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Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices. 23 5 2063 2079

The epidermal growth factor receptor (EGFR) is a key target in anticancer research, whose aberrant function in malignancies has been linked to severe irregularities in critical cellular processes, including cell cycle progression, proliferation, differentiation, and survival. EGFR mutant variants, either transmembrane or translocated to the mitochondria and/or the nucleus, often exhibit resistance to EGFR inhibitors. The ability to noninvasively image and quantify EGFR provides novel approaches in the detection, monitoring, and treatment of EGFR-related malignancies. The current study aimed to deliver a new theranostic agent that combines fluorescence imaging properties with EGFR inhibition. This was achieved via conjugation of an in-house-developed ((4-bromophenyl)amino)quinazoline inhibitor of mutant EGFR-TK, selected from a focused aminoquinazoline library, with a [Ru(bipyridine)3]2+ fluorophore. A triethyleneglycol-derived diamino linker featuring (+)-ionizable sites was employed to link the two functional moieties, affording two unprecedented Ru conjugates with 1:1 and 2:1 stoichiometry of aminoquinazoline to the Ru complex (mono-quinazoline-Ru-conjugate and bis-quinazoline-Ru-conjugate, respectively). The bis-quinazoline-Ru-conjugate, which retains an essential inhibitory activity, was found by fluorescence imaging to be effectively uptaken by Uppsala 87 malignant glioma (grade IV malignant glioma) cells. The fluorescence imaging study and a time-resolved fluorescence resonance energy transfer study indicated a specific subcellular distribution of the conjugate that coincides with that of a mitochondria-targeted dye, suggesting mitochondrial localization of the conjugate and potential association with mitochondria-translocated forms of EGFR. Mitochondrial localization was further documented by the specific concentration of the bis-quinazoline-Ru-conjugate in a mitochondrial isolation assay. 16 10 4260 4273

The current state of the art on bacterial classification using Raman and Surface Enhanced Raman Spectroscopy (SERS) for the purpose of developing a rapid and more accurate method for urinary tract infection (UTI) diagnosis is presented. SERS, an enhanced version of Raman offering much increased sensitivity, provides complex biochemical information which, in conjunction with advanced analysis and classification techniques, can become a valuable diagnostic tool. The variety of metal substrates used for SERS, including silver and gold colloids, as well as nanostructured metal surfaces, is reviewed. The challenges in preprocessing noisy and complicated spectra and the various methods used for feature creation as well as a novel method using spectral band ratios are described. The various unsupervised and supervised classification methods commonly used for SERS spectra of bacteria are evaluated. Current research on transforming SERS into a valuable clinical tool for the diagnosis of UTIs is presented. Specifically, the classification of bacterial spectra (a) as positive or negative for an infection, (b) as belonging to a particular species of bacteria, and (c) as sensitive or resistant to an antibiotic are described. This work can lead to the development of novel technology with extremely important benefits for public health.

Breast cancer is one of the most deadly malignancies worldwide and the second leading cause of death in women. Mammography, i.e. the screening for breast cancer with x-ray imaging, has significantly improved the prognosis of patients diagnosed with the disease. The evaluation of mammograms requires a panel of radiologists, but even well trained experts can err in their assessment. For this reason, Computer-Aided Detection (CAD) systems are becoming more prevalent. In this paper, we introduce a novel approach for breast Micro-Calcification (MC) diagnosis using temporal sequences of digital mammograms. The goal is to increase the MC detection accuracy by subtracting prior images. A new dataset, with precise marking of MC locations, was created specifically for this study. The proposed approach began with temporal subtraction of mammograms, after demon-based registration, which effectively removed unchanged regions and MCs (17.3% reduction in the number of MCs). The second step was the classification of the MCs as benign or suspicious using the subtracted images. A set of diverse features were selected for the classification. Four different classifiers were tested with leave-one-patient-out cross-validation. For comparison, the MC classification was also performed, using single mammograms, without temporal subtraction. The average accuracy of the classification of the MCs as benign or suspicious was 91.3% without and 99.2% with temporal subtraction using Support Vector Machines (statistically significantp=0.026). These results show that temporal subtraction could be a valuable addition to CAD systems to assist radiologists in effectively detecting breast MCs.

The effects of dispersion on optical coherence tomography (OCT) images have long been documented. The imbalance of spectral broadening, caused by dispersion mismatches in the two arms of the OCT interferometer, can result in significant resolution degradation. Efforts to correct this phenomenon have resulted in improved image quality using various techniques. However, dispersion is also present and varies in tissues. As a result, group velocity dispersion (GVD) can be used to detect changes in tissues and provide useful information for diagnosis. Several methods can be utilized to measure the GVD from OCT images: (i) the degradation of the point spread function (PSF), (ii) the shift (walk-off) between images taken at different wavelengths, (iii) the changes in the second derivative of the spectral phase, as well as two new methods, which do not require a reflector and are applicable in intact tissues, i.e., using (iv) the speckle degradation, and (v) the speckle cross correlation. A systematic, experimental, evaluation of these methods is presented to elucidate the capabilities, the limitations, and the accuracy of each technique when attempting to estimate the GVD in scattering samples. The most precise values were obtained from the estimation of the PSF degradation, whereas using the phase derivative method was only applicable to minimally scattering samples. Speckle broadening appears to be the most robust method for tissue GVD measurements.

