Doctoral theses
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Current Record: 46 of 2491
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Identifier |
000463762 |
Title |
Multi-modal data integration using machine and deep learning techniques for predicting high-level clinical outcomes of non-small cell lung cancer patients |
Alternative Title |
Πολυτροπική ενσωμάτωση δεδομένων με χρήση τεχνικών μηχανικής και βαθιάς μάθησης για την πρόβλεψη κλινικών αποτελεσμάτων ασθενών με μη μικροκυτταρικό καρκίνο του πνεύμονα |
Author
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Τριβιζάκης, Ελευθέριος
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Thesis advisor
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Σουγλάκος, Ιωάννης
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Reviewer
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Καραντάνας, Απόστολος
Μαριάς, Κωνσταντίνος
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Abstract |
Contemporary research on multi-omic analysis is expected to play an important
role in the correlation of genotypes with imaging phenotypes by enhancing the
impact of medical image analysis and multi-modal data integration in oncology
towards precision medicine. The association of medical data with molecular or
genetic features of the examined disease can lead to more accurate diagnosis,
enhancement of personalized treatment in patients with lung cancer, and will be a
key contribution in identifying the underlining molecular mechanisms that cause
the disease’s pathogenesis. Furthermore, using machine learning-based multimodal integration to predict the response of specific treatments can spare patients
from unnecessary procedures and improve their quality of life. Additionally, this
can lead to an optimized cost-benefit ratio, especially when considering new and
expensive health care treatments. The examined population of this study consists of
patients with primary non-small cell lung cancer with available imaging
examinations, transcriptomic, and clinical/pathological data such as smoking
history, tumor staging, and mutational burden of specific oncogenes. The latter is
particullarly important since the genetic expression of various targeted oncogenes
has been likend to therapy response for lung cancer patients. In this thesis, advanced
treatment decision systems based on deep learning and artificial intelligence have
been developed to predict the high-level clinical outcomes of patients with nonsmall cell lung cancer, including molecural subtypes, survival prediction, and
therapy response.
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Language |
English |
Subject |
Computed tomography |
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Image analysis |
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Multi-omics |
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Radiomics |
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Transcriptomics |
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Ανάλυση εικόνας |
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Αξονική τομογραφία |
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Μεταγράγωμα |
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πολυ-ομική |
Issue date |
2024-04-17 |
Collection
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School/Department--School of Medicine--Department of Medicine--Doctoral theses
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Type of Work--Doctoral theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/8/4/4/metadata-dlib-1712556818-3857-18610.tkl
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Views |
534 |
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