Leiden University Scholarly Publications

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Kantidakis, G. (2022)

Analysis of sarcoma and non-sarcoma clinical data with statistical methods and machine learning techniques

Doctoral Thesis
This thesis sprang from an interdisciplinary collaboration between the European Organisation for Research and Treatment of Cancer (EORTC), the Mathematical Institute of Leiden University, and the Leiden University Medical Center (LUMC) Department of Medical Oncology. Research was split into two separate parts. In Part I, the main goal was to provide modern efficacy thresholds for designing new phase II clinical trials for common histotypes of locally advanced or metastatic soft-tissue sarcoma patients. An update was necessary as well-established values were reported back in 2002 by the EORTC – Soft Tissue and Bone Sarcoma Group.
Nowadays, there is a growing interest by the medical community in applying machine learning to predict clinical outcomes. In Part II, the main goal was to investigate the potential of existing and novel machine learning techniques compared with traditional statistical benchmarks for real-life clinical data (small/medium or large sample sizes, low- or...Show moreThis thesis sprang from an interdisciplinary collaboration between the European Organisation for Research and Treatment of Cancer (EORTC), the Mathematical Institute of Leiden University, and the Leiden University Medical Center (LUMC) Department of Medical Oncology. Research was split into two separate parts. In Part I, the main goal was to provide modern efficacy thresholds for designing new phase II clinical trials for common histotypes of locally advanced or metastatic soft-tissue sarcoma patients. An update was necessary as well-established values were reported back in 2002 by the EORTC – Soft Tissue and Bone Sarcoma Group.
Nowadays, there is a growing interest by the medical community in applying machine learning to predict clinical outcomes. In Part II, the main goal was to investigate the potential of existing and novel machine learning techniques compared with traditional statistical benchmarks for real-life clinical data (small/medium or large sample sizes, low- or high-dimensional settings) with time-to-event endpoints. Findings indicate an urgent need to pay closer attention to calibration (absolute predictive accuracy) of machine learning techniques to achieve a complete comparison with statistical models.
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Predictive performance
Regression models
Soft-tissue sarcoma
Supervised machine learning
Survival analysis
All authors
Kantidakis, G.
Supervisor
Gelderblom, A.J.; Fiocco, M.
Co-supervisor
Litière, S.
Committee
Sande, M.A.J. van de; Legrand, C.; Spruit, M.R.; Merks, J.H.M.; Ieva, F.
Qualification
Doctor (dr.)
Awarding Institution
Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University
Date
2022-11-23
ISBN (print)
9789036105620

Funding

Sponsorship
Financial support for the publication of this thesis was kindly provided by the European Organisation for Research and Treatment of Cancer - Soft Tissue and Bone Sarcoma Group (EORTC - STBSG), the EORTC Cancer Research Fund (ECRF), and the Leiden University Medical Center (LUMC) Department of Medical Oncology.