Wouter van Elmpt

A literature review of electronic portal imaging for radiotherapy dosimetry

Electronic portal imaging devices (EPIDs) have been the preferred tools for verification of patient positioning for radiotherapy in recent decades. Since EPID images contain dose information, many groups have investigated their use for radiotherapy dose measurement. With the introduction of the amorphous-silicon EPIDs, the interest in EPID dosimetry has been accelerated because of the favourable characteristics such as fast image acquisition, high resolution, digital format, and potential for in vivo measurements and 3D dose verification. As a result, the number of publications dealing with EPID dosimetry has increased considerably over the past approximately 15 years. The purpose of this paper was to review the information provided in these publications. Information available in the literature included dosimetric characteristics and calibration procedures of various types of EPIDs, strategies to use EPIDs for dose verification, clinical approaches to EPID dosimetry, ranging from point dose to full 3D dose distribution verification, and current clinical experience. Quality control of a linear accelerator, pre-treatment dose verification and in vivo dosimetry using EPIDs are now routinely used in a growing number of clinics. The use of EPIDs for dosimetry purposes has matured and is now a reliable and accurate dose verification method that can be used in a large number of situations. Methods to integrate 3D in vivo dosimetry and image-guided radiotherapy (IGRT) procedures, such as the use of kV or MV cone-beam CT, are under development. It has been shown that EPID dosimetry can play an integral role in the total chain of verification procedures that are implemented in a radiotherapy department. It provides a safety net for simple to advanced treatments, as well as a full account of the dose delivered. Despite these favourable characteristics and the vast range of publications on the subject, there is still a lack of commercially available solutions for EPID dosimetry. As strategies evolve and commercial products become available, EPID dosimetry has the potential to become an accurate and efficient means of large-scale patient-specific IMRT dose verification for any radiotherapy department.

Predicting outcomes in radiation oncology —multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome—including survival, recurrence patterns and toxicity—in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

The PET-boost randomised phase II dose-escalation trial in non-small cell lung cancer

PURPOSE The local site of relapse in non-small cell lung cancer (NSCLC) is primarily located in the high FDG uptake region of the primary tumour prior to treatment. A phase II PET-boost trial (NCT01024829) randomises patients between dose-escalation of the entire primary tumour (arm A) or to the high FDG uptake region inside the primary tumour (>50% SUV(max)) (arm B), whilst giving 66 Gy in 24 fractions to involved lymph nodes. We analysed the planning results of the first 20 patients for which both arms A and B were planned. METHODS Boost dose levels were escalated up to predefined normal tissue constraints with an equal mean lung dose in both arms. This also forces an equal mean PTV dose in both arms, hence testing pure dose-redistribution. Actual delivered treatment plans from the ongoing clinical trial were analysed. Patients were randomised between arms A and B if dose-escalation to the primary tumour in arm A of at least 72 Gy in 24 fractions could be safely planned. RESULTS 15/20 patients could be escalated to at least 72 Gy. Average prescribed fraction dose was 3.27±0.31 Gy [3.01-4.28 Gy] and 3.63±0.54 Gy [3.20-5.40 Gy] for arms A and B, respectively. Average mean total dose inside the PTV of the primary tumour was comparable: 77.3±7.9 Gy vs. 77.5±10.1 Gy. For the boost region dose levels of on average 86.9±14.9 Gy were reached. No significant dose differences between both arms were observed for the organs at risk. Most frequent observed dose-limiting constraints were the mediastinal structures (13/15 and 14/15 for arms A and B, respectively), and the brachial plexus (3/15 for both arms). CONCLUSION Dose-escalation using an integrated boost could be achieved to the primary tumour or high FDG uptake regions whilst keeping the pre-defined dose constraints.

Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.

Abstract Purpose. Besides basic measurements as maximum standardized uptake value (SUV)max or SUVmean derived from 18F-FDG positron emission tomography (PET) scans, more advanced quantitative imaging features (i.e. “Radiomics” features) are increasingly investigated for treatment monitoring, outcome prediction, or as potential biomarkers. With these prospected applications of Radiomics features, it is a requisite that they provide robust and reliable measurements. The aim of our study was therefore to perform an integrated stability analysis of a large number of PET-derived features in non-small cell lung carcinoma (NSCLC), based on both a test-retest and an inter-observer setup. Methods. Eleven NSCLC patients were included in the test-retest cohort. Patients underwent repeated PET imaging within a one day interval, before any treatment was delivered. Lesions were delineated by applying a threshold of 50% of the maximum uptake value within the tumor. Twenty-three NSCLC patients were included in the inter-observer cohort. Patients underwent a diagnostic whole body PET-computed tomography (CT). Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Delineation was performed independently by five observers, blinded to each other. Fifteen first order statistics, 39 descriptors of intensity volume histograms, eight geometric features and 44 textural features were extracted. For every feature, test-retest and inter-observer stability was assessed with the intra-class correlation coefficient (ICC) and the coefficient of variability, normalized to mean and range. Similarity between test-retest and inter-observer stability rankings of features was assessed with Spearmans rank correlation coefficient. Results. Results showed that the majority of assessed features had both a high test-retest (71%) and inter-observer (91%) stability in terms of their ICC. Overall, features more stable in repeated PET imaging were also found to be more robust against inter-observer variability. Conclusion. Results suggest that further research of quantitative imaging features is warranted with respect to more advanced applications of PET imaging as being used for treatment monitoring, outcome prediction or imaging biomarkers.

