Clinical implementation of standardized neurocognitive assessment before and after radiation to the brain.

Background: Radiotherapy induced impairment of cognitive function can lead to a reduced quality of life. The aim of this study was to describe the implementation and compliance of standardized neurocognitive assessment. In addition, the first results of cognitive changes for patients receiving a radiation dose to the brain are described. Materials and methods: Patients that received radiation dose to the brain (neuro, head and neck and prophylactic cranial irradiation between April-2019 and Dec-2021 were included. Three neuro cognitive tests were performed a verbal learning and memory test, the Hopkins Verbal Learning Test; a verbal fluency test, the Controlled Oral Word Association Test and a speed and cognitive flexibility test, the Trail Making Test A&B. Tests were performed before the start of radiation, 6 months (6 m) and 1 year (1y) after irradiation. The Reliable Change Index (RCI) between baseline and follow-up was calculated using reference data from literature. Results: 644 patients performed the neurocognitive tests at baseline, 346 at 6 months and 205 at 1y after RT, with compliance rates of 90.4%, 85.6%, and 75.3%, respectively. Reasons for non-compliance were: 1. Patient did not attend appointment (49%), 2. Patient was unable to perform the test due to illness (12%), 3. Patient refused the test (8 %), 4. Various causes, (31%). A semi-automated analysis was developed to evaluate the test results. In total, 26% of patients showed a significant decline in at least one of variables at 1y and 11% on at least 2 variables at 1y. However, an increase in cognitive performance was observed in 49% (≥1 variable) and 22% (≥2 variables). Conclusion: Standardized neurocognitive testing within the radiotherapy clinic was successfully implemented, with a high patient compliance. A semi-automatic method to evaluate cognitive changes after treatment was defined. Data collection is ongoing, long term follow-up (up to 5 years after treatment) and dose-effect analysis will be performed.

Current applications of deep-learning in neuro-oncological MRI.

PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology. METHODS: We reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up. RESULTS: Forty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures. CONCLUSION: Deep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.

Tumor regression during radiotherapy for non-small cell lung cancer patients using cone-beam computed tomography images.

PURPOSE: Previous literature has reported contradicting results regarding the relationship between tumor volume changes during radiotherapy treatment for non-small cell lung cancer (NSCLC) patients and locoregional recurrence-free rate or overall survival. The aim of this study is to validate the results from a previous study by using a different volume extraction procedure and evaluating an external validation dataset. METHODS: For two datasets of 94 and 141 NSCLC patients, gross tumor volumes were determined manually to investigate the relationship between tumor volume regression and locoregional control using Kaplan-Meier curves. For both datasets, different subgroups of patients based on histology and chemotherapy regimens were also investigated. For the first dataset (n = 94), automatically determined tumor volumes were available from a previously published study to further compare their correlation with updated clinical data. RESULTS: A total of 70 out of 94 patients were classified into the same group as in the previous publication, splitting the dataset based on median tumor regression calculated by the two volume extraction methods. Non-adenocarcinoma patients receiving concurrent chemotherapy with large tumor regression show reduced locoregional recurrence-free rates in both datasets (p < 0.05 in dataset 2). For dataset 2, the opposite behavior is observed for patients not receiving chemotherapy, which was significant for overall survival (p = 0.01) but non-significant for locoregional recurrence-free rate (p = 0.13). CONCLUSION: The tumor regression pattern observed during radiotherapy is not only influenced by irradiation but depends largely on the delivered chemotherapy schedule, so it follows that the relationship between patient outcome and the degree of tumor regression is also largely determined by the chemotherapy schedule. This analysis shows that the relationship between tumor regression and outcome is complex, and indicates factors that could explain previously reported contradicting findings. This, in turn, will help guide future studies to fully understand the relationship between tumor regression and outcome.

Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer?

BACKGROUND: Muscle depletion negatively impacts treatment efficacy and survival rates in cancer. Prevention and timely treatment of muscle loss require prediction of patients at risk. We aimed to investigate the potential of skeletal muscle radiomic features to predict future muscle loss. METHODS: A total of 116 patients with stage IV non-small cell lung cancer included in a randomised controlled trial (NCT01171170) studying the effect of nitroglycerin added to paclitaxel-carboplatin-bevacizumab were enrolled. In this post hoc analysis, muscle cross-sectional area and radiomic features were extracted from computed tomography images obtained before initiation of chemotherapy and shortly after administration of the second cycle. For internal cross-validation, the cohort was randomly split in a training set and validation set 100 times. We used least absolute shrinkage and selection operator method to select features that were most significantly associated with muscle loss and an area under the curve (AUC) for model performance. RESULTS: Sixty-nine patients (59%) exhibited loss of skeletal muscle. One hundred ninety-three features were used to construct a prediction model for muscle loss. The average AUC was 0.49 (95% confidence interval [CI]: 0.36, 0.62). Differences in intensity and texture radiomic features over time were seen between patients with and without muscle loss. CONCLUSIONS: The present study shows that skeletal muscle radiomics did not predict future muscle loss during chemotherapy in non-small cell lung cancer. Differences in radiomic features over time might reflect myosteatosis. Future imaging analysis combined with muscle tissue analysis in patients and in experimental models is needed to unravel the biological processes linked to the radiomic features.

Evaluation of third treatment week as temporal window for assessing responsiveness on repeated FDG-PET-CT scans in Non-Small Cell Lung Cancer patients.

PURPOSE: Early assessment of tumour response to treatment with repeated FDG-PET-CT imaging has potential for treatment adaptation but it is unclear what the optimal time window for this evaluation is. Previous studies indicate that changes in SUVmean and the effective radiosensitivity (αeff, accounting for uptake variations and accumulated dose until the second FDG-PET-CT scan) are predictive of 2-year overall survival (OS) when imaging is performed before radiotherapy and during the second week. This study aims to investigate if multiple FDG-PET-derived quantities determined during the third treatment week have stronger predictive power. METHODS: Twenty-eight lung cancer patients were imaged with FDG-PET-CT before radiotherapy (PET1) and during the third week (PET2). SUVmean, SUVmax, SUVpeak, MTV41%-50% (Metabolic Tumour Volume), TLG41%-50% (Total Lesion Glycolysis) in PET1 and PET2 and their change (), as well as average αeff (α¯eff) and the negative fraction of αeff values [Formula: see text] ) were determined. Correlations were sought between FDG-PET-derived quantities and OS with ROC analysis. RESULTS: Neither SUVmean, SUVmax, SUVpeak in PET1 and PET2 (AUC = 0.5-0.6), nor their changes (AUC = 0.5-0.6) were significant for outcome prediction purposes. Lack of correlation with OS was also found for α¯eff (AUC = 0.5) and [Formula: see text] (AUC = 0.5). Threshold-based quantities (MTV41%-50%, TLG41%-50%) and their changes had AUC = 0.5-0.7. P-values were in all cases ≫0.05. CONCLUSIONS: The poor OS predictive power of the quantities determined from repeated FDG-PET-CT images indicates that the third week of treatment might not be suitable for treatment response assessment. Comparatively, the second week during the treatment appears to be a better time window.

Pharmacokinetic modeling of a novel hypoxia PET tracer [18F]HX4 in patients with non-small cell lung cancer.

BACKGROUND: [18F]HX4 is a promising new PET tracer developed to identify hypoxic areas in tumor tissue. This study analyzes [18F]HX4 kinetics and assesses the performance of simplified methods for quantification of [18F]HX4 uptake. To this end, eight patients with non-small cell lung cancer received dynamic PET scans at three different time points (0, 120, and 240 min) after injection of 426 ± 72 MBq [18F]HX4, each lasting 30 min. Several compartment models were fitted to time activity curves (TAC) derived from various areas within tumor tissue using image-derived input functions. RESULTS: Best fits were obtained using the reversible two-tissue compartment model with blood volume parameter (2T4k+VB). Simplified measures correlated well with VT estimates (tumor-to-blood ratio (TBr) R 2 = 0.96, tumor-to-muscle ratio R 2 = 0.94, standardized uptake value R 2 = 0.89). CONCLUSIONS: [18F]HX4 shows reversible kinetics in tumor tissue: 2T4k+VB. TBr based on static imaging at 2 or 4 h can be used for quantification of [18F]HX4 uptake.

Current preclinical and clinical applications of hypoxia PET imaging using 2-nitroimidazoles.

Hypoxia is a common characteristic of solid tumors and is associated with poor prognosis. Positron emission tomography (PET) can visualize tumor hypoxia in a non-invasive, 3-dimensional manner and can be used to acquire information longitudinally. Multiple 2-nitroimidazole based PET tracers are developed, validated and quantified in the search for the ideal hypoxia tracer and several tracers have shown to reliably represent tumor hypoxia. Furthermore, multiple studies describe the prognostic value of hypoxia PET imaging and the ability to monitor hypoxia during treatment. These applications can be of great potential and their role in treatment planning and modification needs to be further assessed with respect to personalized chemoradiation therapy. In this review we focus on the tracers that were positively validated in preclinical and clinical studies and report accurate quantification and visualization of hypoxia. The characteristics of these tracers are summarized for both preclinical and clinical studies. Furthermore, the clinical applications of hypoxia PET imaging are addressed with a focus on the ability to reliably monitor tumor hypoxia during treatment and the prognostic potential. Also the feasibility studies for hypoxia guided intensity modulated radiation therapy and the patient stratification for hypoxia targeted drugs are assessed.

Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer.

PURPOSE: Dynamic contrast-enhanced CT (DCE-CT) quantifies vasculature properties of tumors, whereas static FDG-PET/CT defines metabolic activity. Both imaging modalities are capable of showing intra-tumor heterogeneity. We investigated differences in vasculature properties within primary non-small cell lung cancer (NSCLC) tumors measured by DCE-CT and metabolic activity from FDG-PET/CT. METHODS: Thirty three NSCLC patients were analyzed prior to treatment. FDG-PET/CT and DCE-CT were co-registered. The tumor was delineated and metabolic activity was segmented on the FDG-PET/CT in two regions: low (<50% maximum SUV) and high (≥50% maximum SUV) metabolic uptake. Blood flow, blood volume and permeability were calculated using a maximum slope, deconvolution algorithm and a Patlak model. Correlations were assessed between perfusion parameters for the regions of interest. RESULTS: DCE-CT provided additional information on vasculature and tumor heterogeneity that was not correlated to metabolic tumor activity. There was no significant difference between low and high metabolic active regions for any of the DCE-CT parameters. Furthermore, only moderate correlations between maximum SUV and DCE-CT parameters were observed. CONCLUSIONS: No direct correlation was observed between FDG-uptake and parameters extracted from DCE-CT. DCE-CT may provide complementary information to the characterization of primary NSCLC tumors over FDG-PET/CT imaging.

Interfractional trend analysis of dose differences based on 2D transit portal dosimetry.

Dose delivery of a radiotherapy treatment can be influenced by a number of factors. It has been demonstrated that the electronic portal imaging device (EPID) is valuable for transit portal dosimetry verification. Patient related dose differences can emerge at any time during treatment and can be categorized in two types: (1) systematic-appearing repeatedly, (2) random-appearing sporadically during treatment. The aim of this study is to investigate how systematic and random information appears in 2D transit dose distributions measured in the EPID plane over the entire course of a treatment and how this information can be used to examine interfractional trends, building toward a methodology to support adaptive radiotherapy. To create a trend overview of the interfractional changes in transit dose, the predicted portal dose for the different beams is compared to a measured portal dose using a γ evaluation. For each beam of the delivered fraction, information is extracted from the γ images to differentiate systematic from random dose delivery errors. From the systematic differences of a fraction for a projected anatomical structures, several metrics are extracted like percentage pixels with |γ| > 1. We demonstrate for four example cases the trends and dose difference causes which can be detected with this method. Two sample prostate cases show the occurrence of a random and systematic difference and identify the organ that causes the difference. In a lung cancer case a trend is shown of a rapidly diminishing atelectasis (lung fluid) during the course of treatment, which was detected with this trend analysis method. The final example is a breast cancer case where we show the influence of set-up differences on the 2D transit dose. A method is presented based on 2D portal transit dosimetry to record dose changes throughout the course of treatment, and to allow trend analysis of dose discrepancies. We show in example cases that this method can identify the causes of dose delivery differences and that treatment adaptation can be triggered as a result. It provides an important element toward informed decision-making for adaptive radiotherapy.

Integration of FDG-PET/CT into external beam radiation therapy planning: technical aspects and recommendations on methodological approaches.

UNLABELLED: This work addresses the clinical adoption of FDG-PET/CT for image-guided radiation therapy planning (RTP). As such, important technical and methodological aspects of PET/CT-based RTP are reviewed and practical recommendations are given for routine patient management and clinical studies. First, recent developments in PET/CT hardware that are relevant to RTP are reviewed in the context of quality control and system calibration procedures that are mandatory for a reproducible adoption of PET/CT in RTP. Second, recommendations are provided on image acquisition and reconstruction to support the standardization of imaging protocols. A major prerequisite for routine RTP is a complete and secure data transfer to the actual planning system. Third, state-of-the-art tools for image fusion and co-registration are discussed briefly in the context of PET/CT imaging pre- and post-RTP. This includes a brief review of state-of-the-art image contouring algorithms relevant to PET/CT-guided RTP. Finally, practical aspects of clinical workflow and patient management, such as patient setup and requirements for staff training are emphasized. PET/CT-guided RTP mandates attention to logistical aspects, patient set-up and acquisition parameters as well as an in-depth appreciation of quality control and protocol standardization. CONCLUSION: Upon fulfilling the requirements to perform PET/CT for RTP, a new dimension of molecular imaging can be added to traditional morphological imaging. As a consequence, PET/CT imaging will support improved RTP and better patient care. This document serves as a guidance on practical and clinically validated instructions that are deemed useful to the staff involved in PET/CT-guided RTP.

