, Stefania Luigia Stucci, Angela Gurrado, Claudia Catacchio, Federico Cofone, Michele Maruccia, Amato Antonio Stabile Ianora and Marco Moschetta
J. Pers. Med. 2024, 14(9), 930; https://doi.org/10.3390/jpm14090930 - 31 Aug 2024
Viewed by 944
Abstract
Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total [...] Read more.
Background: Neoadjuvant chemotherapy (NAC) is currently used for treating breast cancer in selected cases. Our study aims to evaluate the role of automated breast ultrasound (ABUS) in the assessment of response to NAC and compare the ABUS results with MRI. Methods: A total of 52 consecutive patients were included in this study. ABUS and MRI sensitivity (SE), specificity (SP), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV) were calculated and represented using Area Under ROC Curve (ROC) analysis, searching for any significant difference (p < 0.05). The McNemar test was used searching for any significant difference in terms of sensitivity by comparing the ABUS and MRI results. The inter-observer agreement between the readers in evaluating the response to NAC for both MRI and ABUS was calculated using Cohen’s kappa k coefficient. Results: A total of 35 cases of complete response and 17 cases of persistent disease were found. MRI showed SE, SP, DA, PPV, and NPV values of 100%, 88%, 92%, 81%, and 100%, respectively, with an AUC value of 0.943 (p < 0.0001). ABUS showed SE, SP, DA, PPV, and NPV values of 88%, 94%, 92%, 89%, and 94%, respectively, with an AUC of 0.913 (p < 0.0001). The McNemar test revealed no significant difference (p = 0.1250). The inter-observer agreement between the two readers in evaluating the response to NAC for MRI and ABUS was, respectively, 0.88 and 0.89. Conclusions: Automatic breast ultrasound represents a new accurate, tri-dimensional and operator-independent tool for evaluating patients referred to NAC. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

11 pages, 2094 KiB  
Article
Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing Artificial Intelligence Diagnostic Precision
by Derek J. Van Booven, Cheng-Bang Chen, Sheetal Malpani, Yasamin Mirzabeigi, Maral Mohammadi, Yujie Wang, Oleksander N. Kryvenko, Sanoj Punnen and Himanshu Arora
J. Pers. Med. 2024, 14(7), 703; https://doi.org/10.3390/jpm14070703 - 30 Jun 2024
Cited by 2 | Viewed by 1248
Abstract
Introduction: In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic [...] Read more.
Introduction: In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality synthetic images of rare or underrepresented GU tissues. We hypothesized that augmenting the training data of computational pathology models with these GAN-generated images, validated through pathologist evaluation and quantitative similarity measures, would significantly enhance model performance in tasks such as tissue classification, segmentation, and disease detection. Methods: To test this hypothesis, we employed a GAN model to produce synthetic images of eight different GU tissues. The quality of these images was rigorously assessed using a Relative Inception Score (RIS) of 1.27 ± 0.15 and a Fréchet Inception Distance (FID) that stabilized at 120, metrics that reflect the visual and statistical fidelity of the generated images to real histopathological images. Additionally, the synthetic images received an 80% approval rating from board-certified pathologists, further validating their realism and diagnostic utility. We used an alternative Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA) to assess the quality of prostate tissue. This allowed us to make a comparison between original and synthetic data in the context of features, which were further validated by the pathologist’s evaluation. Future work will focus on implementing a deep learning model to evaluate the performance of the augmented datasets in tasks such as tissue classification, segmentation, and disease detection. This will provide a more comprehensive understanding of the utility of GAN-generated synthetic images in enhancing computational pathology workflows. Results: This study not only confirms the feasibility of using GANs for data augmentation in medical image analysis but also highlights the critical role of synthetic data in addressing the challenges of dataset scarcity and imbalance. Conclusions: Future work will focus on refining the generative models to produce even more diverse and complex tissue representations, potentially transforming the landscape of medical diagnostics with AI-driven solutions. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

13 pages, 4324 KiB  
Article
Automated 3-D Computer-Aided Measurement of the Bony Orbit: Evaluation of Correlations among Volume, Depth, and Surface Area
by Risto Kontio, Tommy Wilkman, Karri Mesimäki, Yurii Chepurnyi, Antti Asikainen, Aleksi Haapanen, Arto Poutala, Marko Mikkonen, Alina Slobodianiuk and Andrii Kopchak
J. Pers. Med. 2024, 14(5), 508; https://doi.org/10.3390/jpm14050508 - 11 May 2024
Viewed by 1503
Abstract
(1)The study aimed to measure the depth, volume, and surface area of the intact human orbit by applying an automated method of CT segmentation and to evaluate correlations among depth, volume, and surface area. Additionally, the relative increases in volume and surface area [...] Read more.
(1)The study aimed to measure the depth, volume, and surface area of the intact human orbit by applying an automated method of CT segmentation and to evaluate correlations among depth, volume, and surface area. Additionally, the relative increases in volume and surface area in proportion to the diagonal of the orbit were assessed. (2) CT data from 174 patients were analyzed. A ball-shaped mesh consisting of tetrahedral elements was inserted inside orbits until it encountered the bony boundaries. Orbital volume, area depth, and their correlations were measured. For the validation, an ICC was used. (3) The differences between genders were significant (p < 10−7) but there were no differences between sides. When comparing orbit from larger to smaller, a paired sample t-test indicated a significant difference in groups (p < 10−10). A simple linear model (Volume~1 + Gender + Depth + Gender:Depth) revealed that only depth had a significant effect on volume (p < 10−19). The ICCs were 1.0. (4) Orbital volume, depth, and surface area measurements based on an automated CT segmentation algorithm demonstrated high repeatability and reliability. Male orbits were always larger on average by 14%. There were no differences between the sides. The volume and surface area ratio did not differ between genders and was approximately 0.75. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

