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Background and purpose: Growing evidence suggests that spatial dose variations across the rectal surface influence toxicity risk after radiotherapy. Existing methodologies employ a fixed, arbitrary physical extent for rectal dose mapping, limiting their analysis. We developed a method to standardise rectum contours, unfold them into 2D cylindrical surface maps, and identify subregions where higher doses increase rectal toxicities. Materials and methods: Data of 1,048 patients with prostate cancer from the REQUITE study were used. Deep learning based automatic segmentations were generated to ensure consistency. Rectum length was standardised using linear transformations superior and inferior to the prostate. The automatic contours were validated against the manual contours through contour variation assessment with cylindrical mapping. Voxel-based analysis of the dose surface maps for the manual and automatic contours against individual rectal toxicities was performed using Student’s t permutation test and Cox Proportional Hazards Model (CPHM). Significance was defined by permutation testing. Results: Our method enabled the analysis of 1,048 patients using automatic segmentation. Student’s t-test showed significance (p

Background and purpose: Magnetic resonance imaging – linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5 T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects. Materials and methods: Patients were imaged on a 1.5 T MRI-linac or a 1.5 T diagnostic MR system twice before treatment. DCE-MRI parameters including Ktrans were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy. Results: 14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline Ktrans estimates were comparable on both MR systems; 0.13 [95 %CI: 0.10 to 0.16] min−1 (diagnostic MR) and 0.15 [0.12 to 0.18] min−1 (MRI-linac). wCV values were 22.6 % [95 % CI: 16.2 to 37.3 %] (diagnostic MR) and 11.7 % [8.4 to 19.3 %] (MRI-linac). Combined cohort increase in Ktrans was significant (p

Increasing radiotherapy dose to select cardiac structures is associated with cardiac events and premature death. Previous studies have found a dose–response relationship for structures at the base of the heart.We have defined a new cardiac anatomical region at risk for radiotherapy by consensus opinion, based on image-based data-mining studies. The cardiac avoidance area comprises the superior vena cava, right atrium, aortic root, left main coronary artery, and proximal segments of the left anterior descending and right coronary arteries. We describe a contouring atlas for the cardiac avoidance area to facilitate implementation.

Background and purpose: Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM. Materials and methods: Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: i) registering prone/supine cohorts independently and ii) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU). Results: DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found. Conclusions: B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.

Background and purpose: Daily target re-delineation in online adaptive radiotherapy (oART) introduces uncertainty. The aim of this study was to evaluate artificial intelligence (AI) generated contours and inter-observer target variation among radiotherapy technicians in cone-beam CT (CBCT) guided oART of bladder cancer. Materials and methods: For each of 10 consecutive patients treated with oART for bladder cancer, one CBCT was randomly selected and retrospectively included. The bladder (CTV-T) was AI-segmented (CTV-TAI). Seven radiotherapy technicians independently reviewed and edited CTV-TAI, generating CTV-TADP. Contours were benchmarked against a ground truth contour (CTV-TGT) delineated blindly from scratch. CTV-TADP and CTV-TAI were compared to CTV-TGT using volume, dice similarity coefficient, and bidirectional local distance. Dose coverage (D99%>95 %) of CTV-TGT was evaluated for treatment plans optimized for CTV-TAI and CTV-TADP with clinical margins. Inter-observer variation among CTV-TADP was assessed using coefficient of variation and generalized conformity index. Results: CTV-TGT ranged from 48.7 cm3 to 211.6 cm3. The median [range] volume difference was 4.5 [−17.8, 42.4] cm3 for CTV-TADP and −15.5 [−54.2, 4.3] cm3 for CTV-TAI, compared to CTV-TGT. Corresponding dice similarity coefficients were 0.87 [0.71, 0.95] and 0.84 [0.64, 0.95]. CTV-TGT was adequately covered in 68/70 plans optimized on CTV-TADP and in 6/10 plans optimized on CTV-TAI with clinical margins. The median [range] coefficient of variation was 0.08 [0.05, 0.11] and generalized conformity index was 0.78 [0.71, 0.88] among CTV-TADP. Conclusions: Target re-delineation in CBCT-guided oART of bladder cancer demonstrated non-isotropic inter-observer variation. Manual adjustment of AI-generated contours was necessary to cover ground truth targets.

Background: Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis. Materials and methods: 280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression. Results: There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (−3.92%, IQR 4.00, p

Background and purpose: Patients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables’ ability to predict cCR in patients pre chemoradiotherapy. Materials and methods: Using the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced. Results: Using Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69–0.83) in the training set and 0.68 (95% CI 0.57–0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66–0.80) and 0.62 (95% CI 0.51–0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61–0.75) and 0.66 (95% CI 0.56–0.77) in the training and validation sets. Conclusion: The predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.

Background and purpose: Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3D auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and Method: Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors’ gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mDTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results: Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mDTA of (0.81±0.31) mm for the brainstem, (0.20±0.08) mm for the mandible, (0.77±0.14) mm for the left parotid and (0.81±0.28) mm for the right parotid. Conclusions: Through careful tuning and customisation we trained a 3D CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods.

Background and purpose: Anatomical changes during radiotherapy pose a challenge to robustness of plans. Principal component analysis (PCA) is commonly used to model such changes. We propose a toolbox to evaluate how closely a given PCA model can represent actual deformations seen in the patient and highlight regions where the model struggles to capture these changes. Materials and methods: We propose to calculate a residual error map from the difference between an actual displacement vector field (DVF) and the closest DVF that the PCA model can produce. This was done by taking the inner product of the DVF with the PCA components from the model. As a global measure of error, the 90th percentile of the residual errors (Mres90) across the whole scan was used. As proof of principle, we demonstrated this approach on both patient-specific cases and a population-based PCA in head and neck (H&N) cancer patients. These models were created using deformation data from deformable registrations between the planning computed tomography and cone-beam computed tomography (CBCTs), and were evaluated against DVFs from registrations of CBCTs not used to create the model. Results: For our example cases, the oropharyngeal and the nasal cavity regions showed the largest local residual error, indicating the PCA models struggle to predict deformations seen in these regions. Mres90 ranged from 0.4 mm to 6.3 mm across the different models. Conclusions: A method to quantitatively evaluate how well PCA models represent observed anatomical changes was proposed. We demonstrated our approach on H&N PCA models, but it can be applied to other sites.

Purpose: Several efforts are being undertaken toward MRI-based treatment planning for ocular proton therapy for uveal melanoma (UM). The interobserver variability of the gross target volume (GTV) on magnetic resonance imaging (MRI) is one of the important parameters to design safety margins for a reliable treatment. Therefore, this study assessed the interobserver variation in GTV delineation of UM on MRI. Methods and Materials: Six observers delineated the GTV in 10 different patients using the Big Brother contouring software. Patients were scanned at 3T MRI with a surface coil, and tumors were delineated separately on contrast enhanced 3DT1 (T1gd) and 3DT2-weighted scans with an isotropic acquisition resolution of 0.8 mm. Volume difference and overall local variation (median standard deviation of the distance between the delineated contours and the median contour) were analyzed for each GTV. Additionally, the local variation was analyzed for 4 interfaces: sclera, vitreous, retinal detachment, and tumor-choroid interface. Results: The average GTV was significantly larger on T1gd (0.57cm3) compared with T2 (0.51cm3, P = .01). A not significant higher interobserver variation was found on T1gd (0.41 mm) compared with T2 (0.35 mm). The largest variations were found at the tumor-choroid interface due to peritumoral enhancement (T1gd, 0.62 mm; T2, 0.52 mm). As a result, a larger part of this tumor-choroid interface appeared to be included on T1gd-based GTVs compared with T2, explaining the smaller volumes on T2. Conclusions: The interobserver variation of 0.4 mm on MRI are low with respect to the voxel size of 0.8 mm, enabling small treatment margins. We recommend delineation based on the T1gd-weighted scans, as choroidal tumor extensions might be missed.

Background and purpose: For lung cancer patients treated with radiotherapy, radiation dose to the heart has been associated with overall survival, with volumetric dose statistics widely presented. However, critical cardiac structures are present on the hearts surface, where this approach may be sub-optimal. In this work we present a methodology for creating cardiac surface dose maps and identify regions where excess dose is associated with in worse overall survival. Material and methods: A modified cylindrical coordinate system was implemented to map the cardiac surface dose for lung cancer patients. Validation was performed by mapping the cardiac chambers for 55 patients, fitting a point spread function (PSF) to the blurred edge. To account for this uncertainty, dose maps were blurred by a 2D-Gaussian with width described by the PSF. Permutation testing identified regions where excess dose was associated with worse patient survival. The 99th percentile of the max t-value then defined a cardiac surface region to extract dose, from each patient, to be analysed in a multivariable cox-proportional hazards survival model. Results: Cardiac surface maps were created for 648 lung cancer patients. Cardiac surface dose maps were blurred with a 2D- Gaussian filter of size σφ = 4.3° and σy = 1.3units to account for mapping uncertainties. Permutation testing identified significant differences across the surface of the right atria, p

Objective: Radioimmunotherapy (RIT) is a systemic therapy currently used in the treatment of patients with lymphoma. RIT complexes consist of a targeting molecule, commonly an antibody, radionuclide chelates and a linker which can be a nanoparticle platform. Nanoparticles facilitate the attachment of multiple radionuclides and targeting groups to a single complex. Here the target affinity, duration of target association and inhibition of colony formation of Cetuximab-resistant tumour cells with Cetuximab-targeted [177Lu]-AuNPs were investigated. Dose distribution in xenografts derived from EGFR-overexpressing cells was also determined. Methods: Cetuximab-targeted [177Lu]-AuNPs were generated by functionalising 15nm AuNPs with the chelator DOTA and Cetuximab and radiolabelling with 177LuCl3. KDis, a measure of affinity, was determined by competitive binding to EGFR expressing cells. Radio-sensitivity was determined in EGFR expressing tumour cells including the Cetuximab resistant cell line HCT116 using a colony formation assay. Dose distribution was measured in sections from xenografts grown in nude mice using autoradiography. Results: KDis for the complex binding to EGFR on MDA-MB-468 cells was 20 nM. Loss of cell associated [177Lu] activity was biphasic with loss of about 50% of activity in about 4 h. Remaining activity dissociated over a period of about 4 days. HCT8 and MDA-MB-468, but not HCT116 cells were sensitive to the growth inhibitory effect of Cetuximab. However, treatment with Cetuximab-targeted [177Lu]-AuNPs inhibited colony formation in all 3 cell lines. Dose distribution across sections from xenografts was found to demonstrate a co-efficient of variation of 15%. Conclusion: Cetuximab-targeted [177Lu]-AuNPs demonstrate high affinity for EGFR and could be an effective treatment for Cetuximab-resistant colorectal cancer cells. A strategy involving pre-treatment with receptor targeted[177Lu] to improve RIT therapeutic ratios has the potential to enhance clinical outcomes.

Background/purpose: Intensity-modulated proton therapy (IMPT) dose distributions can be severely degraded in targets moving with respiration due to the interplay, range and blurring effects. In this study we investigated the joint and disentangled impact of these effects as a function of the motion amplitudes. Materials/methods: Single-fraction time-resolved proton treatment delivery was simulated using an in-house developed 4D-motion simulation platform. The respiratory induced anatomical changes were described by deformation vector fields (DVF) derived from 4D-Computed Tomography (4D-CT) scans scaled to different motion amplitudes. Based on the individual spots exported from IMPT plans for 10 lung cancer patients, three dose distributions with different combinations of motion effects were generated. The doses were subtracted from each other to study the separated impact of individual effects over the planning target volume (PTV). The results were evaluated using univariate and multivariate regression models including amplitude, tumour size and location. Results: The interplay effect led to an average dose error of 7% for motion amplitude of 20 mm, whereas range and blurring effects were smaller at 2.6% and 2.5%, respectively. These effects increased linear-quadratically with amplitude and were significantly associated with tumour volume or location. Conclusion: Single-fraction dose variations due to interplay effects dominate other respiratory-induced variations for a large range of motion amplitudes. Keywords: IMPT, Respiratory motion, Interplay, Range, Blurring, Dose errors