The aim of this thesis is to determine diagnostic performance of machine learning in differentiating between atypical cartilaginous tumor (ACT) and high-grade chondrosarcoma (CS) based on radiomic features derived from magnetic resonance imaging (MRI) and computed tomography (CT). In chapter 2, the concept of radiomics of musculoskeletal sarcomas is introduced and a systematic review on radiomic feature reproducibility and validation strategies is conducted. In chapter 3, a preliminary study is performed to investigate the performance of MRI radiomics-based machine learning in discriminating ACT from high-grade CS, using a single-center cohort, in comparison with an expert radiologist. In chapter 4, the influence of interobserver segmentation variability on the reproducibility of CT and MRI radiomic features of cartilaginous bone tumors is assessed. In chapter 5, the performance of CT radiomics-based machine learning in discriminating ACT from high-grade CS of long bones is determined and validated using independent data from a multicenter cohort, compared to an expert radiologist. In chapter 6, the performance of MRI radiomics-based machine learning in differentiating between ACT and grade II CS of long bones is determined and validated using independent data from a multicenter cohort, in comparison with an expert radiologist. Finally, in chapter 7, the main results and implications of this thesis are summarized and discussed.

RADIOMICS-BASED MACHINE LEARNING CLASSIFICATION OF BONE CHONDROSARCOMA / S. Gitto ; tutor: L.M. Sconfienza, J.L. Bloem (Leiden University Medical Center, The Netherlands) ; coordinatore: E. Berti. - : . Dipartimento di Scienze Biomediche per la Salute, 2022 Feb 16. ((34. ciclo, Anno Accademico 2021. [10.13130/gitto-salvatore_phd2022-02-16].

RADIOMICS-BASED MACHINE LEARNING CLASSIFICATION OF BONE CHONDROSARCOMA

S. Gitto
2022-02-16

Abstract

The aim of this thesis is to determine diagnostic performance of machine learning in differentiating between atypical cartilaginous tumor (ACT) and high-grade chondrosarcoma (CS) based on radiomic features derived from magnetic resonance imaging (MRI) and computed tomography (CT). In chapter 2, the concept of radiomics of musculoskeletal sarcomas is introduced and a systematic review on radiomic feature reproducibility and validation strategies is conducted. In chapter 3, a preliminary study is performed to investigate the performance of MRI radiomics-based machine learning in discriminating ACT from high-grade CS, using a single-center cohort, in comparison with an expert radiologist. In chapter 4, the influence of interobserver segmentation variability on the reproducibility of CT and MRI radiomic features of cartilaginous bone tumors is assessed. In chapter 5, the performance of CT radiomics-based machine learning in discriminating ACT from high-grade CS of long bones is determined and validated using independent data from a multicenter cohort, compared to an expert radiologist. In chapter 6, the performance of MRI radiomics-based machine learning in differentiating between ACT and grade II CS of long bones is determined and validated using independent data from a multicenter cohort, in comparison with an expert radiologist. Finally, in chapter 7, the main results and implications of this thesis are summarized and discussed.
SCONFIENZA, LUCA MARIA
BERTI, EMILIO
Settore MED/36 - Diagnostica per Immagini e Radioterapia
RADIOMICS-BASED MACHINE LEARNING CLASSIFICATION OF BONE CHONDROSARCOMA / S. Gitto ; tutor: L.M. Sconfienza, J.L. Bloem (Leiden University Medical Center, The Netherlands) ; coordinatore: E. Berti. - : . Dipartimento di Scienze Biomediche per la Salute, 2022 Feb 16. ((34. ciclo, Anno Accademico 2021. [10.13130/gitto-salvatore_phd2022-02-16].
Doctoral Thesis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/902991
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