The impact of organ motion and the appliance of mitigation strategies on the effectiveness of hypoxia-guided proton therapy for non-small cell lung cancer.

BACKGROUND AND PURPOSE: To investigate the impact of organ motion on hypoxia-guided proton therapy treatments for non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: Hypoxia PET and 4D imaging data of six NSCLC patients were used to simulate hypoxia-guided proton therapy with different motion mitigation strategies including rescanning, breath-hold, respiratory gating and tumour tracking. Motion-induced dose degradation was estimated for treatment plans with dose painting of hypoxic tumour sub-volumes at escalated dose levels. Tumour control probability (TCP) and dosimetry indices were assessed to weigh the clinical benefit of dose escalation and motion mitigation. In addition, the difference in normal tissue complication probability (NTCP) between escalated proton and photon VMAT treatments has been assessed. RESULTS: Motion-induced dose degradation was found for target coverage (CTV V95% up to -4%) and quality of the dose-escalation-by-contour (QRMS up to 6%) as a function of motion amplitude and amount of dose escalation. The TCP benefit coming from dose escalation (+4-13%) outweighs the motion-induced losses (<2%). Significant average NTCP reductions of dose-escalated proton plans were found for lungs (-14%), oesophagus (-10%) and heart (-16%) compared to conventional VMAT plans. The best plan dosimetry was obtained with breath hold and respiratory gating with rescanning. CONCLUSION: NSCLC affected by hypoxia appears to be a prime target for proton therapy which, by dose-escalation, allows to mitigate hypoxia-induced radio-resistance despite the sensitivity to organ motion. Furthermore, substantial reduction in normal tissue toxicity can be expected compared to conventional VMAT. Accessibility and standardization of hypoxia imaging and clinical trials are necessary to confirm these findings in a clinical setting.

Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures.

BACKGROUND: Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS: A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS: A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION: The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.

[18F]-HX4 PET/CT hypoxia in patients with squamous cell carcinoma of the head and neck treated with chemoradiotherapy: Prognostic results from two prospective trials.

INTRODUCTION: The presence of hypoxia in head-and-neck squamous cell carcinoma is a negative prognostic factor. PET imaging with [18F] HX4 can be used to visualize hypoxia, but it is currently unknown how this correlates with prognosis. We investigated the prognostic value of [18F] HX4 PET imaging in patients treated with definitive radio(chemo)therapy (RTx). MATERIALS AND METHODS: We analyzed 34 patients included in two prospective clinical trials (NCT01347281, NCT01504815). Static [18F] HX4 PET-CT images were collected, both pre-treatment (median 4 days before start RTx, range 1-16), as well as during RTx (median 13 days after start RTx, range 3-17 days). Static uptake at both time points (n = 33 pretreatment, n = 28 during RTx) and measured changes in hypoxic fraction (HF) and hypoxic volume (HV) (n = 27 with 2 time points) were analyzed. Univariate cox analyses were done for local progression free survival (PFS) and overall survival (OS) at both timepoints. Change in uptake was analyzed by comparing outcome with Kaplan-Meier curves and log-rank test between patients with increased and decreased/stable hypoxia, similarly between patients with and without residual hypoxia (rHV = ratio week 2/baseline HV with cutoff 0.2). Voxelwise Spearman correlation coefficients were calculated between normalized [18F] HX4 PET uptake at baseline and week 2. RESULTS: Analyses of static images showed no prognostic value for [18F] HX4 uptake. Analysis of dynamic changes showed that both OS and local PFS were significantly shorter (log-rank P < 0.05) in patients with an increase in HV during RTx and OS was significantly shorter in patients with rHV, with no correlation to HPV-status. The voxel-based correlation to evaluate spatial distribution yielded a median Spearman correlation coefficient of 0.45 (range 0.11-0.65). CONCLUSION: The change of [18F] HX4 uptake measured on [18F] HX4 PET early during treatment can be considered for implementation in predictive models. With these models patients with a worse prognosis can be selected for treatment intensification.

Nitroglycerin as a radiosensitizer in non-small cell lung cancer: Results of a prospective imaging-based phase II trial.

