Predicting Cancer Patient Survival Using Dynamic Contrast Enhanced MRI A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Medical and Human Sciences 2016 Ben Dickie School of Medicine Contents 1 Introduction 18 1.1 Locally advanced cancers . 18 1.1.1 The need for personalised treatments . 18 1.1.2 TNM stage . 24 1.1.3 Tumour microvasculature and hypoxia . 26 1.1.4 Existing methods for measuring microvascular function . 28 1.1.5 Existing methods for measuring tumour hypoxia . 29 1.2 Magnetic resonance imaging . 32 1.2.1 Nuclear magnetic resonance . 32 1.2.2 The bulk longitudinal magnetisation . 34 1.2.3 Nuclear excitation and relaxation . 36 1.2.4 Spatial localisation of NMR signals . 40 1.2.5 Gradient and spin echoes . 45 1.2.6 Image contrast . 45 1.3 Dynamic contrast-enhanced MRI . 49 1.3.1 Quantitative DCE-MRI . 50 1.3.2 A Quantitative DCE-MRI experiment . 51 1.3.3 Defining the tumour region of interest (ROIt) . 52 1.3.4 Estimating pre-contrast T1 ..................... 55 1.3.5 Dynamic imaging: measuring the TRF and AIF . 56 1.3.6 Tracer kinetic modelling . 60 1.3.7 Prognostic value of pre-treatment microvascular function . 66 1.3.8 Prognostic value of pre-treatment intratumoural microvascular heterogeneity . 68 1.4 Predicting patient prognosis . 78 1.4.1 Endpoints and censoring . 79 1.4.2 Modelling failure time . 80 1.4.3 Estimating S(t), h(t), and H(t) . 83 1.4.4 The Kaplan-Meier and Nelson-Aalen estimators . 85 1.4.5 Parametric models of S(t), h(t), and H(t) . 87 1.4.6 Parametric regression . 89 1.4.7 Cox proportional hazards regression . 89 1.4.8 Testing for differences between two survival distributions . 93 1.4.9 Random survival forest models . 95 1.4.10 Fitting an RSF model . 99 1.4.11 Ranking variables with the RSF . 104 1.4.12 Variable selection using RSF models . 107 1 1.5 Summary, hypotheses and aims . 109 2 Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast-enhanced MRI data 112 2.1 Contribution of authors . 112 2.2 Abstract . 112 2.3 Introduction . 113 2.4 Theory . 115 2.4.1 Sequential estimation . 115 2.4.2 Joint estimation . 118 2.5 Methods . 118 2.5.1 Synthetic data . 120 2.5.2 Clinical data . 122 2.5.3 AIF errors . 123 2.5.4 Monte Carlo and residual bootstrap analyses . 124 2.5.5 Model fitting . 124 2.5.6 Accuracy and precision . 125 2.5.7 Statistical analysis . 125 2.5.8 Sample size . 126 2.6 Results . 127 2.7 Discussion . 134 2.8 Acknowledgments . 138 2.9 Supporting materials . 139 2.9.1 Comparison of sequential and joint VFA T1,0 estimates with reference measurements . 139 2.9.2 Equality of S0,v and S0,d . 142 3 Predicting disease-free survival in locally advanced cervical cancer: a prospective DCE-MRI study 144 3.1 Contribution of authors . 144 3.2 Abstract . 144 3.3 Introduction . 145 3.4 Methods . 146 3.4.1 Patients . 146 3.4.2 Treatment . 147 3.4.3 Clinicopathologic variables . 147 3.4.4 MR imaging . 147 3.4.5 Tracer kinetic analysis . 148 3.4.6 Survival analysis . 151 3.5 Results . 153 2 3.6 Discussion . 163 3.6.1 Clinical relevance of findings . 164 3.6.2 Study limitations . 165 3.6.3 Conclusions . 166 3.7 Acknowledgements . 166 4 Imaging biomarkers of intratumoural microvascular heterogeneity are prognostic for disease-free survival in cervix, bladder, and head and neck cancers 167 4.1 Contribution of authors . 167 4.2 Abstract . 167 4.3 Introduction . 168 4.4 Methods . 170 4.4.1 Experimental design . 170 4.4.2 Patients . 173 4.4.3 Treatment . 173 4.4.4 Clinicopathologic variables . 174 4.4.5 MR imaging . 174 4.4.6 Measurement of microvascular function . 175 4.4.7 Measurements of microvascular heterogeneity . 176 4.4.8 Patient follow-up . 179 4.4.9 Statistical analysis . 179 4.5 Results . 180 4.6 Discussion . 188 4.7 Acknowledgements . 194 4.8 Supporting materials . 194 4.8.1 Additional information on the measurement and interpretation of heterogeneity biomarkers . 194 4.8.1.1 Histogram biomarkers . 194 4.8.1.2 Texture biomarkers . 195 4.8.1.3 Multispectral biomarkers . 195 4.8.1.4 Partitioning biomarkers . 196 trans 4.8.2 Proposed biomarkers: vvas and A . 196 5 High intratumoural variance in plasma flow is an adverse factor for locally advanced cancers of the cervix, bladder, and head and neck 198 5.1 Contribution of authors . 198 5.2 Abstract . 198 5.3 Introduction . 199 5.4 Methods . 202 3 5.4.1 Experimental design . 202 5.4.2 Patients . 202 5.4.3 Treatment . 203 5.4.4 Follow-up . 204 5.4.5 Clinicopathologic variables . 204 5.4.6 Imaging . 204 5.4.7 Tracer kinetic analysis . 205 5.4.8 Gaussian process regression . 206 5.4.9 Statistical analysis . 207 5.5 Results . 208 5.6 Discussion . 213 5.6.1 Limitations . 214 5.6.2 Conclusions . 215 5.7 Acknowledgements . 215 5.8 Supporting materials . 216 5.8.1 Modelling the appearance of parameter maps using a Gaussian process model . 216 6 Discussion and conclusions 219 6.1 Discussion . 219 6.1.1 Further work . ..