Recent Trends in Molecular Image-Guided Theranostic and Personalized Medicine

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 11644

Special Issue Editors


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Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: data science; machine learning; data structures and algorithms; systems engineering; neural networks; data mining; project management; tensor flow; predictive modelling; artificial intelligence; hadoop; apache spark; software development; empirical researchbig data
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Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Interests: computational intellgence; neural networks; image processing; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sudden increase in the market for molecular imaging science enhances the opportunities to develop new molecular imaging agents for diagnosis and therapy. New clinical indications using the proteomic or genomic expression in oncology, cardiology, and neurology also offer a stimulus. This promotes the growth of procedure volume and sales of clinic imaging agents and the development of new radiopharmaceuticals. The growing use of molecular imaging is also helping to control and monitor dosage for increased safety and effectiveness. For instance, molecular imaging in oncology has been focused on identifying tumor-specific markers and applying these markers for the evaluation of patient response to radiation therapy, chemotherapy, or chemo/radiotherapy. Therefore, molecular imaging technologies play a significant role in providing personalized therapy for patients. The opportunity to use image-guiding to select a patient for personalized therapy is truly the focus. The imaging findings could be integrated with metabolomics. In addition, the theranostic concept is equally important in the personalized therapy of diseases. The effort in image-guided cell therapy theranostic approaches in parallel with instrumentation development would be more accurate in evaluating patient response to treatment. This Special Issue will provide new molecular imaging agents and instrumentation development trends. This Special Issue will become the scientific tool for moving a concept from bench work to clinic product development. This Special Issue will be interesting to translational research scientists and support staff, such as clinicians, molecular biologists, imaging scientists, pharmaceutical developers, physicists, fellows, and staff. Both academic and clinical scientists are invited to submit their manuscripts.
Potential topics include, but are not limited to: The image-guided theranostic approach of diseases. Advances in bioimaging applications in preclinical drug discovery. Advances in imaging instrumentation development. Hybrid imaging modalities in disease management. Theranostic agents development. Imaging technology in drug development. Validation of imaging agents on new molecular targets. Personalized drug development from molecular imaging. Link metabolomics and imaging molecular pathways. Treatment delivery using big radiomics analysis. Patient follow-up using big deep analysis. Patient diagnosis, assessment, and consultation in big data. Computer-aided detection and diagnosis. Treatment simulation using computer vision. Image registration/fusion. Image segmentation/auto-contouring. 

Dr. Muhammad Fazal Ijaz
Prof. Dr. Marcin Woźniak
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. Diagnostics 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 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

  • image-guided theranostic approach of diseases
  • advances in bioimaging applications in preclinical drug discovery
  • PET/CT and SPECT/CT in disease management
  • radiation dosimetric determination for radiotheranostic agents
  • imaging technology in drug development
  • validation of imaging agents on new molecular targets
  • personalized drug development from molecular imaging
  • nonradioactive molecular imaging probes

Published Papers (6 papers)

