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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. 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. 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 ( = 7) and the trail making tests ( = 4) were the most frequent outcome assessment tools. All studies used regression ( = 14 linear, = 8 logistic, and = 4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis. 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.

Treatment-related toxicity after irradiation of brain tumours has been underreported in the literature. Furthermore, there is considerable heterogeneity on how and when toxicity is evaluated. The aim of this European Particle Network (EPTN) collaborative project is to develop recommendations for uniform follow-up and toxicity scoring of adult brain tumour patients treated with radiotherapy. A Delphi method-based consensus was reached among 24 international radiation-oncology experts in the field of neuro-oncology concerning the toxicity endpoints, evaluation methods and time points. In this paper, we present a basic framework for consistent toxicity scoring and follow-up, using multiple levels of recommendation. Level I includes all recommendations that are considered minimum of care, whereas level II and III are optional evaluations in the advanced clinical or research setting, respectively. Per outcome domain, the clinical endpoints and evaluation methods per level are listed. Where relevant, the organ at risk threshold doses for recommended referral to specific organ specialists are defined. These consensus-based recommendations for follow-up will enable the collection of uniform toxicity data of brain tumour patients treated with radiotherapy. With adoptation of this standard, collaboration will be facilitated and we can further propel the research field of radiation-induced toxicities relevant for these patients. An online tool to implement this guideline in clinical practice is provided at www.cancerdata.org.

To update the digital online atlas for organs at risk (OARs) delineation in neuro-oncology based on high-quality computed tomography (CT) and magnetic resonance (MR) imaging with new OARs. In this planned update of the neurological contouring atlas published in 2018, ten new clinically relevant OARs were included, after thorough discussion between experienced neuro-radiation oncologists (RTOs) representing 30 European radiotherapy-oncology institutes. Inclusion was based on daily practice and research requirements. Consensus was reached for the delineation after critical review. Contouring was performed on registered CT with intravenous (IV) contrast (soft tissue & bone window setting) and 3 Tesla (T) MRI (T1 with gadolinium & T2 FLAIR) images of one patient (1 mm slices). For illustration purposes, delineation on a 7 T MRI without IV contrast from a healthy volunteer was added. OARs were delineated by three experienced RTOs and a neuroradiologist based on the relevant literature. The presented update of the neurological contouring atlas was reviewed and approved by 28 experts in the field. The atlas is available online and includes in total 25 OARs relevant to neuro-oncology, contoured on CT and MRI T1 and FLAIR (3 T & 7 T). Three-dimensional (3D) rendered films are also available online. In order to further decrease inter- and intra-observer OAR delineation variability in the field of neuro-oncology, we propose the use of this contouring atlas in photon and particle therapy, in clinical practice and in the research setting. The updated atlas is freely available on www.cancerdata.org.

While hypofractionated stereotactic body radiotherapy (SBRT) has been largely adopted in the adult setting, its use remains limited in pediatric patients. This is due, among other factors, to fear of potential toxicities of hypofractionated regimens at a young age. In this context, we report the preliminary acute (<3 months from SBRT) and middle-term (3-24 months) toxicity results of a national prospective study investigating SBRT in pediatric patients. Between 2013 and 2019, 61 patients were included. The first 40 patients (median age: 12 y, range: 3-20) who completed a 2-year-follow-up were included in the present analysis. SBRT was used for treating lung, brain or (para)spinal lesions, either as first irradiation (35%) or in the reirradiation setting (65%). Acute and middle-term grade ≥2 toxicities occurred in 12.5 and 7.5% of the patients, respectively. No grade ≥4 toxicities occurred. Almost all toxicities occurred in the reirradiation setting. SBRT showed a favorable safety profile in young patients treated for lung, brain, and (para)spinal lesions. SBRT appeared to be safe in pediatric patients treated for multiple oncology indications. These results support further evaluation of SBRT, which may have a role to play in this patient population in the future.

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 [F-18]-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 pa

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 p

Despite recent technological advancements in the field of radiation therapy, the treatment results remain disappointing in patients with locally-advanced lung cancer, which experience high rates of local tumour relapse. Hopefully, improvements might come from better biological targeting of the treatment. Notably, the dose painting (DP) strategy is particularly promising. This approach aims at identifying the most radioresistant tumour regions through functional imaging (e.g. PET imaging) in order to specifically target them with higher radiation dose. In this framework, we analysed, for two of the most promising PET tracers in the context of DP (18F-FDG and 18F-FAZA), the stability and the distinctiveness of their uptake patterns during treatment. Obviously, biological tailoring only makes sense if it is coupled to correct physical targeting. In particular, one of the main obstacles hampering precise targeting of lung tumours is the tumour motion associated with respiration. In this context, we evaluated the use of non-invasive methods, such as the administration of continuous positive airway pressure (CPAP), as a way of modulating breathing motion., (MED - Sciences médicales) -- UCL, 2018

