Radiobiological Optimization of Lung and Prostate Radiotherapy Treatments - A Macroscopic Approach

Karia, DB
(2018) Radiobiological Optimization of Lung and Prostate Radiotherapy Treatments - A Macroscopic Approach. Doctor of Philosophy thesis, University of Liverpool.

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Radiobiological modelling is applied to different treatment individualization & optimization strategies and the resulting improvements in treatment outcomes quantified for lung and prostate cancer treatments; this is compared to current approaches based on delivering identical dose in fixed (size and number of) fractions to a given tumour type. In the first investigation, dose-based escalation (level 0) is compared to Iso-toxic (i.e. Iso-NTCP) fixed fraction number prescriptions (level 1) and Iso-toxic prescription dose & fraction number optimization (level 2). NTCP, dose-scaling factor and number of fractions are the parameters used to optimize TCP; multiple dose-limiting OAR endpoints are accounted for in the above analysis. It is shown that Iso-toxic (at 8.6% NTCP) dose fractionation optimization improves the average TCP of the lung cohort by ~19% compared to standard dose fractionation (55 Gy in 20 fractions yielding 8.6% average NTCP). Similarly, for prostate cancer treatment, it is demonstrated that level-2 optimization is superior to standard treatment 60 Gy in 20 fractions): a population TCP increase of 12.6% (α/β=10 Gy) and 9.7% (α/β=1.5 Gy) is observed at 10% Iso-toxic NTCP. The entire analysis is performed with the software ‘RadOpt’ which was written as part of this PhD work and is described in detail in chapter three. ‘RadOpt’ will be made available for other researchers to perform similar cohort comparison analyses (to compare changes in parameters, regimens, etcetera as demonstrated in this thesis). In chapter 4, the effect of patient-specific radiosensitivity information on treatment optimization & strategy selection is explored. For lung-tumour treatments it is shown that if patients are stratified into 3 or 5 subgroups of tumour radiosensitivity, the average TCP of the cohort would increase by about ~7.5% at 15 fractions (for most patients) after implementing level-2 optimization, compared to the current scenario where patient specific radiosensitivity information is not available. For the prostate cancer cohort, level-2 TCP is improved marginally by ~1-2% at a reduced NTCP (1.7% lower NTCP compared to optimization where such information is unavailable). Further, it is reported that patients at the extreme ends of normally distributed tumour radiosensitivity would benefit significantly from changes in treatment strategies if patient-specific tumour radiosensitivity information is accounted for in treatment optimization. In chapter 5, the superiority of radiobiological-parameter-driven VMAT inverse treatment plans (in terms of TCP, NTCP and standard dose-volume metrics) compared to dose-volume parameter based VMAT treatment plans for 4 patients (2 with lung & 2 with prostate cancer) is demonstrated. The analysis also shows that heterogeneous dose-distribution based planning can yield improved TCP and dose sparing of OARs compared to standard planning approach that aims for a fixed and homogeneous tumour dose deposition. Further, it is observed that employing radiobiological model-based objectives/constraints reduces the risk of cold spots in the tumour and improves planning efficiency as additional ‘dummy structures’ for sparing an OAR (e.g. rectum) would not be required. Chapter 6 introduces a novel method of iso-NTCP conversion of normal-tissue dose-volume metrics (Vxx) from one regimen to the other. The analysis is carried out for two (each) lung and prostate treatment endpoints. We introduce two methods to perform this analysis (graphical & mathematical). The graphical method allows a clinician to find the equivalent Vxx for the new regimen such that the NTCPs of the OAR for the two regimens are equal.

Item Type: Thesis (Doctor of Philosophy)
Divisions: Fac of Science & Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 26 Mar 2019 11:06
Last Modified: 09 Jan 2021 04:30
DOI: 10.17638/03030908
  • Nahum, Prof Alan
  • Baker, Dr Colin