Includes bibliographical references. Number of sources in the bibliography: 259. Thesis (Ph. D.) -- University of Cyprus, Faculty of Engineering, Department of Electrical and Computer Engineering, 2023. The University of Cyprus Library holds the printed form of the thesis. Ο καρκίνος είναι η δεύτερη πιο συχνή αιτία θανάτου παγκοσμίως, με τον καρκίνο του μαστού να ανέρχεται περίπου στο ~20% όλων των νέων περιστατικών παγκοσμίως. Η μαστογραφία αποτελεί το σημαντικότερο εργαλείο προ-συμπτωματικού ελέγχου για την έγκαιρη διάγνωση αλλά και τη διαχείριση του καρκίνου του μαστού. Τα τρέχοντα πρωτόκολλα απαιτούν αξιολόγηση της μαστογραφίας από δύο ακτινολόγους (και έναν τρίτο, εάν δεν υπάρχει ομοφωνία), γεγονός που αποτελεί ένδειξη των προκλήσεων που αντιμετωπίζουν οι ειδικοί στην προσπάθειά τους να αξιολογήσουν πιθανές ανωμαλίες σε μια μαστογραφία. Πιο συγκεκριμένα, σε ποσοστό 10% έως 30% των περιπτώσεων του καρκίνου του μαστού, οι ανωμαλίες δεν ανιχνεύονται, με αποτέλεσμα στο ~50% των περιπτώσεων να υπάρχουν ψευδώς αρνητικά αποτελέσματα. Τα τελευταία χρόνια διάφορα υπολογιστικά συστήματα έχουν αναπτυχθεί με σκοπό να υποστηρίξουν τους ακτινολόγους στη διάγνωση και ανίχνευση του καρκίνου του μαστού. Προκειμένου να εντοπίσουν, πιο αποτελεσματικά, πιθανές ανωμαλίες, οι ακτινολόγοι συγκρίνουν πρόσφατες και προηγούμενες μαστογραφίες του ίδιου ασθενή. Οι προηγούμενες μαστογραφίες, όταν είναι διαθέσιμες, μπορούν να παρέχουν χρήσιμες πληροφορίες στους ακτινολόγους. Έχει παρατηρηθεί ότι οι ύποπτες ανωμαλίες τείνουν να αναπτύσσονται μετά από μία “καθαρή” προηγούμενη μαστογραφία. Σύγκριση των εικόνων από τις δύο διαδοχικές απεικονίσεις χρησιμοποιείται συχνά από τους ακτινολόγους ώστε να αναγνωρίσουν και να αξιολογήσουν καινούργιες αλλοιώσεις που είναι πιο πιθανόν να είναι κακοήθεις. Η ανάλυση μαστογραφιών, με την συμπερίληψη προηγούμενων απεικονίσεων που είχαν αξιολογηθεί ως φυσιολογικές, θα μπορούσε να βελτιώσει σημαντικά τη διαγνωστική ικανότητα των αυτόματων συστημάτων, με σημαντικά οφέλη στην πρόληψη και διάγνωση του καρκίνου του μαστού. Σε αυτή τη διατριβή, αναπτύχθηκε ένας αλγόριθμος για την ακριβή ανίχνευση, κατηγοριοποίηση και πρόβλεψη ανωμαλιών του μαστού, που συνδυάζει την αφαίρεση διαδοχικών μαστογραφιών με αλγορίθμους μηχανικής μάθησης. Ο αλγόριθμος κατηγοριοποιεί τις κακοήθειες του μαστού με υψηλή ακρίβεια (90.3% για τις επασβεστώσεις, 98% για μάζες), και προβλέπει την ανάπτυξη κακοήθειας του μαστού στον επόμενο γύρο, με 83.7% ακρίβεια. Ο αλγόριθμος έχει τη δυνατότητα να εφαρμοστεί κλινικά σαν εργαλείο υποστήριξης των ακτινολόγων, αλλά και να προσφέρει βοήθεια σε λιγότερο ανεπτυγμένες χώρες με έλλειψη ακτινολόγων. Επίσης, έχει τη δυνατότητα ενισχύσει την εξατομικευμένη θεραπεία στην Κύπρο. Cancer is the second most frequent cause of mortality worldwide with breast cancer accounting for ~20% of all new cancer cases worldwide. Mammography is an effective screening tool for the early detection and management of breast cancer. Current protocols require evaluation of the mammogram by two radiologists (and a third, if consensus is not reached), which is an indication of the challenges faced, even by experts, when attempting to identify potential abnormalities in a mammogram. Specifically, 10% to 30% of breast cancer cases are missed, resulting in a false negative rate of ~50%, depending on the type of abnormality, the age of the patient, and the breast density. To address the challenge of the radiological assessment of mammograms, Computer-Aided Diagnosis (CAD) systems are being developed to aid radiologists in the diagnosis of breast cancer. Radiologists routinely compare recent and prior images in order to more effectively identify abnormalities that have been developed between the screenings. When available, prior imaging can provide useful insights to the clinicians, allowing them to identify possible signs of malignancy earlier and with more certainty. It has been observed that suspicious abnormalities tend to develop after a “clear” prior mammogram, characterized as normal. Comparison of current and prior images is routinely used by radiologists to identify and evaluate new lesions that are more likely to be malignant. Analysis of mammographic images, including comparison with prior mammograms could significantly improve the accuracy of CAD systems, providing major benefits in the prevention and diagnoses of breast cancer. In this dissertation, an automatic algorithm was developed for the accurate detection, classification, and prediction of breast abnormalities, by combining subtraction of temporally sequential mammograms with machine learning. The algorithm classifies breast abnormalities with high accuracy, 90.3% for micro-calcifications and 98% for masses, and predicts the development of breast cancer in the next screening round, with 83.7% accuracy. The algorithm has the potential to be translated into clinical practice as a breast-cancer second reading tool, provide valuable assistance to clinicians, especially in less-developed countries, and substantially contribute to the early prediction of near-term breast cancer, with significant impact on patient prognosis.