BACKGROUND: Nitroglycerin is proposed as an agent to reduce tumour hypoxia by improving tumour perfusion. We investigated the potential of nitroglycerin as a radio-sensitizer in non-small cell lung cancer (NSCLC) and the potential of functional imaging for patient selection. MATERIAL AND METHODS: Trial NCT01210378 is a single arm phase II trial, designed to detect 15% improvement in 2-year overall survival (primary endpoint) in stage IB-IV NSCLC patients treated with radical (chemo-) radiotherapy and a Transiderm-Nitro 5 patch during radiotherapy. Patients underwent dynamic contrast-enhanced CTs (DCE-CT) and HX4 (hypoxia) PET/CTs before and after nitroglycerin. Secondary endpoints were progression-free survival, toxicity and the prognostic value of tumour perfusion/hypoxia at baseline and after nitroglycerin. RESULTS: The trial stopped after a futility analysis after 42 patients. At median follow-up of 41 months, two-year and median OS were 58% (95% CI: 44-78%) and 38 months (95% CI: 22-54 months), respectively. Nitroglycerin could not reduce tumour hypoxia. DCE-CT parameters did not correlate with OS, whereas hypoxic tumours had a worse OS (p = 0.029). Changes in high-uptake fraction of HX4 and tumour blood flow were negatively correlated (r = -0.650, p = 0.022). The heterogeneity in treatment modalities and patient characteristics combined with a small sample size made further subgroup analysis of survival results impossible. Toxicity related to nitroglyerin was limited to headache (17%) and hypotension (2.4%). CONCLUSION: Nitroglycerin did not improve OS of NSCLC patients treated with (chemo-)radiotherapy. A general ability of nitroglycerin to reduce hypoxia was not shown.

Impact of SBRT fractionation in hypoxia dose painting - Accounting for heterogeneous and dynamic tumor oxygenation.

PURPOSE: Tumor hypoxia, often found in nonsmall cell lung cancer (NSCLC), implies an increased resistance to radiotherapy. Pretreatment assessment of tumor oxygenation is, therefore, warranted in these patients, as functional imaging of hypoxia could be used as a basis for dose painting. This study aimed at investigating the feasibility of using a method for calculating the dose required in hypoxic subvolumes segmented on 18 F-HX4 positron emission tomography (PET) imaging of NSCLC. METHODS: Positron emission tomography imaging data based on the hypoxia tracer 18 F-HX4 of 19 NSCLC patients were included in the study. Normalized tracer uptake was converted to oxygen partial pressure (pO2 ) and hypoxic target volumes (HTVs) were segmented using a threshold of 10 mmHg. Uniform doses required to overcome the hypoxic resistance in the target volumes were calculated based on a previously proposed method taking into account the effect of interfraction reoxygenation, for fractionation schedules ranging from extremely hypofractionated stereotactic body radiotherapy (SBRT) to conventionally fractionated radiotherapy. RESULTS: Gross target volumes ranged between 6.2 and 859.6 cm3 , and the hypoxic fraction < 10 mmHg between 1.2% and 72.4%. The calculated doses for overcoming the resistance of cells in the HTVs were comparable to those currently prescribed in clinical practice as well as those previously tested in feasibility studies on dose escalation in NSCLC. Depending on the size of the HTV and the distribution of pO2 , HTV doses were calculated as 43.6-48.4 Gy for a three-fraction schedule, 51.7-57.6 Gy for five fractions, and 59.5-66.4 Gy for eight fractions. For patients in whom the HTV pO2 distribution was more favorable, a lower dose was required despite a bigger volume. Tumor control probability was lower for single-fraction schedules, while higher levels of tumor control probability were found for schedules employing several fractions. CONCLUSIONS: The method to account for heterogeneous and dynamic hypoxia in target volume segmentation and dose prescription based on 18 F-HX4-PET imaging appears feasible in NSCLC patients. The distribution of oxygen partial pressure within HTV could impact the required prescribed dose more than the size of the volume.

Decision Support Systems in Oncology.

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data-clinical, imaging, biologic, genetic, cost-to produce validated predictive models. DSSs compare the personalized probable outcomes-toxicity, tumor control, quality of life, cost effectiveness-of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders-clinicians, medical directors, medical insurers, patient advocacy groups-and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

Non-linear conversion of HX4 uptake for automatic segmentation of hypoxic volumes and dose prescription.

BACKGROUND: Tumour hypoxia is associated with increased radioresistance and poor response to radiotherapy. Pre-treatment assessment of tumour oxygenation could therefore give the possibility to tailor the treatment by calculating the required boost dose needed to overcome the increased radioresistance in hypoxic tumours. This study concerned the derivation of a non-linear conversion function between the uptake of the hypoxia-PET tracer 18F-HX4 and oxygen partial pressure (pO2). MATERIAL AND METHODS: Building on previous experience with FMISO including experimental data on tracer uptake and pO2, tracer-specific model parameters were derived for converting the normalised HX4-uptake at the optimal imaging time point to pO2. The conversion function was implemented in a Python-based computational platform utilising the scripting and the registration modules of the treatment planning system RayStation. Subsequently, the conversion function was applied to determine the pO2 in eight non-small-cell lung cancer (NSCLC) patients imaged with HX4-PET before the start of radiotherapy. Automatic segmentation of hypoxic target volumes (HTVs) was then performed using thresholds around 10 mmHg. The HTVs were compared to sub-volumes segmented based on a tumour-to-blood ratio (TBR) of 1.4 using the aortic arch as the reference oxygenated region. The boost dose required to achieve 95% local control was then calculated based on the calibrated levels of hypoxia, assuming inter-fraction reoxygenation due to changes in acute hypoxia but no overall improvement of the oxygenation status. RESULTS: Using the developed conversion tool, HTVs could be obtained using pO2 a threshold of 10 mmHg which were in agreement with the TBR segmentation. The dose levels required to the HTVs to achieve local control were feasible, being around 70-80 Gy in 24 fractions. CONCLUSIONS: Non-linear conversion of tracer uptake to pO2 in NSCLC imaged with HX4-PET allows a quantitative determination of the dose-boost needed to achieve a high probability of local control.

Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer.

BACKGROUND AND PURPOSE: We aimed to identify tumour subregions with characteristic phenotypes based on pre-treatment multi-parametric functional imaging and correlate these subregions to treatment outcome. The subregions were created using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4-PET/CT) and tumour vasculature (DCE-CT). MATERIALS AND METHODS: 36 non-small cell lung cancer (NSCLC) patients underwent functional imaging prior to radical radiotherapy. Kinetic analysis was performed on DCE-CT scans to acquire blood flow (BF) and volume (BV) maps. HX4-PET/CT and DCE-CT scans were non-rigidly co-registered to the planning FDG-PET/CT. Two clustering steps were performed on multi-parametric images: first to segment each tumour into homogeneous subregions (i.e. supervoxels) and second to group the supervoxels of all tumours into phenotypic clusters. Patients were split based on the absolute or relative volume of supervoxels in each cluster; overall survival was compared using a log-rank test. RESULTS: Unsupervised clustering of supervoxels yielded four independent clusters. One cluster (high hypoxia, high FDG, intermediate BF/BV) related to a high-risk tumour type: patients assigned to this cluster had significantly worse survival compared to patients not in this cluster (p = 0.035). CONCLUSIONS: We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.

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.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

BACKGROUND: Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). MATERIAL AND METHODS: For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUVmean) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. RESULTS: Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm3) by the best performing model. CONCLUSIONS: We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Decision support systems for personalized and participative radiation oncology.

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

Quantitative assessment of Zirconium-89 labeled cetuximab using PET/CT imaging in patients with advanced head and neck cancer: a theragnostic approach.

Biomarkers predicting treatment response to the monoclonal antibody cetuximab in locally advanced head and neck squamous cell carcinomas (LAHNSCC) are lacking. We hypothesize that tumor accessibility is an important factor in treatment success of the EGFR targeting drug. We quantified uptake of cetuximab labeled with Zirconium-89 (89Zr) using PET/CT imaging.Seventeen patients with stage III-IV LAHNSCC received a loading dose unlabeled cetuximab, followed by 10 mg 54.5±9.6 MBq 89Zr-cetuximab. PET/CT images were acquired either 3 and 6 or 4 and 7 days post-injection. 89Zr-cetuximab uptake was quantified using standardized uptake value (SUV) and tumor-to-background ratio (TBR), and correlated to EGFR immunohistochemistry. TBR was compared between scan days to determine optimal timing.Uptake of 89Zr-cetuximab varied between patients (day 6-7: SUVpeak range 2.5-6.2). TBR increased significantly (49±28%, p < 0.01) between first (1.1±0.3) and second scan (1.7±0.6). Between groups with a low and high EGFR expression a significant difference in SUVmean (2.1 versus 3.0) and SUVpeak (3.2 versus 4.7) was found, however, not in TBR. Data is available at www.cancerdata.org (DOI: 10.17195/candat.2016.11.1).In conclusion, 89Zr-cetuximab PET imaging shows large inter-patient variety in LAHNSCC and provides additional information over FDG-PET and EGFR expression. Validation of the predictive value is recommended with scans acquired 6-7 days post-injection.