Purpose: Several studies reported both systematic and random variations of the mean position of mobile tumors from fraction to fraction. This so-called baseline shift is a major source of uncertainties for mobile targets and can jeopardize treatment quality. Unlike conventional photon therapy, the inclusion of this error in a PTV margin is inadequate in proton therapy because of the range uncertainties. Accounting for this uncertainty in a robust optimizer is much more appropriate, using for instance population-based estimations of the shifts. We developed a baseline-shift model able to automatically generate modified 4D-CT series used as uncertainty scenarios in the TPS. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at planning time are generated from a 4D-CT data. The GTV contour in the MidPCT represents the mean position of the tumor along the breathing cycle. Our model can simulate a baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall, in order to allow the tumor to slide within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: After rigid registration, a baseline shift of 9.5 mm is measured between the first- and second-week 4D-CT sets (W1-CT and W2-CT). In order to validate our model, a third 4D-CT series (BS-W1-CT) was generated from W1-CT to reproduce the measured shift (Figure 1). Water equivalent thickness (WET) has been computed for each voxel of the 3 MidPCTs and revealed that the baseline shift between W1-CT and W2-CT led to a root mean square error (RMSE) of 0.52 mm in the GTV. This WET RMSE was reduced to 0.18 mm between W2-CT and the simulated BS-W1-CT. In additi

BACKGROUND: PET-guided dose painting (DP) aims to target radioresistant tumour regions in order to improve radiotherapy (RT) outcome. Besides the well-known [18F]fluorodeoxyglucose (FDG), the hypoxia positron emission tomography (PET) tracer [18F]fluoroazomycin arabinoside (FAZA) could provide further useful information to guide the radiation dose prescription. In this study, we compare the spatial distributions of FDG and FAZA PET uptakes in lung tumours. MATERIAL AND METHODS: Fourteen patients with unresectable lung cancer underwent FDG and FAZA 4D-PET/CT on consecutive days at three time-points: prior to RT (pre), and during the second (w2), and the third (w3) weeks of RT. All PET/CT were reconstructed in their time-averaged midposition (MidP). The metabolic tumour volume (MTV: FDG standardised uptake value (SUV) > 50% SUVmax), and the hypoxic volume (HV: FAZA SUV > 1.4) were delineated within the gross tumour volume (GTVCT). FDG and FAZA intratumoral PET uptake distributions were subsequently pairwise compared, using both volume-, and voxel-based analyses. RESULTS: Volume-based analysis showed large overlap between MTV and HV: median overlapping fraction was 0.90, 0.94 and 0.94, at the pre, w2 and w3 time-points, respectively. Voxel-wise analysis between FDG and FAZA intratumoral PET uptake distributions showed high correlation: median Spearman's rank correlation coefficient was 0.76, 0.77 and 0.76, at the pre, w2 and w3 time-points, respectively. Interestingly, tumours with high FAZA uptake tended to show more similarity between FDG and FAZA intratumoral uptake distributions than those with low FAZA uptake. CONCLUSIONS: In unresectable lung carcinomas, FDG and FAZA PET uptake distributions displayed unexpectedly strong similarity, despite the distinct pathways targeted by these tracers. Hypoxia PET with FAZA brought very little added value over FDG from the perspective of DP in this population.

Purpose: Large intra- and inter-fraction variations of the breathing motion amplitude have been reported by several publications, with possibly dramatic consequences on treatment quality for moving tumors. This study aims at modeling the amplitude variation as a new uncertainty scenario in the plan robustness verification/optimization. Methods: The OpenREGGUI implementation of diffeomorphic deformable registration (Janssens 2010) was used to compute the velocity fields from a reference phase to all 4DCT phases. Combinations of these fields were then used to deform all phases to the motion Mid-Position. The Mid-Position CT image (MidPCT) is generated by taking the median in each voxel of all deformed phases. This operation reduces noise and artifacts of the MidPCT, compared to individual 4DCT phases. The MidPCT can be deformed back to generate a 4DCT of better quality. The motion amplitude of this reconstructed 4DCT can be scaled by multiplying the velocity fields with a scalar factor. The fast Monte Carlo code MCsquare was used to perform a 4D robustness verification of proton therapy PBS plans. Multiple scenarios of motion amplitudes are simulated. The resulting doses were reported in a DVH-band. Results: A 20% increase of the initial breathing amplitude was first simulated to verify the method. Because rotations are preserved by the operations on velocity fields, the effective variation of tumor displacement, measured between both 4DCT, was 19%. A proton PBS plan was optimized for the initial 4DCT series. Its robustness was then tested with 100 scenarios of motion patterns randomly sampled from a Gaussian distribution centered on the initial motion amplitude and with a standard deviation of 40%. The test revealed a deviation of the CTV D95 ranging from 94% to 97% of the prescribed dose. Conclusion: Our model enables a more realistic robustness verification/optimization of mobile tumor treatments and is directly usable in commercial TPS.