Συνδυαστική ανάλυση φαινότυπου και γονότυπου στον καρκίνο του πνεύμονα στο πλαίσιο της ραδιο-γονιδιωματικής

Summarization: During the last years, there is an increased interest in the development of models that intend to link cancer imaging features to the tumor genetic profile (Radiogenomics), in order to contribute in the diagnosis, evaluation, treatment planning and prognosis of lung cancer. Imaging features are extracted from the medical standard-of-care images and reflect the tumor phenotype. The tumor phenotype is formed by the rearrangement and the alterations of the genetic information. The gene mutations lead to cell proliferation and thus to cancer spread, which defines the cancer stage. There is an emerging need of valid diagnosis tools for lung cancer staging in order to define the proper treatment planning. This study aims at investigating correlations among the most significant imaging features and genes in lung cancer and their potential to detect the stage of the patients with Non-Small Cell Lung Cancer (NSCLC). The proposed analysis includes the identification of the differentially expressed genes between cancer and healthy population by the application of the Significance Analysis of Microarrays (SAM) algorithm and the 2-fold change technique. Subsequently, correlation of these genes with the Computed Tomography (CT) imaging-derived features was conducted through the Spearman rank correlation test, SAM for quantitative problems and False Discovery Rate (FDR) methods, revealing 78 significant genes correlated to imaging features. These genes were validated for their diagnostic character through classification and clustering techniques followed by the formation of clusters of co-expressed imaging features (metafeatures). From these two procedures, 77 homogeneous metafeatures and 73 significant genes were identified. These genes were analyzed with least absolute shrinkage and selection operation (LASSO) regression for their ability to predict the metafeatures accurately. Through the analysis, 51 metafeatures that are correlated and can be predicted with the genes, were identified. The last step was comprised of the examination of the predictive ability of the remaining significant genes and metafeatures in lung cancer staging through various classification tests using linear Support Vector Machines (SVM) algorithms. This study concluded that staging cancer could be predicted from a) genes, with an accuracy of 75.00% - 94.11%, b) metafeatures, with an accuracy of 70.83% - 95.00% and c) the combination of metafeatures and genes, with an accuracy of 85.24% - 100.00%. Additionally, artificial imaging features were produced from the linear combination of the genes that could replace the actual metafeatures and predict cancer staging with an accuracy of 76.47% - 83.60%. Finally, signaling and metabolism pathways as well as miRNA targets were revealed during the enrichment analysis of the derived gene signatures.Περίληψη: Tα τελευταία χρόνια έχει παρατηρηθεί αυξημένο επιστημονικό ενδιαφέρον, για την ανάπτυξη μοντέλων, τα οποία στοχεύουν στην συσχέτιση απεικονιστικών χαρακτηριστικών του καρκίνου με το γενετικό του προφίλ (Ραδιο-γονιδιωματική), ώστε να συμβάλουν στην διάγνωση, αξιολόγηση, θεραπεία και πρόγνωση του καρκίνου του πνεύμονα. Τα απεικονιστικά χαρακτηριστικά εξάγονται από ιατρικές standard-of-care εικόνες και αντιπροσωπεύουν τον καρκινικό φαινότυπο. Ο καρκινικός φαινότυπος δημιουργείται από την αναδιάταξη και τις αλλοιώσεις τις γενετικής πληροφορίας. Η μετάλλαξη των γονιδίων οδηγεί στον κυτταρικό πολλαπλασιασμό και κατά συνέπεια, στην εξάπλωση του καρκίνου, η οποία χαρακτηρίζει το καρκινικό στάδιο. Έγκυρα διαγνωστικά εργαλεία για την αναγνώριση του καρκινικού σταδίου είναι αναγκαία, ώστε να επιλεγεί η κατάλληλη θεραπεία. Η παρούσα έρευνα έχει ως στόχο την εξερεύνηση συσχετίσεων μεταξύ των πιο σημαντικών απεικονιστικών χαρακτηριστικών και γονιδίων του καρκίνου του πνεύμονα και της δυνατότητάς τους να ανιχνεύσουν το καρκινικό στάδιο ασθενών με μη-μικροκυτταρικό καρκίνο του πνεύμονα (ΜΜΚΠ). Η παρούσα ανάλυση περιλαμβάνει την αναγνώριση των διαφορετικά εκφραζόμενων γονιδίων μεταξύ πληθυσμών που έχουν προσβληθεί από καρκίνο και υγιών πληθυσμών, μέσω της εφαρμογής του αλγορίθμου Significance Analysis of Microarrays (SAM) και της τεχνικής 2-fold change. Εν συνεχεία, υλοποιήθηκαν συσχετίσεις των γονιδίων με παραγόμενα απεικονιστικά χαρακτηριστικά αξονικής τομογραφίας, μέσω των μεθόδων Spearman rank correlation test, SAM για ποσοτικά προβλήματα και False Discovery Rate (FDR), αποκαλύπτοντας 78 σημαντικά γονίδια συσχετιζόμενα με απεικονιστικά χαρακτηριστικά. Τα γονίδια αυτά, αξιολογήθηκαν ως προς την εγκυρότητά τους για τον διαγνωστικό τους χαρακτήρα μέσω τεχνικών ταξινόμησης και clustering. Ακολούθησε ο σχηματισμός clusters από συνεκφραζόμενα απεικονιστικά χαρακτηριστικά (metafeatures). Από αυτές τις δυο διαδικασίες , 77 ομογενή metafeatures και 73 σημαντικά γονίδια αναγνωρίστηκαν. Τα γονίδια αναλύθηκαν μέσω του αλγορίθμου Least Absolute Shrinkage and Selection Operation (LASSO) regression, για να διερευνηθεί η δυνατότητά τους να προβλέψουν με ακρίβεια τα metafeatures. Μέσω της ανάλυσης, 51 metafeatures, τα οποία είναι συσχετιζόμενα και μπορούν να προβλεφθούν μέσω των γονιδίων, αναγνωρίστηκαν. Το τελευταίο στάδιο περιλάμβανε την εξέταση της προβλεπτικής ικανότητας των εναπομεινάντων σημαντικών γονιδίων και metafeatures, του καρκίνου του πνεύμονα, μέσω ποικίλων τεστ ταξινόμησης χρησιμοποιώντας Linear Support Vector Machines (SVM) αλγορίθμους. Η παρούσα έρευνα είχε ως βασικό συμπέρασμα ότι, το καρκινικό στάδιο μπορεί να προβλεφθεί μέσω a) γονιδίων, με ακρίβεια 75.00%-95.11%, b) metafeatures, με ακρίβεια 70.83%-95.00%, και c) συνδυασμού metafeatures και γονιδίων, με ακρίβεια 85.24%-100.00%. Επιπλέον, τεχνητά απεικονιστικά χαρακτηριστικά παράχθηκαν μέσω γραμμικού συνδυασμού γονιδίων, τα οποία δείχνουν ότι μπορούν να αντικαταστήσουν τα πραγματικά metafeatures και να προβλέψουν το καρκινικό στάδιο με ακρίβεια 76.47%-83.60%. Τέλος, ανακαλύφθηκαν σηματοδοτικά και μεταβολικά μονοπάτια καθώς και miRNA targets μέσω της ανάλυσης εμπλουτισμού των παραγόμενων γονιδιακών υπογραφών

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC

Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification