Predicting the risk of neurocognitive decline after brain irradiation in adult patients with a primary brain tumor.

BACKGROUND: Deterioration of neurocognitive function in adult patients with a primary brain tumor is the most concerning side effect of radiotherapy. This study was aimed to develop and evaluate Normal-Tissue Complication Probability (NTCP) models using clinical and dose-volume measures for 6-month, 1-year and 2-year Neurocognitive Decline (ND) post-radiotherapy. METHODS: A total of 219 patients with a primary brain tumor treated with radical photon and/or proton radiotherapy (RT) between 2019 and 2022 were included. Controlled Oral Word Association (COWA) test, Hopkins Verbal Learning Test-Revised (HVLTR) and Trail Making Test (TMT) were used to objectively measure ND. A comprehensive set of potential clinical and dose-volume measures on several brain structures were considered for statistical modelling. Clinical, dose-volume and combined models were constructed and internally tested in terms of discrimination (Area Under the Curve, AUC), calibration (Mean Absolute Error, MAE) and net benefit. RESULTS: 50%, 44.5% and 42.7% of the patients developed ND at 6-month, 1-year and 2-year timepoints, respectively. Following predictors were included in the combined model for 6-month ND: age at radiotherapy>56 years (OR=5.71), overweight (OR=0.49), obesity (OR=0.35), chemotherapy (OR=2.23), brain V20Gy≥20% (OR=3.53), brainstem volume≥26cc (OR=0.39) and hypothalamus volume≥0.5cc (OR=0.4). Decision curve analysis showed that the combined models had the highest net benefits at 6-month (AUC=0.79, MAE=0.021), 1-year (AUC=0.72, MAE=0.027) and 2-year (AUC=0.69, MAE=0.038) timepoints. CONCLUSION: The proposed NTCP models use easy-to-obtain predictors to identify patients at high-risk of ND after brain RT. These models can potentially provide a base for RT-related decisions and post-therapy neurocognitive rehabilitation interventions.

The existence of cranial bone flap displacement during brain radiotherapy.

This retrospective study examined bone flap displacement during radiotherapy in 25 post-operative brain tumour patients. Though never exceeding 2.5 mm, the sheer frequency of displacement highlights the need for future research on larger populations to validate its presence and assess the potential clinical impact on planning tumour volume margins.

Anatomical changes in resection cavity during brain radiotherapy.

BACKGROUND AND PURPOSE: Brain tumors are in general treated with a maximal safe resection followed by radiotherapy of remaining tumor including the resection cavity (RC) and chemotherapy. Anatomical changes of the RC during radiotherapy can have impact on the coverage of the target volume. The aim of the current study was to quantify the potential changes of the RC and to identify risk factors for RC changes. MATERIALS AND METHODS: Sixteen patients treated with pencil beam scanning proton therapy between October 2019 and April 2020 were retrospectively analyzed. The RC was delineated on pre-treatment computed tomography (CT) and magnetic resonance imaging, and weekly CT-scans during treatment. Isotropic expansions were applied to the pre-treatment RC (1-5 mm). The percentage of volume of the RC during treatment within the expanded pre-treatment volumes was quantified. Potential risk factors (volume of RC, time interval surgery-radiotherapy and relationship of RC to the ventricles) were evaluated using Spearman's rank correlation coefficient. RESULTS: The average variation in relative RC volume during treatment was 26.1% (SD 34.6%). An expansion of 4 mm was required to cover > 95% of the RC volume in > 90% of patients. There was a significant relationship between the absolute volume of the pre-treatment RC and the volume changes during treatment (Spearman's ρ = - 0.644; p = 0.007). CONCLUSION: RCs are dynamic after surgery. Potentially, an additional margin in brain cancer patients with an RC should be considered, to avoid insufficient target coverage. Future research on local recurrence patterns is recommended.

Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology.

PURPOSE: Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas. METHODS: Image and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests. RESULTS: CT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88-0.95) and 0.84 (0.77-0.89) respectively, as well as lowest MSHD: 0.27 (0.22-0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔDmax,0.03cc-parameters when splitting the data in ≤ 4 cm and > 4 cm OAR-distance (p < 0.001). CONCLUSION: MR-based DLC and CT-based atlas-contouring enable high-quality segmentation. It was shown that a combination of both CT- and MR-autocontouring models results in the best quality.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Purpose: Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Methods: Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). Results: The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. Conclusion: We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.

Neurocognition in adults with intracranial tumors: does location really matter?

OBJECTIVE: As preservation of cognitive functioning increasingly becomes important in the light of ameliorated survival after intracranial tumor treatments, identification of eloquent brain areas would enable optimization of these treatments. METHODS: This cohort study enrolled adult intracranial tumor patients who received neuropsychological assessments pre-irradiation, estimating processing speed, verbal fluency and memory. Anatomical magnetic resonance imaging scans were used for multivariate voxel-wise lesion-symptom predictions of the test scores (corrected for age, gender, educational level, histological subtype, surgery, and tumor volume). Potential effects of histological and molecular subtype and corresponding WHO grades on the risk of cognitive impairment were investigated using Chi square tests. P-values were adjusted for multiple comparisons (p < .001 and p < .05 for voxel- and cluster-level, resp.). RESULTS: A cohort of 179 intracranial tumor patients was included [aged 19-85 years, median age (SD) = 58.46 (14.62), 50% females]. In this cohort, test-specific impairment was detected in 20-30% of patients. Higher WHO grade was associated with lower processing speed, cognitive flexibility and delayed memory in gliomas, while no acute surgery-effects were found. No grading, nor surgery effects were found in meningiomas. The voxel-wise analyses showed that tumor locations in left temporal areas and right temporo-parietal areas were related to verbal memory and processing speed, respectively. INTERPRETATION: Patients with intracranial tumors affecting the left temporal areas and right temporo-parietal areas might specifically be vulnerable for lower verbal memory and processing speed. These specific patients at-risk might benefit from early-stage interventions. Furthermore, based on future validation studies, imaging-informed surgical and radiotherapy planning could further be improved.

Correlation of reduced temporal muscle thickness and systemic muscle loss in newly diagnosed glioblastoma patients.

PURPOSE: Reduced temporal muscle thickness (TMT) has recently been postulated as a prognostic imaging marker and an objective tool to assess patients frailty in glioblastoma. Our aim is to investigate the correlation of TMT and systemic muscle loss to confirm that TMT is an adequate surrogate marker of sarcopenia in newly diagnosed glioblastoma patients. METHODS: TMT was assessed on preoperative MR-images and skeletal muscle area (SMA) was assessed at the third lumbar vertebra on preoperative abdominal CT-scans. Previous published TMT sex-specific cut-off values were used to classify patients as 'patient at risk of sarcopenia' or 'patient with normal muscle status'. Correlation between TMT and SMA was assessed using Spearman's rank correlation coefficient. RESULTS: Sixteen percent of the 245 included patients were identified as at risk of sarcopenia. The mean SMA of glioblastoma patients at risk of sarcopenia (124.3 cm2, SD 30.8 cm2) was significantly lower than the mean SMA of patients with normal muscle status (146.3 cm2, SD 31.1 cm2, P < .001). We found a moderate association between TMT and SMA in the patients with normal muscle status (Spearman's rho 0.521, P < .001), and a strong association in the patients at risk of sarcopenia (Spearman's rho 0.678, P < .001). CONCLUSION: Our results confirm the use of TMT as a surrogate marker of total body skeletal muscle mass in glioblastoma, especially in frail patients at risk of sarcopenia. TMT can be used to identify patients with muscle loss early in the disease process, which enables the implementation of adequate intervention strategies.

Performance and usability evaluation of a mobile health data capture application in clinical cancer trials follow-up.

Mobile health data capture applications (mHDA's) may improve communication between healthcare providers and patients. However, there is limited literature about the use of mHDA's facilitating clinical trials. In this study, the effectiveness of an application, supporting follow-up visits of cancer trial participants was investigated. Twenty participants were provided with an e-questionnaire via the mHDA. Participants rated the usability of the application as high performing (mean Systems Usability Scale 87 points). The research team rated the mHDA as highly applicable and efficient in preparing visits. Anamnesis, physical examination and agreement on further policy were performed within an average of 31 min.

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review.

Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors. Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to "radiotherapy," "brain," "prediction model," and "neurocognitive impairments." Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool. Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n = 7) and the trail making tests (n = 4) were the most frequent outcome assessment tools. All studies used regression (n = 14 linear, n = 8 logistic, and n = 4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis. Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

Automatic dose verification system for breast radiotherapy: Method validation, contour propagation and DVH parameters evaluation.

PURPOSE: Image guided radiotherapy (IGRT) strategies allow detecting and monitoring anatomical changes during external beam radiotherapy (EBRT). However, assessing the dosimetric impact of anatomical changes is not straightforward. In current IGRT strategies dose volume histograms (DVH) are not available due to lack of contours and dose recalculations on the cone-beam CT (CBCT) scan. This study investigates the feasibility of using automatically calculated DVH parameters in CBCTs using an independent dose calculation engine and propagated contours. METHOD: A prospective study (NCT03385031) of thirty-one breast cancer patients who received additional CBCT imaging (N = 70) was performed. Manual and automatically propagated contours were generated for all CBCTs and an automatic dose recalculation was performed. Differences between planned and CBCT-derived DVH parameters (mean and maximum dose to targets, 95% volume coverage to targets and mean heart dose (MHD)) were calculated using the dose verification system with manual and propagated contours and, in both cases, benchmarked against DVH differences quantified in the TPS using manually contoured CBCTs. RESULTS: Differences in DVH parameters between the TPS and dose verification system with propagated contours were -1.3% to 0.7% (95% CI) for mean dose to the target volume, -0.3 to 0.2 Gy (95% CI) in MHD and -3.9% to 2.9% (95% CI) in target volume coverage. CONCLUSION: The use of an independent fully automatic dose verification system with contour propagation showed to be feasible and sufficiently reliable to recalculate CBCT based DVHs during breast EBRT. Volume coverage parameters, i.e. V95%, proved to be especially sensitive to contouring differences.

Development of explanatory movies for the delineation of new organs at risk in neuro-oncology.

Ten new organs at risk (OARs) were recently introduced in the updated European Particle Therapy Network neurological contouring atlas. Despite the use of the illustrated atlas and descriptive text, interindividual contouring variations may persist. To further facilitate the contouring of these OARs, educational films were developed and published on www.cancerdata.org.

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.

The immunocytokine L19-IL2: An interplay between radiotherapy and long-lasting systemic anti-tumour immune responses.

Recently, we have shown that the administration of the tumour-targeted antibody-based immunocytokine L19-IL2 after radiotherapy (RT) resulted in synergistic anti-tumour effect. Here we show that RT and L19-IL2 can activate a curative abscopal effect, with a long-lasting immunological memory. Ionizing radiation (single dose of 15Gy, 5 × 2Gy or 5 × 5Gy) was delivered to primary C51 colon tumour-bearing immunocompetent mice in combination with L19-IL2 and response of secondary non-irradiated C51 or CT26 colon tumours was evaluated. 15Gy + L19-IL2 triggered a curative (20%) abscopal effect, which was T cell dependent. Moreover, 10Gy + L19-IL2 treated and cured mice were re-injected after 150 days with C51 tumour cells and tumour uptake was assessed. Age-matched controls (matrigel injected mice treated with 10Gy + L19-IL2, mice cured after treatment with surgery + L19-IL2 and mice cured after high dose RT 40Gy + vehicle) were included. Several immunological parameters in blood, tumours, lymph nodes and spleens were investigated. Treatment with 10Gy + L19-IL2 resulted in long-lasting immunological memory, associated with CD44+CD127+ expression on circulating T cells. This combination treatment can induce long-lasting curative abscopal responses, and therefore it has also great potential for treatment of metastatic disease. Preclinical findings have led to the initiation of a phase I clinical trial (NCT02086721) in our institute investigating stereotactic ablative radiotherapy with L19-IL2 in patients with oligometastatic solid tumours.

Three-dimensional dose evaluation in breast cancer patients to define decision criteria for adaptive radiotherapy.

BACKGROUND: Dose-guided adaptive radiation therapy (DGART) is the systematic evaluation and adaptation of the dose delivery during treatment for an individual patient. The aim of this study is to define quantitative action levels for DGART by evaluating changes in 3D dose metrics in breast cancer and correlate them with clinical expert evaluation. MATERIAL AND METHODS: Twenty-three breast cancer treatment plans were evaluated, that were clinically adapted based on institutional IGRT guidelines. Reasons for adaptation were variation in seroma, hematoma, edema, positioning or problems using voluntary deep inspiration breath hold. Sixteen patients received a uniform dose to the breast (clinical target volume 1; CTV1). Six patients were treated with a simultaneous integrated boost to CTV2. The original plan was copied to the CT during treatment (re-CT) or to the stitched cone-beam CT (CBCT). Clinical expert evaluation of the re-calculated dose distribution and extraction of dose-volume histogram (DVH) parameters were performed. The extreme scenarios were evaluated, assuming all treatment fractions were given to the original planning CT (pCT), re-CT or CBCT. Reported results are mean ± SD. RESULTS: DVH results showed a mean dose (Dmean) difference between pCT and re-CT of -0.4 ± 1.4% (CTV1) and -1.4 ± 2.1% (CTV2). The difference in V95% was -2.6 ± 4.4% (CTV1) and -9.8 ± 8.3% (CTV2). Clinical evaluation and DVH evaluation resulted in a recommended adaptation in 17/23 or 16/23 plans, respectively. Applying thresholds on the DVH parameters: Dmean CTV, V95% CTV, Dmax, mean lung dose, volume exceeding 107% (uniform dose) or 90% (SIB) of the prescribed dose enabled the identification of patients with an assumed clinically relevant dose difference, with a sensitivity of 0.89 and specificity of 1.0. Re-calculation on CBCT imaging identified the same plans for adaptation as re-CT imaging. CONCLUSIONS: Clinical expert evaluation can be related to quantitative DVH parameters on re-CT or CBCT imaging to select patients for DGART.

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.

Evaluation of tumour hypoxia during radiotherapy using [18F]HX4 PET imaging and blood biomarkers in patients with head and neck cancer.

BACKGROUND AND PURPOSE: Increased tumour hypoxia is associated with a worse overall survival in patients with head and neck squamous cell carcinoma (HNSCC). The aims of this study were to evaluate treatment-associated changes in [18F]HX4-PET, hypoxia-related blood biomarkers, and their interdependence. MATERIAL AND METHODS: [18F]HX4-PET/CT scans of 20 patients with HNSCC were acquired at baseline and after ±20Gy of radiotherapy. Within the gross-tumour-volumes (GTV; primary and lymph nodes), mean and maximum standardized uptake values, the hypoxic fraction (HF) and volume (HV) were calculated. Also, the changes in spatial uptake pattern were evaluated using [18F]HX4-PET/CT imaging. For all patients, the plasma concentration of CAIX, osteopontin and VEGF was assessed. RESULTS: At baseline, tumour hypoxia was detected in 69 % (22/32) of the GTVs. During therapy, we observed a significant decrease in all image parameters. The HF decreased from 21.7 ± 19.8 % (baseline) to 3.6 ± 10.0 % (during treatment; P < 0.001). Only two patients had a HV > 1 cm3 during treatment, which was located for >98 % within the baseline HV. During treatment, no significant changes in plasma CAIX or VEGF were observed, while osteopontin was increased. CONCLUSIONS: [18F]HX4-PET/CT imaging allows monitoring changes in hypoxia during (chemo)radiotherapy whereas the blood biomarkers were not able to detect a treatment-associated decrease in hypoxia.

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.