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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: 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

Brain tumours make up nearly one-third of paediatric malignancies. Over time, advancements in oncological treatments like radiotherapy have helped reduce normal-tissue toxicity when treating cancers in the brain. However, clinicians are still facing a trade-off between treatment efficacy and potential side effects. The aim of this review is to address the late effects of cranial irradiation on the neuroendocrine system and to identify factors that make patients more vulnerable to radiation-induced endocrine sequelae. Radiation damage to the hypothalamic–pituitary axis, which orchestrates hormone release, can lead to endocrinopathy; up to 48.8% of children who have undergone cranial irradiation develop a hormone deficiency. This may lead to further health complications that can appear up to decades after the last treatment, lowering the patients’ quality of life and increasing long-term costs as lifelong hormone replacement therapy may be required. Growth hormone deficiency is the most common sequelae, followed by either thyroid or gonadotropic hormone deficiency. Adrenocorticotropic hormone deficiency tends to be the least common. Identified factors that increase the risk of late endocrine deficiency include total radiation dose, age at treatment, and time since last treatment. However, as there are various other factors that may potentiate the damage, a universal solution proven to be most effective in sparing the endocrine tissues is yet to be identified. Until then, accounting for the identified risk factors during treatment planning may in some cases help reduce the development of endocrine sequelae in childhood cancer survivors.

BackgroundStereotactic arrhythmia radioablation (STAR) is a potential new therapy for patients with refractory ventricular tachycardia (VT). The arrhythmogenic substrate (target) is synthesized from clinical and electro-anatomical information. This study was designed to evaluate the baseline interobserver variability in target delineation for STAR.MethodsDelineation software designed for research purposes was used. The study was split into three phases. Firstly, electrophysiologists delineated a well-defined structure in three patients (spinal canal). Secondly, observers delineated the VT-target in three patients based on case descriptions. To evaluate baseline performance, a basic workflow approach was used, no advanced techniques were allowed. Thirdly, observers delineated three predefined segments from the 17-segment model. Interobserver variability was evaluated by assessing volumes, variation in distance to the median volume expressed by the root-mean-square of the standard deviation (RMS-SD) over the target volume, and the Dice-coefficient.ResultsTen electrophysiologists completed the study. For the first phase interobserver variability was low as indicated by low variation in distance to the median volume (RMS-SD range: 0.02–0.02 cm) and high Dice-coefficients (mean: 0.97 ± 0.01). In the second phase distance to the median volume was large (RMS-SD range: 0.52–1.02 cm) and the Dice-coefficients low (mean: 0.40 ± 0.15). In the third phase, similar results were observed (RMS-SD range: 0.51–1.55 cm, Dice-coefficient mean: 0.31 ± 0.21).ConclusionsInterobserver variability is high for manual delineation of the VT-target and ventricular segments. This evaluation of the baseline observer variation shows that there is a need for methods and tools to improve variability and allows for future comparison of interventions aiming to reduce observer variation, for STAR but possibly also for catheter ablation.

PurposeFor patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer.Methods199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90th percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported.ResultsLocal relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation).ConclusionIn these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.

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.

BackgroundOne in three high-risk prostate cancer patients treated with radiotherapy recur. Detection of lymph node metastasis and microscopic disease spread using conventional imaging is poor, and many patients are under-treated due to suboptimal seminal vesicle or lymph node irradiation. We use Image Based Data Mining (IBDM) to investigate association between dose distributions, and prognostic variables and biochemical recurrence (BCR) in prostate cancer patients treated with radiotherapy. We further test whether including dose information in risk-stratification models improves performance.MethodPlanning CTs, dose distributions and clinical information were collected for 612 high-risk prostate cancer patients treated with conformal hypo-fractionated radiotherapy, intensity modulated radiotherapy (IMRT), or IMRT plus a single fraction high dose rate (HDR) brachytherapy boost. Dose distributions (including HDR boost) of all studied patients were mapped to a reference anatomy using the prostate delineations. Regions where dose distributions significantly differed between patients that did and did-not experience BCR were assessed voxel-wise using 1) a binary endpoint of BCR at four-years (dose only) and 2) Cox-IBDM (dose and prognostic variables). Regions where dose was associated with outcome were identified. Cox proportional-hazard models with and without region dose information were produced and the Akaike Information Criterion (AIC) was used to assess model performance.ResultsNo significant regions were observed for patients treated with hypo-fractionated radiotherapy or IMRT. Regions outside the target where higher dose was associated with lower BCR were observed for patients treated with brachytherapy boost. Cox-IBDM revealed that dose response was influenced by age and T-stage. A region at the seminal vesicle tips was identified in binary- and Cox-IBDM. Including the mean dose in this region in a risk-stratification model (hazard ratio=0.84, p=0.005) significantly reduced AIC values (p=0.019), indicating superior performance, compared with prognostic variables only. The region dose was lower in the brachytherapy boost patients compared with the external beam cohorts supporting the occurrence of marginal misses.ConclusionAssociation was identified between BCR and dose outside of the target region in high-risk prostate cancer patients treated with IMRT plus brachytherapy boost. We show, for the first-time, that the importance of irradiating this region is linked to prognostic variables.

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: 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.

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.

BackgroundThere is increasing evidence of cardiac toxicity of thoracic radiotherapy however, it is difficult to draw conclusions on cardiac dose constraints due to the heterogeneity of published studies. Moreover, few studies record data on cause of death. The aim of this paper is to investigate the relationship between conventional cardiac dosimetric parameters and death with cardiac causes using data from the UK national cause of death registry.MethodsData on cancer diagnosis, treatment and cause of death following radical lung cancer radiotherapy were obtained from Public Health England for all patients treated at the Christie NHS Foundation Trust between 1/1/10 and 31/12/16. Individuals with metastatic disease and those who received multiple courses of thoracic radiotherapy where excluded. All patients who received > 45Gy in 20 fractions were included. Cardiac cause of death was defined as the following ICD-10 codes on death certificate: I20-I25; I30-I32; I34-I37; I40-I52. Cardiac V5Gy, V30Gy, V50Gy and mean heart dose (MHD) were extracted. Cumulative incidence of death with cardiac causes were plotted for each cardiac dosimetric parameter. Multi-variable Fine and Gray competing risk analysis was used to model predictors for cardiac death with non-cardiac death as a competing risk.ResultsCardiac dosimetric parameters were available for 967 individuals, 110 died with a cardiac cause (11.4%). Patients with a cardiac comorbidity had an increased risk of death with a cardiac cause compared with those without a cardiac comorbidity (2-year cumulative incidence 21.3% v 6.2%, p

Radiation-induced heart disease (RIHD) is a recent concern in patients with lung cancer after being treated with radiotherapy. Most of information we have in the field of cardiac toxicity comes from studies utilizing real-world data (RWD) as randomized controlled trials (RCTs) are generally not practical in this field. This article is a narrative review of the literature using RWD to study RIHD in patients with lung cancer following radiotherapy, summarizing heart dosimetric factors associated with outcome, strength, and limitations of the RWD studies, and how RWD can be used to assess a change to cardiac dose constraints.

Abstract Background The primary aim was to test the hypothesis that deriving pre-treatment 3D magnetic resonance tumour volume (mrTV) quantification improves performance characteristics for the prediction of loco-regional failure compared with standard maximal tumour diameter (1D) assessment in patients with squamous cell carcinoma of the anus undergoing chemoradiotherapy. Methods We performed an early evaluation case-control study at two UK centres (2007–2014) in 39 patients with loco-regional failure (cases), and 41 patients disease-free at 3 years (controls). mrTV was determined using the summation of areas method (Volsum). Reproducibility was assessed using intraclass concordance correlation (ICC) and Bland-Altman limits of agreements. We derived receiver operating curves using logistic regression models and expressed accuracy as area under the curve (ROCAUC). Results The median time per patient for Volsum quantification was 7.00 (inter-quartile range, IQR: 0.57–12.48) minutes. Intra and inter-observer reproducibilities were generally good (ICCs from 0.79 to 0.89) but with wide limits of agreement (intra-observer: − 28 to 31%; inter-observer: − 28 to 46%). Median mrTVs were greater for cases (32.6 IQR: 21.5–53.1 cm3) than controls (9.9 IQR: 5.7–18.1 cm3, p

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

PurposeLower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location.MethodsDose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models.ResultsDose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose.ConclusionsPatients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data.

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.

Abstract Background Thoracic and head and neck cancer radiation therapy (RT) can cause damage to nearby healthy organs such as the esophagus, causing acute radiation-induced esophageal damage (ARIED). A non-invasive method to detect and monitor ARIED can facilitate optimizing RT to avoid ARIED while improving local tumor control. Current clinical guidelines are limited to scoring the esophageal damage based on the symptoms of patients. Magnetic resonance imaging (MRI) is a non-invasive imaging modality that may potentially visualize radiation-induced organ damage. We investigated the feasibility of using T2-weighted MRI to detect and monitor ARIED using a two-phased study in mice. Methods The first phase aimed to establish the optimal dose level at which ARIED is inducible and to determine the time points where ARIED is detectable. Twenty four mice received a single dose delivery of 20 and 40 Gy at proximal and distal spots of 10.0 mm (in diameter) on the esophagus. Mice underwent MRI and histopathology analysis with esophageal resection at two, three, and 4 weeks post-irradiation, or earlier in case mice had to be euthanized due to humane endpoints. In the second phase, 32 mice received a 40 Gy single dose and were studied at two, three, and 7 days post-irradiation. We detected ARIED as a change in signal intensity of the MRI images. We measured the width of the hyperintense area around the esophagus in all mice that underwent MRI prior to and after irradiation. We conducted a blind qualitative comparison between MRI findings and histopathology as the gold standard. Results/conclusions A dose of 40 Gy was needed to induce substantial ARIED. MRI detected ARIED as high signal intensity, visible from 2 days post-irradiation. Quantitative MRI analysis showed that the hyperintense area around the esophagus with severe ARIED was 1.41 mm wider than with no damage and MRI-only mice. The overall sensitivity and specificity were 56 and 43% respectively to detect any form of ARIED. However, in this study MRI correctly detected 100% of severe ARIED cases. Our two-phased preclinical study showed that MRI has the potential to detect ARIED as a change in signal intensity and width of enhancement around the esophagus.

