Heart and Lung Dose as Predictors of Overall Survival in Patients With Locally Advanced Lung Cancer. A National Multicenter Study.

Introduction: It is an ongoing debate how much lung and heart irradiation impact overall survival (OS) after definitive radiotherapy for lung cancer. This study uses a large national cohort of patients with locally advanced NSCLC to investigate the association between OS and irradiation of lung and heart. Methods: Treatment plans were acquired from six Danish radiotherapy centers, and patient characteristics were obtained from national registries. A hybrid segmentation tool automatically delineated the heart and substructures. Dose-volume histograms for all structures were extracted and analyzed using principal component analyses (PCAs). Parameter selection for a multivariable Cox model for OS prediction was performed using cross-validation based on bootstrapping. Results: The population consisted of 644 patients with a median survival of 26 months (95% confidence interval [CI]: 24-29). The cross-validation selected two PCA variables to be included in the multivariable model. PCA1 represented irradiation of the heart and affected OS negatively (hazard ratio, 1.14; 95% CI: 1.04-1.26). PCA2 characterized the left-right balance (right atrium and left ventricle) irradiation, showing better survival for tumors near the right side (hazard ratio, 0.92; 95% CI: 0.84-1.00). Besides the two PCA variables, the multivariable model included age, sex, body-mass index, performance status, tumor dose, and tumor volume. Conclusions: Besides the classic noncardiac risk factors, lung and heart doses had a negative impact on survival, while it is suggested that the left side of the heart is a more radiation dose-sensitive region. The data indicate that overall heart irradiation should be reduced to improve the OS if possible.

Frequency and characteristics of immune-related thyroid adverse events in patients with resected stage III/IV melanoma treated with adjuvant PD-1 inhibitors: a national cohort study.

PURPOSE: Immune-related thyroid adverse events (irTAEs) occur frequently following immune checkpoint inhibitor (ICI) therapy. The purpose of this study is to provide knowledge about the incidence, clinical timeline characteristics, associated factors of irTAEs, and potential impact on treatment efficacy in patients with melanoma receiving adjuvant ICI therapy. METHODS: A national multicenter retrospective cohort study of patients with resected stage III/IV melanoma treated with adjuvant PD-1 inhibitors between November 2018 and December 2020. Data were extracted from the Danish Metastatic Melanoma Database. The irTAEs were defined as two consecutive abnormal TSH values and subdivided into transient or persistent. RESULTS: Of 454 patients, 99 developed an irTAE (21.8%), of these were 46 transient (46.5%) and 53 persistent (53.5%). Median time to transient and persistent irTAE was 55 and 44 days, respectively (p = 0.57). A hyperthyroid phase followed by hypothyroidism was seen in 73.6% of persistent irTAEs, whereas 87% of transient irTAEs developed an isolated hypo- or hyperthyroid phase. Multiple variable analysis demonstrated an association between irTAE and female sex (HR 2.45; 95% CI 1.63-3.70; p < 0.001), but no association with recurrence-free survival (HR 0.86; 95% CI 0.50-1.48; p = 0.587) or overall survival (HR 1.05; 95% CI 0.52-2.12, p = 0.891). CONCLUSIONS: IrTAE is a common side effect to PD-1 inhibitors primarily occurring within the first 3 months, with a high risk of persistency. Female sex is a strong predictive factor. IrTAE was not associated with improved clinical outcome.

An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort.

BACKGROUND AND PURPOSE: Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. MATERIALS AND METHODS: The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. RESULTS: The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. CONCLUSION: The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy.

Background: Adaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework. Methods: The network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics. Results: The trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid. Conclusion: We successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source.

A national repository of complete radiotherapy plans: design, Results, and experiences.

BACKGROUND: Previously, many radiotherapy (RT) trials were based on a few selected dose measures. Many research questions, however, rely on access to the complete dose information. To support such access, a national RT plan database was created. The system focuses on data security, ease of use, and re-use of data. This article reports on the development and structure, and the functionality and experience of this national database. METHODS AND MATERIALS: A system based on the DICOM-RT standard, DcmCollab, was implemented with direct connections to all Danish RT centres. Data is segregated into any number of collaboration projects. User access to the system is provided through a web interface. The database has a finely defined access permission model to support legal requirements. RESULTS: Currently, data for more than 14,000 patients have been submitted to the system, and more than 50 research projects are registered. The system is used for data collection, trial quality assurance, and audit data set generation.Users reported that the process of submitting data, waiting for it to be processed, and then manually attaching it to a project was resource intensive. This was accommodated with the introduction of triggering features, eliminating much of the need for users to manage data manually. Many other features, including structure name mapping, RT plan viewer, and the Audit Tool were developed based on user input. CONCLUSION: The DcmCollab system has provided an efficient means to collect and access complete datasets for multi-centre RT research. This stands in contrast with previous methods of collecting RT data in multi-centre settings, where only singular data points were manually reported. To accommodate the evolving legal environment, DcmCollab has been defined as a 'data processor', meaning that it is a tool for other research projects to use rather than a research project in and of itself.