Therapy response assessment in radiotherapy of lung cancer.

Radiotherapy represents an important therapeutic modality in the treatment of lung cancer. Treatment response assessment after high-dose radiotherapy with or without concurrent chemotherapy using conventional imaging methods is limited since normal tissue appearance might resemble tumour recurrence very close. Positron emission tomography (PET) based imaging has been introduced in this situation with great enthusiasm and provides useful additional information on the biologic characteristics of the irradiated region, be it tumour or healthy lung tissue, provided some marginal conditions are taken into account. Furthermore, biologic imaging seems highly appealing for treatment guidance especially during treatment protocols including multimodality approaches with neoadjuvant intent. Treatment response might not only serve as a surrogate marker for pathological remission but for overall prognosis as well. Within this context, the optimal time point and the best parameter to evaluate remain issues of continuing debate. This review is aimed to give an overview of the current state of the scientific knowledge.

Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry.

Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V90 and V95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our predictive model, 2D transit dosimetry measurements can now directly be translated in clinically more relevant DVH parameter changes for the PTV during conventional breast treatment. In this way, the possibility to design decision protocols based on extracted DVH changes is created instead of undertaking elaborate actions such as repeated treatment planning or 3D dose reconstruction for a large group of patients.

A global calibration model for a-Si EPIDs used for transit dosimetry.

Electronic portal imaging devices (EPIDs) are not only applied for patient setup verification and detection of organ motion but are also increasingly used for dosimetric verification. The aim of our work is to obtain accurate dose distributions from a commercially available amorphous silicon (a-Si) EPID for transit dosimetry applications. For that purpose, a global calibration model was developed, which includes a correction procedure for ghosting effects, field size dependence and energy dependence of the a-Si EPID response. In addition, the long-term stability and additional buildup material for this type of EPID were determined. Differences in EPID response due to photon energy spectrum changes have been measured for different absorber thicknesses and field sizes, yielding off-axis spectrum correction factors based on transmission measurements. Dose measurements performed with an ionization chamber in a water tank were used as reference data, and the accuracy of the dosimetric calibration model was determined for a large range of treatment conditions. Gamma values using 3% as dose-difference criterion and 3 mm as distance-to-agreement criterion were used for evaluation. The field size dependence of the response could be corrected by a single kernel, fulfilling the gamma evaluation criteria in case of virtual wedges and intensity modulated radiation therapy fields. Differences in energy spectrum response amounted up to 30%-40%, but could be reduced to less than 3% using our correction model. For different treatment fields and (in)homogeneous phantoms, transit dose distributions satisfied in almost all situations the gamma criteria. We have shown that a-Si EPIDs can be accurately calibrated for transit dosimetry purposes.

Experimental verification of a portal dose prediction model.

Electronic portal imaging devices (EPIDs) can be used to measure a two-dimensional (2D) dose distribution behind a patient, thus allowing dosimetric treatment verification. For this purpose we experimentally assessed the accuracy of a 2D portal dose prediction model based on pencil beam scatter kernels. A straightforward derivation of these pencil beam scatter kernels for portal dose prediction models is presented based on phantom measurements. The model is able to predict the 2D portal dose image (PDI) behind a patient, based on a PDI without the patient in the beam in combination with the radiological thickness of the patient, which requires in addition a PDI with the patient in the beam. To assess the accuracy of portal dose and radiological thickness values obtained with our model, various types of homogeneous as well as inhomogeneous phantoms were irradiated with a 6 MV photon beam. With our model we are able to predict a PDI with an accuracy better than 2% (mean difference) if the radiological thickness of the object in the beam is symmetrically situated around the isocenter. For other situations deviations up to 3% are observed for a homogeneous phantom with a radiological thickness of 17 cm and a 9 cm shift of the midplane-to-detector distance. The model can extract the radiological thickness within 7 mm (maximum difference) of the actual radiological thickness if the object is symmetrically distributed around the isocenter plane. This difference in radiological thickness is related to a primary portal dose difference of 3%. It can be concluded that our model can be used as an easy and accurate tool for the 2D verification of patient treatments by comparing predicted and measured PDIs. The model is also able to extract the primary portal dose with a high accuracy, which can be used as the input for a 3D dose reconstruction method based on back-projection.