Image Based Data Mining (IBDM) is a novel analysis technique allowing the interrogation of large amounts of routine radiotherapy data. Using this technique, unexpected correlations have been identified between dose close to the prostate and biochemical relapse, and between dose to the base of the heart and survival in lung cancer. However, most analyses to date have considered only dose when identifying a region of interest, with confounding variables accounted for post-hoc, most often using a multivariate Cox regression. In this work, we introduce a novel method to account for confounding variables directly in the analysis, by performing a Cox regression in every voxel of the dose distribution, and apply it in the analysis of a large cohort of lung cancer patients. Our method produces three-dimensional maps of hazard for clinical variables, accounting for dose at each spatial location in the patient. Results confirm that a region of interest exists in the base of the heart where those patients with poor performance status (PS), PS > 1, have a stronger adverse reaction to incidental dose, but that the effect changes when considering other clinical variables, with patient age becoming dominant. Analyses such as this will help shape future clinical trials in which hypotheses generated by the analysis will be tested.

To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.

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

PURPOSE:During resections of brain tumors, neurosurgeons have to weigh the risk between residual tumor and damage to brain functions. Different perspectives on these risks result in practice variation. We present statistical methods to localize differences in extent of resection between institutions which should enable to reveal brain regions affected by such practice variation. METHODS:Synthetic data were generated by simulating spheres for brain, tumors, resection cavities, and an effect region in which a likelihood of surgical avoidance could be varied between institutions. Three statistical methods were investigated: a non-parametric permutation based approach, Fisher's exact test, and a full Bayesian Markov chain Monte Carlo (MCMC) model. For all three methods the false discovery rate (FDR) was determined as a function of the cut-off value for the q-value or the highest density interval, and receiver operating characteristic and precision recall curves were created. Sensitivity to variations in the parameters of the synthetic model were investigated. Finally, all these methods were applied to retrospectively collected data of 77 brain tumor resections in two academic hospitals. RESULTS:Fisher's method provided an accurate estimation of observed FDR in the synthetic data, whereas the permutation approach was too liberal and underestimated FDR. AUC values were similar for Fisher and Bayes methods, and superior to the permutation approach. Fisher's method deteriorated and became too liberal for reduced tumor size, a smaller size of the effect region, a lower overall extent of resection, fewer patients per cohort, and a smaller discrepancy in surgical avoidance probabilities between the different surgical practices. In the retrospective patient data, all three methods identified a similar effect region, with lower estimated FDR in Fisher's method than using the permutation method. CONCLUSIONS:Differences in surgical practice may be detected using voxel statistics. Fisher's test provides a fast method to localize differences but could underestimate true FDR. Bayesian MCMC is more flexible and easily extendable, and leads to similar results, but at increased computational cost.

ObjectiveThe objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).Materials and methodsFAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).ResultsWith only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.ConclusionsFASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.

Background: Changes in prostate cancer apparent diffusion coefficient (ADC) derived from diffusion-weighted magnetic resonance imaging (MRI) provide a noninvasive method for assessing radiotherapy response. This may be attenuated by neoadjuvant hormone therapy (NA-HT). We investigate ADC values measured before, during and after external beam radiotherapy (EBRT) following NA-HT. Methods: Patients with ⩾T2c biopsy-proven prostate cancer receiving 3 months of NA-HT plus definitive radiotherapy were prospectively identified. All underwent ADC-MRI scans in the week before EBRT, in the third week of EBRT and 8 weeks after its completion. Imaging was performed at 1.5 T. The tumour, peripheral zone (PZ) and central zone (CZ) of the prostate gland were identified and median ADC calculated for each region and time point. Results: Between September and December 2014, 15 patients were enrolled (median age 68.3, range 57–78) with a median Gleason score of 7 (6–9) and prostate-specific antigen (PSA) at diagnosis 14 (3–197) ng/ml. Median period of NA-HT prior to first imaging was 96 days (69–115). All patients completed treatment. Median follow up was 25 months (7–34), with one patient relapsing in this time. Thirteen patients completed all imaging as intended, one withdrew after one scan and another missed the final imaging. PZ and CZ could not be identified in one patient. Median tumour ADC before, during and post radiotherapy was 1.24 × 10 −3 mm 2 /s (interquartile range 0.16 × 10 −3 mm 2 /s), 1.31 × 10 −3 mm 2 /s (0.22 × 10 −3 mm 2 /s), then 1.32 × 10 −3 mm 2 /s (0.13 × 10 −3 mm 2 /s) respectively ( p > 0.05). There was no significant difference between median tumour and PZ or CZ ADC at any point. Gleason score did not correlate with ADC values. Conclusions: Differences in ADC parameters of normal and malignant tissue during EBRT appear attenuated by prior NA-HT. The use of changes in ADC as a predictive tool in this group may have limited utility.

PURPOSE:Precise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations. MATERIALS AND METHODS:This study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions. RESULTS:For the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible. DISCUSSION/CONCLUSION:A novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.