RADIOMICS FOR OUTCOME PREDICTION IN EARLY-STAGE NON-SMALL CELL LUNG CANCER PATIENTS TREATED WITH STEREOTACTIC BODY RADIOTHERAPY (SBRT): METHODOLOGICAL CHALLENGES AND CLINICAL APPLICATIONS
type: CreativeWork
description: ABSTRACT In recent years, Radiation Oncology has taken advantage of progresses in the fields of biology, computational sciences, and medical engineering. This has laid the foundation for the incorporation of high-throughput data, deriving from different sources, including medical images, histopathological samples, and blood. Of these, quantitative imaging-derived biomarkers represent an appealing and actionable source of information in addition to classical qualitative and semi-quantitative parameters used in clinical practice. This field of study, referred to as radiomics, is the subject of several investigations, on multiple disease sites. Of these, Non-Small Cell Lung Cancer (NSCLC) is a good model of study, not only for its relevance- being one of the so-called “big killers” in Oncology- but also for the availability of preliminary results, suggesting the potentials of radiomics in this clinical setting. Specifically, focus will be given to early-stage NSCLC, a stage of limited disease involvement which may be treated with either surgery, or radiotherapy. On these premises, one of the main scopes of this thesis is to investigate currently unsolved methodological questions in the radiomic pipeline for NSCLC; focusing on common issues, such as the impact of image preprocessing and the stability of radiomic features across the respiratory phases of the simulation computed tomography. To do so, statistics and machine learning methods will be explored and discussed with an eye to their scalability to current clinical practice. Furthermore, the construction of clinical, radiomic and clinico-radiomic models on a retrospective highly-curated dataset will be presented. Other than presenting and commenting these results, space will be given to current limitations of clinico-radiomic models, including their explainability and generalizability. Finally, a Chapter will be dedicated to the presentation of a prospective observational trial- which has recently been funded by th…
abstract: ABSTRACT In recent years, Radiation Oncology has taken advantage of progresses in the fields of biology, computational sciences, and medical engineering. This has laid the foundation for the incorporation of high-throughput data, deriving from different sources, including medical images, histopathological samples, and blood. Of these, quantitative imaging-derived biomarkers represent an appealing and actionable source of information in addition to classical qualitative and semi-quantitative parameters used in clinical practice. This field of study, referred to as radiomics, is the subject of several investigations, on multiple disease sites. Of these, Non-Small Cell Lung Cancer (NSCLC) is a good model of study, not only for its relevance- being one of the so-called “big killers” in Oncology- but also for the availability of preliminary results, suggesting the potentials of radiomics in this clinical setting. Specifically, focus will be given to early-stage NSCLC, a stage of limited disease involvement which may be treated with either surgery, or radiotherapy. On these premises, one of the main scopes of this thesis is to investigate currently unsolved methodological questions in the radiomic pipeline for NSCLC; focusing on common issues, such as the impact of image preprocessing and the stability of radiomic features across the respiratory phases of the simulation computed tomography. To do so, statistics and machine learning methods will be explored and discussed with an eye to their scalability to current clinical practice. Furthermore, the construction of clinical, radiomic and clinico-radiomic models on a retrospective highly-curated dataset will be presented. Other than presenting and commenting these results, space will be given to current limitations of clinico-radiomic models, including their explainability and generalizability. Finally, a Chapter will be dedicated to the presentation of a prospective observational trial- which has recently been funded by th…
author: VOLPE, STEFANIA
language: eng
keywords: non-small cell lung cancer, radiomic, radiation oncology, outcome modeling, Settore MED/36 - Diagnostica per Immagini e Radioterapia
date published: 2023-12-12
date modified: 2026-07-27T20:18:46Z
provider: Università degli Studi di Milano
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Milano

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2434/1018373

PropertyValue
property ID: DOI
value: 10.13130/volpe-stefania_phd2023-12-12