End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort.

Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.

Impact of abdominal compression on intra-fractional motion and delivered dose in magnetic resonance image-guided adaptive radiation ablation of adrenal gland metastases.

PURPOSE: The current study investigated the impact of abdominal compression on motion and the delivered dose during non-gated, magnetic resonance image (MRI)-guided radiation ablation of adrenal gland metastases. METHODS: Thirty-one patients with adrenal gland metastases treated to 45-60 Gy in 3-8 fractions on a 1.5 T MRI-linac were included in the study. The patients were breathing freely (n = 14) or with motion restricted by using an abdominal compression belt (n = 17). The time-resolved position of the target in online 2D cine MR images acquired during treatment was assessed and used to estimate the dose delivered to the GTV and abutting luminal organs at risk (OAR). RESULTS: The median (range) 3D root-mean-square target position error was significantly higher in patients treated without a compression belt [2.9 (1.9-5.6) mm] compared to patients using the belt [2.1 (1.2-3.5) mm] (P < 0.01). The median (range) GTV V95% was significantly reduced from planned 98.6 (65.9-100) % to delivered 96.5 (64.5-99.9) % due to motion (P < 0.01). Most prominent dose reductions were found in patients showing either large target drift or respiration motion and were mainly treated without abdominal compression. Motion did not lead to an increased number of constraint violations for luminal OAR. CONCLUSIONS: Acceptable target coverage and dose to OAR was observed in the vast majority of patients despite intra-fractional motion during adaptive MRI-guided radiation ablation. The use of abdominal compression significantly reduced the target position error and prevented the most prominent target coverage degradations and is, therefore, recommended as motion management at MRI-linacs.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients.

BACKGROUND: In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. MATERIALS AND METHODS: The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. RESULTS: The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 - 0.90] and 0.68 [0.51 - 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 - 1.1] mm and 1.9 mm [1.5 - 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in ΔNTCP. CONCLUSIONS: The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the ΔNTCP calculations could be discerned.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group.

BACKGROUND AND PURPOSE: The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it. MATERIALS AND METHODS: Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group. RESULTS: The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types. CONCLUSION: Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Does coronary artery calcium score have an impact on overall survival for locally advanced non-small cell lung cancer treated with definitive radiotherapy.

BACKGROUND AND PURPOSE: Coronary artery calcium score (CACs) is an excellent marker for survival in non-cancer patients, but its role in locally advanced non-small cell lung cancer (LA-NSCLC) patients remains uncertain. In this study, we hypothesize that CACs is a prognostic marker for survival in a competing risk analysis in LA-NSCLC patients treated with definitive radiotherapy. MATERIALS AND METHODS: We included 644 patients with LA-NSCLC treated in 2014-2015 in Denmark. Baseline patient characteristics were derived from the Danish Lung Cancer Registry. Radiotherapy planning CT scans were used for manual CACs measurements, and the patients were divided into four groups, CACs 0, 1-99, 100-399, and ≥400. A multivariable Cox model utilizing bootstrapping for cross-validation modeled overall survival (OS). RESULTS: The median follow-up time was seven years, and the median OS was 26 months (95% CI 24-29). Within each CAC group 0, 1-99, 100-399, and ≥400 were 172, 182, 143, and 147 patients, respectively. In the univariable analysis, the survival decreased with increasing CACs. However, after adjustment for age, PS, radiotherapy dose, and logarithmic GTV, CACs did not have a statistically significant impact on OS with hazard ratios of 1.04 (95% CI 0.85-1.28), 1.11 (95%CI 0.89-1.43), and 1.16 (95%CI 0.92-1.47) for CACs 1-99, CACs 100-399 and ≥400, respectively. Elevated CACs was observed in 73 % of the patients suggesting a high risk of cardiac comorbidity before radiotherapy. CONCLUSION: CACs did not add prognostic information to our population's classical risk factors, such as tumor volume, performance status, and age; the lung cancer has the highest priority despite the risk of baseline cardiac comorbidity.

The effect of respiration-induced target motion on 3D magnetic resonance images used to guide radiotherapy.

Background and purpose: 3D Magnetic Resonance Imaging (MRI) is used in radiation therapy for reference planning and, lately, for adaptive treatments on MR accelerators. This study aimed to investigate the impact of different types of respiratory motion on the apparent target position and extent in such scans. Materials and methods: An MRI motion phantom with a 30 mm diameter target was used to simulate cranial-caudal (CC) motion and imaged at an MR-Linac using a standard clinically released 3D T2w sequence. Scans were acquired for each combination of functions (sin(t), sin4(t) and sin12(t)), peak-to-peak amplitudes (5, 10, 15 and 20 mm), and periods (4, 5 and 6 s). Furthermore, respiration CC motion patterns from two patients were used. Motion functions were shifted such that the time average target position would match a static reference scan at 0-position. The target was automatically identified in coronal and sagittal images using k-means clustering. The mean position and area of the target were calculated and compared to the reference scan. Results: Artefacts increased with amplitude and depended on the motion type. Sin(t) and sin4(t) oscillations resulted in a blurring of the target, which led to an increased target area, while sin12(t) motion did not show significant changes in the target area. However, for the sin12(t) motion, the offset in apparent position was prominent, while that was not the case for the sin(t) and sin4(t) motion. The patient respiration motion profiles showed similar trends. Conclusions: In 3D MRI, target motion can change apparent tumour extent and apparent position. The changes increase with motion amplitude and depend on the motion type.

Larynx cancer survival model developed through open-source federated learning.

INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.

Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer.

Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.

Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients.

Background: During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC. Material/methods: 32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD). Results: 32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08-0.39 cm) and 0.07 cm (range 0.04-0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001). Conclusion: Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy.

INTRODUCTION: Prediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution. METHODS: Open-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions. RESULTS: 1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71-0.76) and 0.70 (0.66-0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise. CONCLUSION: Open-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation.

Causal relation between heart irradiation and survival of lung cancer patients after radiotherapy.

INTRODUCTION: In a recent study, setup uncertainties in the direction of the heart were shown to impact the overall survival of non-small cell lung cancer (NSCLC) patients after radiotherapy, indicating the causal effect between heart irradiation and survival. The current study aims to externally evaluate this observation within a patient cohort treated using daily IGRT. METHOD: NSCLC patients with locally-advanced disease and daily CBCT were included. For all treatment fractions, the distance between the isocenter and the heart was evaluated based on the clinical setup registrations. The variation in heart position between planning and treatment (DeltaDistance) was estimated from these registrations. The possible impact of DeltaDistance on survival was analysed by a multivariable Cox model of overall survival, allowing for a time-dependent impact of DeltaDistance to allow for toxicity latency. RESULTS: Daily CBCT information was available for 489 patients at Odense University Hospital. The primary Cox model contained GTV volume, patient age, performance status, and DeltaDistance. DeltaDistance significantly impacted overall survival approximately 50 months after radiotherapy. Subanalyses indicated that the observed effect is mainly present among the patients with the least clinical risk factors. CONCLUSION: Our results confirm the impact of setup variations in the direction of the heart on the survival of NSCLC patients, even within a cohort using daily CBCT setup guidance. This result indicates a causal effect between heart irradiation and survival. It will be challenging to reduce the setup uncertainty even further; thus, increased focus on dose constraints on the heart seems warranted.

Safety of gadolinium based contrast agents in magnetic resonance imaging-guided radiotherapy - An investigation of chelate stability using relaxometry.

BACKGROUND AND PURPOSE: With the introduction of hybrid magnetic resonance linacs (MR-linac), improved imaging has enabled daily treatment adaptation. However, the use of gadolinium based contrast agents (GBCAs) is desired to further improve MR image contrast. GBCAs are in the form of a non-toxic metalorganic gadolinium complex, but toxic un-chelated aqueous gadolinium(III), Gd3+(aq), can be released in patients if the organic ligand is degraded by the radiation. In this study, T1 relaxation measurements were performed to study the effect of radiation on three GBCAs. MATERIALS AND METHODS: GBCAs, gadoteric acid, gadobutrol and gadoxectic acid were investigated in a concentration range of 10-100 mM. Measurements were performed on a 500 MHz nuclear MR (NMR) spectrometer with a high-resolution inversion recovery sequence to determine T1. Samples were irradiated with 7 MV photons on an MR-linac to a total dose of 100 Gy. The lower detection limit of Gd3+(aq) was established by estimating the overall measurement uncertainty and comparing to corresponding changes in R1 when replacing chelated Gd3+ with gadolinium nitrate at predefined percentages. RESULTS: The overall measurement uncertainty was estimated to ±0.0053 ms-1, corresponding to Gd3+(aq) detection levels 1%-1.5% or 1-4.5 micro molar at clinical GBCA dosage. No detectable differences in R1 were observed between irradiated and non-irradiated samples for any GBCA. CONCLUSIONS: This study did not find any measurable degradation of GBCAs due to irradiation with high-energy X-rays, however, in-vivo investigations are needed to provide the clinical basis for safe use of contrast agents in a radiotherapy workflow.

Robust extraction of biological information from diffusion-weighted magnetic resonance imaging during radiotherapy using semi-automatic delineation.

Background and purpose: Diffusion-Weighted Magnetic Resonance imaging (DWI) quantifies water mobility through the Apparent Diffusion Coefficient (ADC), a promising radiotherapy response biomarker. ADC measurements depend on manual delineation of a region of interest, a time-consuming and observer-dependent process. Here, the aim was to introduce and test the performance of a new, semi-automatic delineation tool (SADT) for ADC calculation within the viable region of the tumour. Materials and methods: Thirty patients with rectal cancer were scanned with DWI before radiotherapy (RT) (baseline) and two weeks into RT (week 2). The SADT was based on intensities in b=1100 s mm-2 DWI and derived ADC maps. ADC values measured using the SADT and manual delineations were compared using Bland-Altman- and correlation analyses. Delineations were repeated to assess intra-observer variation, and repeatability was estimated using repeated DWI scans. Results: ADC measured using the SADT and manual delineation showed strong and moderate correlation at baseline and week 2, respectively, with the SADT measuring systematically smaller values. Intra-observer ADC variation was slightly smaller for the SADT compared to manual delineation both at baseline, [-0.00; 0.03] vs. [-0.02; 0.04] 10-3 mm2 s-1, and week 2, [-0.01; 0.00] vs. [-0.04; 0.07] 10-3 mm2 s-1 (68.3% limits of agreement). The ADC change between baseline and week 2 was larger than the ADC uncertainty ( ± 0.04 · 10-3 mm2 s-1) in all cases except one. Conclusion: The presented SADT showed performance comparable to manual expert delineation, and with sufficient consistency to allow extraction of potential biological information from the viable tumour.

Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment.

BACKGROUND AND PURPOSE: With daily, MR-guided online adapted radiotherapy (MRgART) it may be possible to reduce the PTV in pelvic RT. This study investigated the potential reduction in normal tissue complication probability (NTCP) of MRgART compared to standard radiotherapy for high-risk prostate cancer. MATERIALS AND METHODS: Twenty patients treated with 78 Gy to the prostate and 56 Gy to elective pelvic lymph nodes were included. VMAT plans were generated with standard clinical PTV margins. Additionally to the planning MR, patients had three MRI scans during treatment to simulate an MRgART. A reference plan with PTV margins determined for MRgART was created per patient and adapted to each of the following MRs. Adapted plans were warped to the planning MR for dose accumulation. The standard plan was rigidly registered to each adaptation MR before it was warped to the planning MR for dose accumulation. Dosimetric impact was compared by DVH analysis and potential clinical effects were assessed by NTCP modeling. RESULTS: MRgART yielded statistically significant lower doses for the bladder wall, rectum and peritoneal cavity, compared to the standard RT, which translated into reduced median risks of urine incontinence (ΔNTCP 2.8%), urine voiding pain (ΔNTCP 2.8%) and acute gastrointestinal toxicity (ΔNTCP 17.4%). Mean population accumulated doses were as good or better for all investigated OAR when planned for MRgART as standard RT. CONCLUSION: Online adapted radiotherapy may reduce the dose to organs at risk in high-risk prostate cancer patients, due to reduced PTV margins. This potentially translates to significant reductions in the risks of acute and late adverse effects.

Tumour motion analysis from planning to end of treatment course for a large cohort of peripheral lung SBRT targets.

BACKGROUND: The aim is to quantify and analyse tumour motion during a course of treatment for lung SBRT patients. MATERIAL AND METHODS: Peak-to-peak motion of 483 tumours in 441 patients treated with peripheral lung SBRT at a single institution over a two year period was measured on planning CT and at all treatment fractions. Planning 4D-CT scans were analysed using our clinical workflow involving deformable propagation of the delineated target to all phases. Similarly, acquisition of the 4D-CBCT data followed the clinical workflow based on XVI 5.0 available on Elekta linacs. Differences and correlations of the peak-to-peak motion on the planning CT and at treatment were analysed. RESULTS: On the planning CT, a total of 81.4% of the tumours had a peak-to-peak motion <10 mm, and 96.1% had <20 mm. The largest motion was observed in the CC direction, with largest amplitude for tumours located in the caudal posterior part of the lung. The difference in amplitude in CC between planning CT and first fraction had a mean and standard deviation of 0.3 mm and 3.5 mm, respectively, and the largest differences were observed in the caudal posterior part of the lung. Patients with a difference in tumour motion amplitude exceeding two standard deviations (>7 mm) at the first fraction were evaluated individually, and they all had poor 4DCT image quality. The difference between the first and second/third fractions had a mean and standard deviation of 0.4 mm/0.5 mm and 2.0 mm/1.9 mm. CONCLUSION: Tumour motion at first treatment was similar to motion at planning, and motion at subsequent treatments was very similar to motion at first treatment. Large tumour motions are located towards the caudal posterior tumour locations. Patients with poor 4D-CT image quality should be closely followed at the first treatment to verify the motion.