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Research

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15 pages, 2065 KiB  
Article
Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform
by Manoj Diwakar, Prabhishek Singh, Ravinder Singh, Dilip Sisodia, Vijendra Singh, Ankur Maurya, Seifedine Kadry and Lukas Sevcik
Diagnostics 2023, 13(8), 1395; https://doi.org/10.3390/diagnostics13081395 - 12 Apr 2023
Cited by 4 | Viewed by 1569
Abstract
Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract [...] Read more.
Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information. Full article
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26 pages, 4544 KiB  
Article
Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner
by E. Rajesh, Shajahan Basheer, Rajesh Kumar Dhanaraj, Soni Yadav, Seifedine Kadry, Muhammad Attique Khan, Ye Jin Kim and Jae-Hyuk Cha
Diagnostics 2023, 13(1), 95; https://doi.org/10.3390/diagnostics13010095 - 28 Dec 2022
Cited by 8 | Viewed by 1867
Abstract
The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to [...] Read more.
The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods. Full article
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17 pages, 7239 KiB  
Article
Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain
by Manoj Diwakar, Prabhishek Singh, Chetan Swarup, Eshan Bajal, Muskan Jindal, Vinayakumar Ravi, Kamred Udham Singh and Teekam Singh
Diagnostics 2022, 12(11), 2766; https://doi.org/10.3390/diagnostics12112766 - 12 Nov 2022
Cited by 3 | Viewed by 1292
Abstract
In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and [...] Read more.
In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones. Full article
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15 pages, 2586 KiB  
Article
An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism
by Tariq Saeed Mian
Diagnostics 2022, 12(8), 1796; https://doi.org/10.3390/diagnostics12081796 - 25 Jul 2022
Cited by 6 | Viewed by 1602
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD [...] Read more.
Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD based on vocal disorders is at the forefront of research. In this paper, an experimental study is performed on an open source Kaggle PD speech dataset and novel comparative techniques were employed to identify PD. We proposed an unsupervised autoencoder feature selection technique, and passed the compressed features to supervised machine-learning (ML) algorithms. We also investigated the state-of-the-art deep learning 1D convolutional neural network (CNN-1D) for PD classification. In this study, the proposed algorithms are support vector machine, logistic regression, random forest, naïve Bayes, and CNN-1D. The classifier performance is evaluated in terms of accuracy score, precision, recall, and F1 score measure. The proposed 1D-CNN model shows the highest result of 0.927%, and logistic regression shows 0.922% on the benchmark dataset in terms of F1 measure. The major contribution of the proposed approach is that unsupervised neural network feature selection has not previously been investigated in Parkinson’s detection. Clinicians can use these techniques to analyze the symptoms presented by patients and, based on the results of the above algorithms, can diagnose the disease at an early stage, which will allow for improved future treatment and care. Full article
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Review

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21 pages, 1767 KiB  
Review
A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion
by Manoj Diwakar, Prabhishek Singh, Vinayakumar Ravi and Ankur Maurya
Diagnostics 2023, 13(5), 820; https://doi.org/10.3390/diagnostics13050820 - 21 Feb 2023
Cited by 6 | Viewed by 1873
Abstract
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain [...] Read more.
Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction. To obtain the most clinically relevant information, multi-modality-based image fusion is beneficial. Nevertheless, numerous multi-modality-based image fusion techniques are present in the literature. Each method has its assumptions, merits, and barriers. This paper critically analyses some sizable non-conventional work within multi-modality-based image fusion. Often, researchers seek help in apprehending multi-modality-based image fusion and choosing an appropriate multi-modality-based image fusion approach; this is unique to their cause. Hence, this paper briefly introduces multi-modality-based image fusion and non-conventional methods of multi-modality-based image fusion. This paper also signifies the merits and downsides of multi-modality-based image fusion. Full article
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24 pages, 2006 KiB  
Review
Current Status of 68Ga-Pentixafor in Solid Tumours
by Bawinile Hadebe, Machaba Michael Sathekge, Colleen Aldous and Mariza Vorster
Diagnostics 2022, 12(9), 2135; https://doi.org/10.3390/diagnostics12092135 - 02 Sep 2022
Cited by 3 | Viewed by 2316
Abstract
Chemokine receptor CXCR4 is overexpressed in neoplasms and its expression is related to tumour invasion, metastasis and aggressiveness. 68Ga-Pentixafor is used to non-invasively image the expression of CXCR4 in tumours and has been widely used in haematological malignancies. Recent evidence shows that [...] Read more.
Chemokine receptor CXCR4 is overexpressed in neoplasms and its expression is related to tumour invasion, metastasis and aggressiveness. 68Ga-Pentixafor is used to non-invasively image the expression of CXCR4 in tumours and has been widely used in haematological malignancies. Recent evidence shows that therapies targeting CXCR4 can increase the chemosensitivity of the tumour as well as inhibit tumour metastasis and aggressiveness. 68Ga-Pentixafor has shown promise as an elegant radiotracer to aid in the selection of patients whose tumours demonstrate CXCR4 overexpression and who therefore may benefit from novel therapies targeting CXCR4. In addition, its therapeutic partners 177Lu- and 90Y-Pentixather have been investigated in the treatment of patients with advanced haematological malignancies, and initial studies have shown a good treatment response in metabolically active lesions. 68Ga-Pentixafor in solid tumours complements 18F-FDG by providing prognostic information and selecting patients who may benefit from therapies targeting CXCR4. This review summarises the available literature on the potential applications of 68Ga-Pentixafor in solid tumours. Full article
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