‘Rapid Learning health care in oncology’ – An approach towards decision support systems enabling customised radiotherapy’ ☆ ☆☆

PURPOSE An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. MATERIAL AND RESULTS Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. CONCLUSION Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Optimal gating compared to 3D and 4D PET reconstruction for characterization of lung tumours

PurposeWe investigated the added value of a new respiratory amplitude-based PET reconstruction method called optimal gating (OG) with the aim of providing accurate image quantification in lung cancer.MethodsFDG-PET imaging was performed in 26 lung cancer patients during free breathing using a 24-min list-mode acquisition on a PET/CT scanner. The data were reconstructed using three methods: standard 3D PET, respiratory-correlated 4D PET using a phase-binning algorithm, and OG. These datasets were compared in terms of the maximum SUV (SUVmax) in the primary tumour (main endpoint), noise characteristics, and volumes using thresholded regions of SUV 2.5 and 40% of the SUVmax.ResultsSUVmax values from the 4D method (13.7 ± 5.6) and the OG method (14.1 ± 6.5) were higher (4.9 ± 4.8%, p < 0.001 and 6.9 ± 8.8%, p < 0.001, respectively) than that from the 3D method (13.1 ± 5.4). SUVmax did not differ between the 4D and OG methods (2.0 ± 8.4%, p = NS). Absolute and relative threshold volumes did not differ between methods, except for the 40% SUVmax volume in which the value from the 3D method was lower than that from the 4D method (−5.3 ± 7.1%, p = 0.007). The OG method exhibited less noise than the 4D method. Variations in volumes and SUVmax of up to 40% and 27%, respectively, of the individual gates of the 4D method were also observed.ConclusionThe maximum SUVs from the OG and 4D methods were comparable and significantly higher than that from the 3D method, yet the OG method was visibly less noisy than the 4D method. Based on the better quantification of the maximum and the less noisy appearance, we conclude that OG PET is a better alternative to both 3D PET, which suffers from breathing averaging, and the noisy images of a 4D PET.

Response Assessment Using 18F-FDG PET Early in the Course of Radiotherapy Correlates with Survival in Advanced-Stage Non–Small Cell Lung Cancer

This study investigated the possibility of early response assessment based on 18F-FDG uptake during radiotherapy with respect to overall survival in patients with non–small cell lung cancer. Methods: 18F-FDG PET/CT was performed before radiotherapy and was repeated in the second week of radiotherapy for 34 consecutive lung cancer patients. The CT volume and standardized uptake value (SUV) parameters of the primary tumor were quantified at both time points. Changes in volume and SUV parameters correlated with 2-y overall survival. Results: The average change in mean SUV in the primary tumor of patients with a 2-y survival was a decrease by 20% ± 21%—significantly different (P < 0.007) from nonsurvivors, who had an increase by 2% ± 22%. A sensitivity and specificity of 63% and 93%, respectively, to separate the 2 groups was reached for a decrease in mean SUV of 15%. Survival curves were significantly different using this cutoff (P = 0.001). The hazard ratio for a 1% decrease in mean SUV was 1.032 (95% confidence interval, 1.010–1.055). Changes in tumor volume defined on CT did not correlate with overall survival. Conclusion: The use of repeated 18F-FDG PET to assess treatment response early during radiotherapy is possible in patients undergoing radiotherapy or sequential or concurrent chemoradiotherapy. A decrease in 18F-FDG uptake by the primary tumor correlates with higher long-term overall survival.

Is high-dose stereotactic body radiotherapy (SBRT) for stage I non-small cell lung cancer (NSCLC) overkill? A systematic review

BACKGROUND AND PURPOSE For stereotactic body radiotherapy (SBRT), typically a scheme of 60 Gy in 3-8 fractions is applied, producing local tumour control rates around 90%. The dose specification is in one point only and ignores possible underdosages at the edge of the planning target volume (PTV). We investigated the doses at the edge of the PTV and correlated this with local tumour control with the aim to shed light on the radiation dose needed to eradicate stage I NSCLC. MATERIALS AND METHODS Published data on the freedom from local progression (FFLP) data from SBRT and accelerated high-dose conventional radiotherapy series for stage I NSCLC with a follow up of at least 30 months were included. The EQD(2,T) was calculated from the dose at the periphery of the PTV. RESULTS Fifteen studies for SBRT (1076 patients) showed a median FFLP of 88.0±10.4% with a median EQD(2,T) of 76.9±17.4 Gy. The median FFLP was 87.6±6.0% for the accelerated schedules with an EQD(2,T) of 86.9±39.1 Gy, respectively. No significant relation was found between FFLP and the EQD(2,T) (p=0.23). CONCLUSIONS Several fractionated and accelerated schedules with equal biological doses achieve the same tumour control rates as SBRT. Lower, but more uniform doses to the whole PTV may be sufficient to achieve similar control rates, with the possibility to deliver SBRT in adapted schedules, beneficial to centrally located tumours in the vicinity of critical structures like the oesophagus and great vessels.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.