The application of positron emission tomography in radiotherapy treatment planning

Positron emission tomography (PET) is a molecular imaging technique that provides a direct and accurate evaluation of tissue function in vivo. PET of the glucose analogue 18F-fluoro-deoxy-glucose, is increasingly in use to aid in gross target volume delineation in radiotherapy treatment planning (RTP) where it shows reduced inter-observer variability. The aim of this thesis was to develop and investigate a new technique for delineating PET-GTV with sufficient accuracy for RTP. A new technique, volume and contrast adjusted thresholding (VCAT), has been developed to automatically determine the optimum threshold value that measures the true volume on PET images. The accuracy was investigated in spherical and irregular lesions in phantoms using both iterative and filtered back-projection reconstructions and different image noise levels. The accuracy of delineation for the irregular lesions was assessed by comparison with CT using the Dice Similarity Coefficient and Euclidean Distance Transformation. A preliminarily investigation of implementing the newly developed technique in patients was carried out. VCAT proved to determine volumes and delineate tumour boundaries on PET/CT well within the acceptable errors for radiotherapy treatment planning irrespective of lesion contrast, image noise level and reconstruction technique.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Chemoradiotherapy of locally-advanced non-small cell lung cancer: Analysis of radiation dose-response, chemotherapy and survival-limiting toxicity effects indicates a low alpha/beta ratio

Purpose To analyse changes in 2-year overall survival (OS2yr) with radiotherapy (RT) dose, dose-per-fraction, treatment duration and chemotherapy use, in data compiled from prospective trials of RT and chemo-RT (CRT) for locally-advanced non-small cell lung cancer (LA-NSCLC). Material and methods OS2yr data was analysed for 6957 patients treated on 68 trial arms (21 RT-only, 27 sequential CRT, 20 concurrent CRT) delivering doses-per-fraction ≤4.0 Gy. An initial model considering dose, dose-per-fraction and RT duration was fitted using maximum-likelihood techniques. Model extensions describing chemotherapy effects and survival-limiting toxicity at high doses were assessed using likelihood-ratio testing, the Akaike Information Criterion (AIC) and cross-validation. Results A model including chemotherapy effects and survival-limiting toxicity described the data significantly better than simpler models (p < 10−14), and had better AIC and cross-validation scores. The fitted α/β ratio for LA-NSCLC was 4.0 Gy (95%CI: 2.8–6.0 Gy), repopulation negated 0.38 (95%CI: 0.31–0.47) Gy EQD2/day beyond day 12 of RT, and concurrent CRT increased the effective tumour EQD2 by 23% (95%CI: 16–31%). For schedules delivered in 2 Gy fractions over 40 days, maximum modelled OS2yr for RT was 52% and 38% for stages IIIA and IIIB NSCLC respectively, rising to 59% and 42% for CRT. These survival rates required 80 and 87 Gy (RT or sequential CRT) and 67 and 73 Gy (concurrent CRT). Modelled OS2yr rates fell at higher doses. Conclusions Fitted dose–response curves indicate that gains of ~10% in OS2yr can be made by escalating RT and sequential CRT beyond 64 Gy, with smaller gains for concurrent CRT. Schedule acceleration achieved via hypofractionation potentially offers an additional 5–10% improvement in OS2yr. Further 10–20% OS2yr gains might be made, according to the model fit, if critical normal structures in which survival-limiting toxicities arise can be identified and selectively spared

Configuration space analysis of common cost functions in radiotherapy beam-weight optimization algorithms

Configuration space analysis of common cost functions in radiotherapy beam-weight optimization algorithms. The successful implementation of downhill search engines in radiotherapy optimization algorithms depends on the absence of local minima in the search space. Such techniques are much faster than stochastic optimization methods but may become trapped in local minima if they exist. A technique known as 'configuration space analysis' was applied to examine the search space of cost functions used in radiotherapy beam-weight optimization algorithms. A downhill-simplex beam-weight optimization algorithm was run repeatedly to produce a frequency distribution of final cost values. By plotting the frequency distribution as a function of final cost, the existence of local minima can be determined. Common cost functions such as the quadratic deviation of dose to the planning target volume (PTV), integral dose to organs-at-risk (OARs), dose-threshold and dose-volume constraints for OARs were studied. Combinations of the cost functions were also considered. The simple cost function terms such as the quadratic PTV dose and integral dose to OAR cost function terms are not susceptible to local minima. In contrast. (lose- threshold and dose-volume OAR constraint cost function terms are able to produce local minima in the example case studied

Gross failure rates and failure modes for a commercial AI‐based auto‐segmentation algorithm in head and neck cancer patients

AbstractPurposeArtificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter‐ and intra‐observer variability. There has been little research investigating gross failure rates and failure modes of such systems.Method50 head and neck (H&amp;N) patient data sets with “gold standard” contours were compared to AI‐generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&amp;N OARs (brainstem, mandible, left and right parotid). An AI‐based commercial system was applied to 500 H&amp;N patients. AI‐generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI‐based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub‐types (setup position, anatomy, image artefacts and unknown).ResultsThere were 24 true failures of the AI‐based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid).ConclusionTrue failures of the AI‐based system were predominantly associated with a non‐standard element within the CT scan. It is likely that these non‐standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI‐based system in the H&amp;N region for the OARs investigated was low (∼1%).</jats:sec

4DCT and VMAT for lung patients with irregular breathing

PurposeIrregular breathing in lung cancer patients is a common contra-indication to 4D computerized tomography (4DCT), which may then limit radiotherapy treatment options. For irregular breathers, we investigated whether 3DCT or 4DCT (1) better represents tumor motion, (2) better represents average tumor densities, and (3) better allows for volumetric modulated arc threarpy (VMAT) plans delivered with acceptable dosimetric accuracy.MethodsTen clinical breathing traces were identified with irregularities in phase and amplitude, and fed to a programmable moving platform incorporating an anthropomorphic lung tumor phantom. 3DCT and 4DCT data resorted by phase (4DCT-P) and amplitude (4DCT-A) were acquired for each trace. Tumors were delineated by Hounsfield unit (HU) thresholding and apparent motion range assessed. HU profiles were extracted from each image and agreement with calculated expected profiles quantified using area-under-curve (AUC) scoring. Clinically representative VMAT plans were created for each image, delivered to the irregularly moving phantom, and measured with a small-volume ion chamber at the tumor center.ResultsMedian difference from expected tumor motion range for 3DCT, 4DCT-P, and 4DCT-A was 2.5 [1.6-3.6] cm, 1.1 [0.1-1.9] cm, and 1.3 [0.4-1.9] cm, respectively (p = 0.005, 4DCT-P vs. 3DCT). Median AUC scores (ideal = 0) for 3DCT, 4DCT-P, and 4DCT-A were 0.25 [0.14-0.49], 0.12 [0.05-0.42], and 0.13 [0.09-0.44], respectively (p = 0.005, 4DCT-P vs. 3DCT). Nine of ten 4DCT-P plans and all 4DCT-A plans measured within 2.5% of expected dose in the treatment planning system (TPS), compared with seven 3DCT plans.ConclusionFor the cases studied tumor motion range and average density was better represented with 4DCT compared with 3DCT, even in the presence of irregular breathing. 4DCT images allowed for delivery of VMAT plans with acceptable dosimetric accuracy. No significant differences were detected between phase and amplitude resorting. In combination with 4D cone beam imaging at treatment, our findings have given us confidence to introduce 4DCT and VMAT for lung radiotherapy patients with irregular breathing