PET imaging of zirconium-89 labelled cetuximab: A phase I trial in patients with head and neck and lung cancer.

BACKGROUND AND PURPOSE: PET imaging of cetuximab uptake may help selecting cancer patients with the highest chance of benefit. The aim of this phase I trial was to determine the safety of the tracer 89Zr-cetuximab and to assess tumour uptake. METHODS: Two dose schedules were used; two consecutive doses of 60MBq 89Zr-cetuximab or a single dose of 120MBq, both preceded by 400mg/m2 of unlabelled cetuximab. Toxicity (CTCAE 3.0) was scored twice weekly. PET-CT scans were acquired on days 4, 5 and 6 (step 1) or 5, 6, 7 (step 2). Because tumour uptake could not be assessed satisfactorily, a third step was added including EGFR overexpressing tumours. RESULTS: Nine patients were included (6 NSCLC; 3 HNC). No additional toxicity was associated with administration of 89Zr-cetuximab compared to standard cetuximab. A tumour to blood ratio (TBR)>1 was observed in all but one patient, with a maximum of 4.56. TBR was not different between dose schedules. There was a trend for higher TBR at intervals>5days after injection. CONCLUSIONS: Both presented 89Zr-cetuximab administration schedules are safe. The recommended dose for future trials is 60MBq, with a minimum time interval for scanning of 6days.

Multiparametric imaging of patient and tumour heterogeneity in non-small-cell lung cancer: quantification of tumour hypoxia, metabolism and perfusion.

PURPOSE: Multiple imaging techniques are nowadays available for clinical in-vivo visualization of tumour biology. FDG PET/CT identifies increased tumour metabolism, hypoxia PET visualizes tumour oxygenation and dynamic contrast-enhanced (DCE) CT characterizes vasculature and morphology. We explored the relationships among these biological features in patients with non-small-cell lung cancer (NSCLC) at both the patient level and the tumour subvolume level. METHODS: A group of 14 NSCLC patients from two ongoing clinical trials (NCT01024829 and NCT01210378) were scanned using FDG PET/CT, HX4 PET/CT and DCE CT prior to chemoradiotherapy. Standardized uptake values (SUV) in the primary tumour were calculated for the FDG and hypoxia HX4 PET/CT scans. For hypoxia imaging, the hypoxic volume, fraction and tumour-to-blood ratio (TBR) were also defined. Blood flow and blood volume were obtained from DCE CT imaging. A tumour subvolume analysis was used to quantify the spatial overlap between subvolumes. RESULTS: At the patient level, negative correlations were observed between blood flow and the hypoxia parameters (TBR >1.2): hypoxic volume (-0.65, p = 0.014), hypoxic fraction (-0.60, p = 0.025) and TBR (-0.56, p = 0.042). At the tumour subvolume level, hypoxic and metabolically active subvolumes showed an overlap of 53 ± 36 %. Overlap between hypoxic sub-volumes and those with high blood flow and blood volume was smaller: 15 ± 17 % and 28 ± 28 %, respectively. Half of the patients showed a spatial mismatch (overlap <5 %) between increased blood flow and hypoxia. CONCLUSION: The biological imaging features defined in NSCLC tumours showed large interpatient and intratumour variability. There was overlap between hypoxic and metabolically active subvolumes in the majority of tumours, there was spatial mismatch between regions with high blood flow and those with increased hypoxia.

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine.

BACKGROUND: Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided. AIM: The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm. DISCUSSION: Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

PET-based dose painting in non-small cell lung cancer: Comparing uniform dose escalation with boosting hypoxic and metabolically active sub-volumes.

BACKGROUND AND PURPOSE: We compared two imaging biomarkers for dose-escalation in patients with advanced non-small cell lung cancer (NSCLC). Treatment plans boosting metabolically active sub-volumes defined by FDG-PET or hypoxic sub-volumes defined by HX4-PET were compared with boosting the entire tumour. MATERIALS AND METHODS: Ten NSCLC patients underwent FDG- and HX4-PET/CT scans prior to radiotherapy. Three isotoxic dose-escalation plans were compared per patient: plan A, boosting the primary tumour (PTVprim); plan B, boosting sub-volume with FDG >50% SUVmax (PTVFDG); plan C, boosting hypoxic volume with HX4 tumour-to-background >1.4 (PTVHX4). RESULTS: Average boost volumes were 507 ± 466 cm(3) for PTVprim, 173 ± 127 cm(3) for PTVFDG and 114 ± 73 cm(3) for PTVHX4. The smaller PTVHX4 overlapped on average 87 ± 16% with PTVFDG. Prescribed dose was escalated to 87 ± 10 Gy for PTVprim, 107 ± 20 Gy for PTVFDG, and 117 ± 15 Gy for PTVHX4, with comparable doses to the relevant organs-at-risk (OAR). Treatment plans are available online (https://www.cancerdata.org/10.1016/j.radonc.2015.07.013). CONCLUSIONS: Dose escalation based on metabolic sub-volumes, hypoxic sub-volumes and the entire tumour is feasible. Highest dose was achieved for hypoxia plans, without increasing dose to OAR. For most patients, boosting the metabolic sub-volume also resulted in boosting the hypoxic volume, although to a lower dose, but not vice versa.

In vivo quantification of hypoxic and metabolic status of NSCLC tumors using [18F]HX4 and [18F]FDG-PET/CT imaging.

PURPOSE: Increased tumor metabolism and hypoxia are related to poor prognosis in solid tumors, including non-small cell lung cancer (NSCLC). PET imaging is a noninvasive technique that is frequently used to visualize and quantify tumor metabolism and hypoxia. The aim of this study was to perform an extensive comparison of tumor metabolism using 2[(18)F]fluoro-2-deoxy-d-glucose (FDG)-PET and hypoxia using HX4-PET imaging. EXPERIMENTAL DESIGN: FDG- and HX4-PET/CT images of 25 patients with NSCLC were coregistered. At a global tumor level, HX4 and FDG parameters were extracted from the gross tumor volume (GTV). The HX4 high-fraction (HX4-HF) and HX4 high-volume (HX4-HV) were defined using a tumor-to-blood ratio > 1.4. For FDG high-fraction (FDG-HF) and FDG high-volume (FDG-HV), a standardized uptake value (SUV) > 50% of SUVmax was used. We evaluated the spatial correlation between HX4 and FDG uptake within the tumor, to quantify the (mis)match between volumes with a high FDG and high HX4 uptake. RESULTS: At a tumor level, significant correlations were observed between FDG and HX4 parameters. For the primary GTV, the HX4-HF was three times smaller compared with the FDG-HF. In 53% of the primary lesions, less than 1 cm(3) of the HX4-HV was outside the FDG-HV; for 37%, this volume was 1.9 to 12 cm(3). Remarkably, a distinct uptake pattern was observed in 11%, with large hypoxic volumes localized outside the FDG-HV. CONCLUSION: Hypoxic tumor volumes are smaller than metabolic active volumes. Approximately half of the lesions showed a good spatial correlation between the PET tracers. In the other cases, a (partial) mismatch was observed. The addition of HX4-PET imaging has the potential to individualize patient treatment.

High-dose-rate prostate brachytherapy based on registered transrectal ultrasound and in-room cone-beam CT images.

PURPOSE: To present a high-dose-rate (HDR) brachytherapy procedure for prostate cancer using transrectal ultrasound (TRUS) to contour the regions of interest and registered in-room cone-beam CT (CBCT) images for needle reconstruction. To characterize the registration uncertainties between the two imaging modalities and explore the possibility of performing the procedure solely on TRUS. METHODS AND MATERIALS: Patients were treated with a TRUS/CBCT-based HDR brachytherapy procedure. For 100 patients, dosimetric results were analyzed. For 40 patients, registration uncertainties were examined by determining differences in fiducial marker positions on TRUS and registered CBCT. The accuracy of needle reconstruction on TRUS was investigated by determining the position differences of needle tips on TRUS and CBCT. The dosimetric impact of reregistration and needle reconstruction on TRUS only was studied for 8 patients. RESULTS: The average prostate V100 was 97.8%, urethra D10 was 116.3%, and rectum D1 cc was 66.4% of the prescribed dose. For 85% of the patients, registration inaccuracies were within 3 mm. Large differences were found between needle tips on TRUS and CBCT, especially in cranial-caudal direction, with a maximum of 10.4 mm. Reregistration resulted in a maximum V100 reduction of 0.9%, whereas needle reconstruction on TRUS only gave a maximum reduction of 9.4%. CONCLUSIONS: HDR prostate brachytherapy based on TRUS combined with CBCT is an accurate method. Registration uncertainties, and consequently dosimetric inaccuracies, are small compared with the uncertainties of performing the procedure solely based on static TRUS images. CBCT imaging is a requisite in our current procedure.