19 pages, 5164 KiB  
Article
The Role of the 18F-FDG PET/CT in the Management of Patients Suspected of Cardiac Implantable Electronic Devices’ Infection
by Antonio Rosario Pisani, Dino Rubini, Corinna Altini, Rossella Ruta, Maria Gazzilli, Angela Sardaro, Francesca Iuele, Nicola Maggialetti and Giuseppe Rubini
J. Pers. Med. 2024, 14(1), 65; https://doi.org/10.3390/jpm14010065 - 4 Jan 2024
Viewed by 1448
Abstract
Background: Infection of Cardiac Implantable Electronic Devices (CIEDI) is a real public health problem. The main aim of this study was to determine the diagnostic performance of 18F-FDG PET/CT in the diagnosis of CIEDI. Methods: A total of 48 patients, who performed [...] Read more.
Background: Infection of Cardiac Implantable Electronic Devices (CIEDI) is a real public health problem. The main aim of this study was to determine the diagnostic performance of 18F-FDG PET/CT in the diagnosis of CIEDI. Methods: A total of 48 patients, who performed 18F-FDG PET/CT for the clinical suspicion of CIEDI were retrospectively analyzed; all patients were provided with a model with procedural recommendations before the exam. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy (DA) of 18F-FDG PET/CT were calculated; the reproducibility of qualitative analysis was assessed with Cohen’s κ test. The semi-quantitative parameters (SUVmax, SQR and TBR) were evaluated in CIEDI+ and CIEDI− patients using the Student’ t-test; ROC curves were elaborated to detect cut-off values. The trend of image quality with regards to procedural recommendation adherence was evaluated. Results: Se, Sp, PPV, NPV and DA were respectively 96.2%, 81.8%, 86.2%, 94.7% and 89.6%. The reproducibility of qualitative analysis was excellent (K = 0.89). Semiquantitative parameters resulted statistically different in CIEDI+ and CIEDI− patients. Cut-off values were SUVmax = 2.625, SQR = 3.766 and TBR = 1.29. Trend curves showed increasing image quality due to adherence to procedural recommendations. Conclusions: 18F-FDG-PET/CT is a valid tool in the management of patients suspected of CIEDI and adherence to procedural recommendations improves its image quality. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

12 pages, 12059 KiB  
Article
The Role of Coronary CT Angiography in the Evaluation of Dual Left Anterior Descending Artery Prevalence and Subtypes: A Retrospective Multicenter Study
by Nicola Maggialetti, Sara Greco, Giovanni Lorusso, Cristiana Mileti, Gabriella Sfregola, Maria Chiara Brunese, Marcello Zappia, Maria Paola Belfiore, Pasquale Sullo, Alfonso Reginelli, Nicola Maria Lucarelli and Arnaldo Scardapane
J. Pers. Med. 2023, 13(7), 1127; https://doi.org/10.3390/jpm13071127 - 12 Jul 2023
Cited by 3 | Viewed by 1472
Abstract
Background: The aim of this multicenter study was to evaluate the prevalence and features of dual left anterior descending artery (LAD) subtypes using coronary CT angiography (CCTA). Methods: A retrospective multicenter analysis of 2083 CCTA from December 2020 to November 2022 was conducted [...] Read more.
Background: The aim of this multicenter study was to evaluate the prevalence and features of dual left anterior descending artery (LAD) subtypes using coronary CT angiography (CCTA). Methods: A retrospective multicenter analysis of 2083 CCTA from December 2020 to November 2022 was conducted to search for the presence and morphological features of dual LAD. The two classifications used were the updated classification of Spindola-Franco and the Jariwala classification. Statistical tests were conducted to evaluate the prevalence of dual LADs among sexes and its association with angina in patients without significant coronary stenoses and/or associated cardiac anomalies. Results: Dual LAD was observed in 124 (5.96%) patients analyzed. According to the Spindola-Franco revisited classification, type I dual LAD was the most common (71/124, 57.26%). According to the Jariwala classification, all cases were group I. In the general population, there was a higher prevalence of dual LAD among females (7.3% females vs. 5.1% males; p value: 0.04). No statistically significant difference was found in the prevalence of angina in the dual LAD population compared to the no dual LAD population (2.1% vs. 1.5%; p value: 0.10). Conclusions: The acknowledgment and reporting of LAD duplication is helpful for an optimal management of coronary patients with this condition. Dual LAD was more frequent in the female population, mainly not related with angina. Myocardial bridge was more frequent in the dual LAD population than in the no dual LAD population. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

Review

Jump to: Research

14 pages, 2970 KiB  
Review
Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma
by Maria Chiara Brunese, Pasquale Avella, Micaela Cappuccio, Salvatore Spiezia, Giulia Pacella, Paolo Bianco, Sara Greco, Luigi Ricciardelli, Nicola Maria Lucarelli, Corrado Caiazzo and Gianfranco Vallone
J. Pers. Med. 2024, 14(6), 572; https://doi.org/10.3390/jpm14060572 - 27 May 2024
Viewed by 1310
Abstract
Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. [...] Read more.
Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts. Full article
(This article belongs to the Special Issue State-of-the-Art Research on the Imaging in Personalized Medicine)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop