Simulation as a tool to model potential workflow enhancements in radiotherapy treatment pathways - A systematic review

This systematic review aimed to synthesize and summarize the use of simulation of radiotherapy pathways. The objective was to establish the suitability of those simulations in modeling the potential introduction of processes and technologies to speed up radiotherapy pathways. A systematic literature search was carried out using PubMed and Scopus databases to evaluate the use of simulation in radiotherapy pathways. Full journal articles and conference proceedings were considered, and the search was limited to the English language only. To be eligible for inclusion, articles had to model multiple sequential processes in the radiotherapy pathway concurrently to demonstrate the suitability of simulation modeling in typical pathways. Papers solely modeling scheduling, capacity, or queuing strategies were excluded. In total, 151 potential studies were identified and screened to find 18 relevant studies in October 2022. Studies showed that various pathways could be modeled, including the entire pathway from referral to end of treatment or the constituent phases such as pre-treatment, treatment, or other subcomponents. The data required to generate models varied from study to study, but at least 3 months of data were needed. This review demonstrates that modeling and simulation of radiotherapy pathways are feasible and that model output matches real-world systems. Validated models give researchers confidence to modify models with potential workflow enhancements to assess their potential effect on real-world systems. It is recommended that researchers follow best practice guidelines when building models to ensure that they are fit for purpose and to enable decision makers to have confidence in their results.

Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients

BACKGROUND AND PURPOSE

This study aims to build machine learning models to predict radiation-induced rectal toxicities for three clinical endpoints and explore whether the inclusion of radiomic features calculated on radiotherapy planning computerised tomography (CT) scans combined with dosimetric features can enhance the prediction performance.

MATERIALS AND METHODS

183 patients recruited to the VoxTox study (UK-CRN-ID-13716) were included. Toxicity scores were prospectively collected after 2 years with grade ≥ 1 proctitis, haemorrhage (CTCAEv4.03); and gastrointestinal (GI) toxicity (RTOG) recorded as the endpoints of interest. The rectal wall on each slice was divided into 4 regions according to the centroid, and all slices were divided into 4 sections to calculate region-level radiomic and dosimetric features. The patients were split into a training set (75%, N = 137) and a test set (25%, N = 46). Highly correlated features were removed using four feature selection methods. Individual radiomic or dosimetric or combined (radiomic + dosimetric) features were subsequently classified using three machine learning classifiers to explore their association with these radiation-induced rectal toxicities.

RESULTS

The test set area under the curve (AUC) values were 0.549, 0.741 and 0.669 for proctitis, haemorrhage and GI toxicity prediction using radiomic combined with dosimetric features. The AUC value reached 0.747 for the ensembled radiomic-dosimetric model for haemorrhage.

CONCLUSIONS

Our preliminary results show that region-level pre-treatment planning CT radiomic features have the potential to predict radiation-induced rectal toxicities for prostate cancer. Moreover, when combined with region-level dosimetric features and using ensemble learning, the model prediction performance slightly improved.

Sub-regional analysis of the parotid glands: model development for predicting late xerostomia with radiomics features in head and neck cancer patients

BACKGROUND

The irradiation of sub-regions of the parotid has been linked to xerostomia development in patients with head and neck cancer (HNC). In this study, we compared the xerostomia classification performance of radiomics features calculated on clinically relevant and de novo sub-regions of the parotid glands of HNC patients.

MATERIAL AND METHODS

All patients (N = 117) were treated with TomoTherapy in 30-35 fractions of 2-2.167 Gy per fraction with daily mega-voltage-CT (MVCT) acquisition for image-guidance purposes. Radiomics features (N = 123) were extracted from daily MVCTs for the whole parotid gland and nine sub-regions. The changes in feature values after each complete week of treatment were considered as predictors of xerostomia (CTCAEv4.03, grade ≥ 2) at 6 and 12 months. Combinations of predictors were generated following the removal of statistically redundant information and stepwise selection. The classification performance of the logistic regression models was evaluated on train and test sets of patients using the Area Under the Curve (AUC) associated with the different sub-regions at each week of treatment and benchmarked with the performance of models solely using dose and toxicity at baseline.

RESULTS

In this study, radiomics-based models predicted xerostomia better than standard clinical predictors. Models combining dose to the parotid and xerostomia scores at baseline yielded an AUCtest of 0.63 and 0.61 for xerostomia prediction at 6 and 12 months after radiotherapy while models based on radiomics features extracted from the whole parotid yielded a maximum AUCtest of 0.67 and 0.75, respectively. Overall, across sub-regions, maximum AUCtest was 0.76 and 0.80 for xerostomia prediction at 6 and 12 months. Within the first two weeks of treatment, the cranial part of the parotid systematically yielded the highest AUCtest.

CONCLUSION

Our results indicate that variations of radiomics features calculated on sub-regions of the parotid glands can lead to earlier and improved prediction of xerostomia in HNC patients.

An Update to the Malthus Model for Radiotherapy Utilisation in England

AIMS

The Malthus Programme predicts national and local radiotherapy demand by combining cancer incidence data with decision trees detailing the indications, and appropriate dose fractionation, for radiotherapy. Since the last model update in 2017, technological advancements and the COVID-19 pandemic have led to increasing hypofractionation of radiotherapy schedules. Indications for radiotherapy have also evolved, particularly in the context of oligometastatic disease. Here we present a brief update on the model for 2021. We have updated the decision trees for breast, prostate, lung and head and neck cancers, and incorporated recent cancer incidence data into our model, generating a current estimate of fraction demand for these four cancer sites across England.

MATERIALS AND METHODS

The decision tree update was based on evidence from practice-changing randomised controlled trials, published guidelines, audit data and expert opinion. Site- and stage-specific incidence data were taken from the National Disease Registration Service. We used the updated model to estimate the proportion of patients who would receive radiotherapy (appropriate rate of radiotherapy) and the fraction demand per million population at a national and Clinical Commissioning Group level in 2021.

RESULTS

The total predicted fraction demand has decreased by 11.4% across all four cancer sites in our new model, compared with the 2017 version. This reduction can be explained primarily by greater use of hypofractionated treatments (including stereotactic ablative radiotherapy) and a shift towards earlier stage presentation. The only large change in appropriate rate of radiotherapy was an absolute decrease of 3% for lung cancer.

CONCLUSIONS

Compared with our previous model, the current version predicts a reduction in fraction demand across England. This is driven principally by hypofractionation of radiotherapy regimens, using technology that requires increasingly complex planning. Treatment complexity and local service factors need to be taken into account when translating fraction burden into linear accelerator demand or throughput.

Assessing the generalisability of radiomics features previously identified as predictive of radiation-induced sticky saliva and xerostomia

Background and purpose

While core to the scientific approach, reproducibility of experimental results is challenging in radiomics studies. A recent publication identified radiomics features that are predictive of late irradiation-induced toxicity in head and neck cancer (HNC) patients. In this study, we assessed the generalisability of these findings.

Materials and Methods

The procedure described in the publication in question was applied to a cohort of 109 HNC patients treated with 50-70 Gy in 20-35 fractions using helical radiotherapy although there were inherent differences between the two patient populations and methodologies. On each slice of the planning CT with delineated parotid and submandibular glands, the imaging features that were previously identified as predictive of moderate-to-severe xerostomia and sticky saliva 12 months post radiotherapy (Xer12m and SS12m) were calculated. Specifically, Short Run Emphasis (SRE) and maximum CT intensity (maxHU) were evaluated for improvement in prediction of Xer12m and SS12m respectively, compared to models solely using baseline toxicity and mean dose to the salivary glands.

Results

None of the associations previously identified as statistically significant and involving radiomics features in univariate or multivariate models could be reproduced on our cohort.

Conclusion

The discrepancies observed between the results of the two studies delineate limits to the generalisability of the previously reported findings. This may be explained by the differences in the approaches, in particular the imaging characteristics and subsequent methodological implementation. This highlights the importance of external validation, high quality reporting guidelines and standardisation protocols to ensure generalisability, replication and ultimately clinical implementation.

Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features

Background and purpose

The images acquired during radiotherapy for image-guidance purposes could be used to monitor patient-specific response to irradiation and improve treatment personalisation. We investigated whether the kinetics of radiomics features from daily mega-voltage CT image-guidance scans (MVCT) improve prediction of moderate-to-severe xerostomia compared to dose/volume parameters in radiotherapy of head-and-neck cancer (HNC).

Materials and Methods

All included HNC patients (N = 117) received 30 or more fractions of radiotherapy with daily MVCTs. Radiomics features were calculated on the contra-lateral parotid glands of daily MVCTs. Their variations over time after each complete week of treatment were used to predict moderate-to-severe xerostomia (CTCAEv4.03 grade ≥ 2) at 6, 12 and 24 months post-radiotherapy. After dimensionality reduction, backward/forward selection was used to generate combinations of predictors.Three types of logistic regression model were generated for each follow-up time: 1) a pre-treatment reference model using dose/volume parameters, 2) a combination of dose/volume and radiomics-based predictors, and 3) radiomics-based predictors. The models were internally validated by cross-validation and bootstrapping and their performance evaluated using Area Under the Curve (AUC) on separate training and testing sets.

Results

Moderate-to-severe xerostomia was reported by 46 %, 33 % and 26 % of the patients at 6, 12 and 24 months respectively. The selected models using radiomics-based features extracted at or before mid-treatment outperformed the dose-based models with an AUCtrain/AUCtest of 0.70/0.69, 0.76/0.74, 0.86/0.86 at 6, 12 and 24 months, respectively.

Conclusion

Our results suggest that radiomics features calculated on MVCTs from the first half of the radiotherapy course improve prediction of moderate-to-severe xerostomia in HNC patients compared to a dose-based pre-treatment model.

Machine Learning for Auto-Segmentation in Radiotherapy Planning

Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter- and intra-observer variability. Automatic segmentation (auto-segmentation), using computer algorithms, seeks to address these issues. Advances in machine learning and computer vision have led to the development of methods for accurate and efficient auto-segmentation. This review surveys auto-segmentation techniques and applications in radiotherapy planning. It provides an overview of traditional approaches to auto-segmentation, including intensity analysis, shape modelling and atlas-based methods. The focus, though, is on uses of machine learning and deep learning, including convolutional neural networks. Finally, the future of machine-learning-driven auto-segmentation in clinical settings is considered, and the barriers that must be overcome for it to be widely accepted into routine practice are highlighted.

Variations in Demand across England for the Magnetic Resonance-Linac Technology, Simulated Utilising Local-level Demographic and Cancer Data in the Malthus Project

AIMS

Cancer incidence varies across England, which affects the local-level demand for treatments. The magnetic resonance-linac (MR-linac) is a new radiotherapy technology that combines imaging and treatment. Here we model the demand and demand variations for the MR-linac across England.

MATERIALS AND METHODS

Initial clinical indications were provided by the MR-linac consortium and introduced into the Malthus radiotherapy clinical decision trees. The Malthus model contains Clinical Commissioning Group (CCG) population, cancer incidence and stage presentation data (for lung and prostate) and simulated the demand for the MR-linac for all CCGs and Radiotherapy Operational Delivery Networks (RODN) across England.

RESULTS

Based on the initial target clinical indications, the MR-linac could service 16% of England's fraction burden. The simulated fractions/million population demand/annum varies between 3000 and 10 600 fractions/million at the CCG level. Focussing only on the cancer population, the simulated fractions/1000 cancer cases demand/annum ranges from 1028 to 1195 fractions/1000 cases. If a national average for fractions/million demand was then used, at the RODN level, the variation from actual annual demand ranges from an overestimation of 8400 fractions to an underestimation of 5800 fractions. When using the national average fractions/1000 cases, the RODN demand varies from an overestimation of 3200 fractions to an underestimation of 3000 fractions.

CONCLUSIONS

Planning cancer services is complex due to regional variations in cancer burden. The variations in simulated demand of the MR-linac highlight the requirement to use local-level data when planning to introduce a new technology.

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

INTRODUCTION

Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.

METHODS

A retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction.

RESULTS

The optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3.

CONCLUSIONS

VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.

Multi-scale segmentation in GBM treatment using diffusion tensor imaging

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps

OBJECTIVES

Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes.

METHODS

Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges.

RESULTS

The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation.

CONCLUSION

Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians.

ADVANCES IN KNOWLEDGE

This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.

Associations between voxel-level accumulated dose and rectal toxicity in prostate radiotherapy

Background and Purpose

Associations between dose and rectal toxicity in prostate radiotherapy are generally poorly understood. Evaluating spatial dose distributions to the rectal wall (RW) may lead to improvements in dose-toxicity modelling by incorporating geometric information, masked by dose-volume histograms. Furthermore, predictive power may be strengthened by incorporating the effects of interfraction motion into delivered dose calculations.Here we interrogate 3D dose distributions for patients with and without toxicity to identify rectal subregions at risk (SRR), and compare the discriminatory ability of planned and delivered dose.

Material and Methods

Daily delivered dose to the rectum was calculated using image guidance scans, and accumulated at the voxel level using biomechanical finite element modelling. SRRs were statistically determined for rectal bleeding, proctitis, faecal incontinence and stool frequency from a training set (n = 139), and tested on a validation set (n = 47).

Results

SRR patterns differed per endpoint. Analysing dose to SRRs improved discriminative ability with respect to the full RW for three of four endpoints. Training set AUC and OR analysis produced stronger toxicity associations from accumulated dose than planned dose. For rectal bleeding in particular, accumulated dose to the SRR (AUC 0.76) improved upon dose-toxicity associations derived from planned dose to the RW (AUC 0.63). However, validation results could not be considered significant.

Conclusions

Voxel-level analysis of dose to the RW revealed SRRs associated with rectal toxicity, suggesting non-homogeneous intra-organ radiosensitivity. Incorporating spatial features of accumulated delivered dose improved dose-toxicity associations. This may be an important tool for adaptive radiotherapy in the future.

Enhanced magnetic and room temperature intrinsic magnetodielectric effect in Mn modified Ba2Mg2Fe12O22 Y-type hexaferrite

We have reported a systematic investigation on structural, magnetic, magnetodielectric and magnetoimpedance characteristics of Y-type Ba₂Mg₂(Fe_1-x Mn _x )₁₂O₂₂ (0  ⩽  x  ⩽  0.12) hexaferrite synthesized by solid-state reaction route. Rietveld refinement of x-ray diffraction pattern confirms the phase purity of all the samples with rhombohedral crystal structure. The Mn dopant modulates not only superexchange angle near to the boundary of magnetic blocks but also magnetic transition temperature. Temperature-dependent magnetization data suggests that due to Mn doping at Fe sites, ferrimagnetic to proper screw transition temperature (T _II) increases from 190 K to 208 K, while there is a decrease in proper screw to longitudinal conical spin transition temperature (T _I) from 35 K to 25 K. We observe remarkable decrease in the magnetic field from 20 kOe to 12 kOe to produce intermediate spin ordering from ferrimagnetic ordering which can be understood because of modification of superexchange angle due to Mn doping. The value of loss tangent decreases with increasing doping concentration at 300K, i.e. ~60% and 180% in BMFM4 (x  =  0.04) and BMFM8 (x  =  0.08) respectively as compared to BMF, suggesting the evolution of intrinsic feature in the doped samples. Magnetodielectric (MD) effect shows that in the low-frequency regime, the robust MD effect is because of Maxwell-Wagner interfacial polarization, whereas in the high-frequency regime intrinsic effect dominates. Further, magnetoimpedance measurement confirms the presence of substantial intrinsic MD% (~6%) at 1.3 T applied field at 300 K for 4% Mn-doped sample. Finally, the nature and strength of magnetoelectric coupling in BMFM4 and BMFM8 samples at 300 K is found to be biquadratic (P ² M ²) and maximum strength of coupling is 3.09  ×  10^-4 emu² g^-2 and 2.34  ×  10^-4 emu² g^-2, respectively.

Versatile magnetic microdiscs for the radio enhancement and mechanical disruption of glioblastoma cancer cells

This study describes the use of highly versatile, lithographically defined magnetic microdiscs. Gold covered magnetic microdiscs are used in both radiosensitizing cancer cells, acting as intracellular emitters of secondary electrons during radiotherapy, and as well as inducing mechanical damage by exerting a mechanical torque when exposed to a rotating magnetic field. This study reveals that lithographically defined microdiscs with a uniform size of 2 microns in diameter highly increase the DNA damage and reduce the glioblastoma colony formation potential compared to conventional radiation therapy. Furthermore, the addition of mechanical disruption mediated by the magnetic component of the discs increased the efficiency of brain cancer cell killing.

First study demonstrating the use of physically engineered magnetic particles that display two functionalities for cancer treatment.

A Phase I Study of Pegylated Arginine Deiminase (Pegargiminase), Cisplatin, and Pemetrexed in Argininosuccinate Synthetase 1-Deficient Recurrent High-grade Glioma

PURPOSE

Patients with recurrent high-grade gliomas (HGG) are usually managed with alkylating chemotherapy ± bevacizumab. However, prognosis remains very poor. Preclinically, we showed that HGGs are a target for arginine depletion with pegargiminase (ADI-PEG20) due to epimutations of argininosuccinate synthetase (ASS1) and/or argininosuccinate lyase (ASL). Moreover, ADI-PEG20 disrupts pyrimidine pools in ASS1-deficient HGGs, thereby impacting sensitivity to the antifolate, pemetrexed.

PATIENTS AND METHODS

We expanded a phase I trial of ADI-PEG20 with pemetrexed and cisplatin (ADIPEMCIS) to patients with ASS1-deficient recurrent HGGs (NCT02029690). Patients were enrolled (01/16-06/17) to receive weekly ADI-PEG20 36 mg/m² intramuscularly plus pemetrexed 500 mg/m² and cisplatin 75 mg/m² intravenously once every 3 weeks for up to 6 cycles. Patients with disease control were allowed ADI-PEG20 maintenance. The primary endpoints were safety, tolerability, and preliminary estimates of efficacy.

RESULTS

Ten ASS1-deficient heavily pretreated patients were treated with ADIPEMCIS therapy. Treatment was well tolerated with the majority of adverse events being Common Terminology Criteria for Adverse Events v4.03 grade 1-2. The best overall response was stable disease in 8 patients (80%). Plasma arginine was suppressed significantly below baseline with a reciprocal increase in citrulline during the sampling period. The anti-ADI-PEG20 antibody titer rose during the first 4 weeks of treatment before reaching a plateau. Median progression-free survival (PFS) was 5.2 months (95% confidence interval (CI), 2.5-20.8) and overall survival was 6.3 months (95% CI, 1.8-9.7).

CONCLUSIONS

In this recurrent HGG study, ADIPEMCIS was well tolerated and compares favorably to historical controls. Additional trials of ADI-PEG20 in HGG are planned.

Autosegmentation of the rectum on megavoltage image guidance scans

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Anatomical change during radiotherapy for head and neck cancer, and its effect on delivered dose to the spinal cord

•

A cohort of 133 head & neck cancer patients treated with TomoTherapy was examined.

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Differences between planned and delivered maximum spinal cord dose were small.

•

Substantial weight loss and anatomical change during treatment was observed.

•

No link between weight loss or anatomical change, and dose differences was seen.

Background and purpose

The impact of weight loss and anatomical change during head and neck (H&N) radiotherapy on spinal cord dosimetry is poorly understood, limiting evidence-based adaptive management strategies.

Materials and methods

133 H&N patients treated with daily mega-voltage CT image-guidance (MVCT-IG) on TomoTherapy, were selected. Elastix software was used to deform planning scan SC contours to MVCT-IG scans, and accumulate dose. Planned (D_P) and delivered (D_A) spinal cord D_2% (SCD_2%) were compared. Univariate relationships between neck irradiation strategy (unilateral vs bilateral), T-stage, N-stage, weight loss, and changes in lateral separation (LND) and CT slice surface area (SSA) at C1 and the superior thyroid notch (TN), and ΔSCD_2% [(D_A – D_P) D_2%] were examined.

Results

The mean value for (D_A – D_P) D_2% was −0.07 Gy (95%CI −0.28 to 0.14, range −5.7 Gy to 3.8 Gy), and the mean absolute difference between D_P and D_A (independent of difference direction) was 0.9 Gy (95%CI 0.76–1.04 Gy). Neck treatment strategy (p = 0.39) and T-stage (p = 0.56) did not affect ΔSCD_2%. Borderline significance (p = 0.09) was seen for higher N-stage (N2-3) and higher ΔSCD_2%. Mean reductions in anatomical metrics were substantial: weight loss 6.8 kg; C1LND 12.9 mm; C1SSA 12.1 cm²; TNLND 5.3 mm; TNSSA 11.2 cm², but no relationship between weight loss or anatomical change and ΔSCD_2% was observed (all r² < 0.1).

Conclusions

Differences between delivered and planned spinal cord D_2% are small in patients treated with daily IG. Even patients experiencing substantial weight loss or anatomical change during treatment do not require adaptive replanning for spinal cord safety.

Older Age, Early Symptoms and Physical Function are Associated with the Severity of Late Symptom Clusters for Men Undergoing Radiotherapy for Prostate Cancer

AIMS

To identify symptom clusters and predisposing factors associated with long-term symptoms and health-related quality of life after radiotherapy in men with prostate cancer.

MATERIALS AND METHODS

Patient-reported outcomes (PROs) data from the Medical Research Council RT01 radiotherapy with neoadjuvant androgen deprivation therapy trial of 843 patients were used. PROs were collected over 5 years with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI) and the 36 item Short-Form Health Survey (SF-36). Symptom clusters were explored using hierarchical cluster analysis. The association of treatment dose, baseline patient characteristics and early symptom clusters with the change in severity of PROs over 3 years was investigated with multivariate linear mixed effects models.

RESULTS

Seven symptom clusters of three or more symptoms were identified. The clusters were stable over time. The longitudinal profiles of symptom clusters showed the onset of acute symptoms during treatment for all symptom clusters and significant recovery by 6 months. Some clusters, such as physical health and sexual function, were adversely affected more than others by androgen deprivation therapy, and were less likely to return to pretreatment levels over time. Older age was significantly associated with decreased long-term physical function, physical health and sexual function (P < 0.001). Both baseline and acute symptom clusters were significant antecedents for impaired function and health-related quality of life at 3 years.

CONCLUSIONS

Men with poorer physical function and health before or during treatment were more likely to report poorer PROs at year 3. Early assessment using PROs and lifestyle interventions should be used to identify those with higher needs and provide targeted rehabilitation and symptom management.

Automatic contour propagation using deformable image registration to determine delivered dose to spinal cord in head-and-neck cancer radiotherapy

To determine delivered dose to the spinal cord, a technique has been developed to propagate manual contours from kilovoltage computed-tomography (kVCT) scans for treatment planning to megavoltage computed-tomography (MVCT) guidance scans. The technique uses the Elastix software to perform intensity-based deformable image registration of each kVCT scan to the associated MVCT scans. The registration transform is then applied to contours of the spinal cord drawn manually on the kVCT scan, to obtain contour positions on the MVCT scans. Different registration strategies have been investigated, with performance evaluated by comparing the resulting auto-contours with manual contours, drawn by oncologists. The comparison metrics include the conformity index (CI), and the distance between centres (DBC). With optimised registration, auto-contours generally agree well with manual contours. Considering all 30 MVCT scans for each of three patients, the median CI is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.759 \pm 0.003$ \end{document}0.759±0.003, and the median DBC is (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.87 \pm 0.01$ \end{document}0.87±0.01) mm. An intra-observer comparison for the same scans gives a median CI of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.820 \pm 0.002$ \end{document}0.820±0.002 and a DBC of (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$0.64 \pm 0.01$ \end{document}0.64±0.01) mm. Good levels of conformity are also obtained when auto-contours are compared with manual contours from one observer for a single MVCT scan for each of 30 patients, and when they are compared with manual contours from six observers for two MVCT scans for each of three patients. Using the auto-contours to estimate organ position at treatment time, a preliminary study of 33 patients who underwent radiotherapy for head-and-neck cancers indicates good agreement between planned and delivered dose to the spinal cord.

Feasibility of Hippocampal Avoidance Radiotherapy for Glioblastoma

With improvements in survival for good performance status patients and in specific molecular subtypes of glioblastoma, some patients will survive to develop significant neurocognitive dysfunction. This retrospective planning study quantified hippocampal radiation doses in patients with glioblastoma receiving radical chemo-radiotherapy and compared this with the radiation doses that showed clinical correlation with neurocognitive dysfunction, and evaluated the potential for clinically meaningful hippocampal dose reduction using helical TomoTherapy^®.

Cambridge Brain Mets Trial 1 (CamBMT1): A proof of principle study of afatinib penetration into cerebral metastases (mets) for patients (pts) undergoing neurosurgical resection, combined with low-dose, targeted radiotherapy (RT)—Phase 1b results

2008

Background: Failure of drugs to cross the blood brain barrier (BBB) can be a major reason for treatment failure in pts with brain tumours. Preliminary data suggest that low-dose RT may disrupt the BBB, and could facilitate increased drug delivery into brain tumours. CamBMT1 is a phase 1b/2 pre-operative window-of-opportunity trial designed to test if the delivery of afatinib into brain mets might be enhanced by targeted, low dose-RT. Methods: Pts with operable brain mets from breast or lung origin were treated with afatinib for 11 days prior to surgery on day 12. Pts also received a single fraction of targeted RT on day 10 (pts in either 2Gy or 4Gy arm). In phase 1b, afatinib dose (20, 30, or 40mg QD) was escalated in each arm using an accelerated titration design. Primary endpoint: steady-state afatinib concentration in resected brain mets, compared with plasma. Secondary endpoints: safety and tolerability. Results: 10 pts were treated (4 breast, 6 lung), with no dose-limiting toxicities seen, thus completing recruitment to phase 1b. Treatment was generally well tolerated. Median afatinib concentrations on day 12 were: plasma 22.7ng/mL (range 9.94-179); and tumour 405ng/g (range 120-1129). Conclusions: It was feasible to conduct a window-of-opportunity study of afatinib plus RT in pts with operable brain mets. The recommended phase 2 dose of afatinib was 40mg QD for both 2Gy and 4Gy arms. Afatinib concentrations in resected tumour were on average >15-fold higher than those in plasma. Phase 2 of CamBMT1 is now underway in multiple UK sites, and randomises patients into 3 treatment arms (n=20 per arm): 1. afatinib 40mg QD; 2. afatinib 40mg QD + 2Gy# RT; 3. afatinib 40mg QD + 4Gy# RT. Clinical trial information: NCT02768337. [Table: see text]

Delivered dose can be a better predictor of rectal toxicity than planned dose in prostate radiotherapy

Background and purpose

For the first time, delivered dose to the rectum has been calculated and accumulated throughout the course of prostate radiotherapy using megavoltage computed tomography (MVCT) image guidance scans. Dosimetric parameters were linked with toxicity to test the hypothesis that delivered dose is a stronger predictor of toxicity than planned dose.

Material and methods

Dose–surface maps (DSMs) of the rectal wall were automatically generated from daily MVCT scans for 109 patients within the VoxTox research programme. Accumulated-DSMs, representing total delivered dose, and planned-DSMs, from planning CT data, were parametrised using Equivalent Uniform Dose (EUD) and ‘DSM dose-width’, the lateral dimension of an ellipse fitted to a discrete isodose cluster. Associations with 6 toxicity endpoints were assessed using receiver operator characteristic curve analysis.

Results

For rectal bleeding, the area under the curve (AUC) was greater for accumulated dose than planned dose for DSM dose-widths up to 70 Gy. Accumulated 65 Gy DSM dose-width produced the strongest spatial correlation (AUC 0.664), while accumulated EUD generated the largest AUC overall (0.682). For proctitis, accumulated EUD was the only reportable predictor (AUC 0.673). Accumulated EUD was systematically lower than planned EUD.

Conclusions

Dosimetric parameters extracted from accumulated DSMs have demonstrated stronger correlations with rectal bleeding and proctitis, than planned DSMs.

Abstract OT1-04-01: Cambridge brain mets trial 1 (CamBMT1): A proof-of-principle phase 1b / randomised phase 2 study of afatinib penetration into brain metastases for patients undergoing neurosurgical resection, both with and without prior low-dose, targeted radiotherapy

Background

Failure of drugs to cross the blood brain barrier (BBB) can be a major reason for treatment failure for patients with brain tumors. For most patients who don't respond to treatment, it is not known whether this is due to inadequate drug concentrations in the tumor, or due to drug resistance. Preliminary data suggest that low-dose radiotherapy may disrupt the BBB, and could facilitate increased drug delivery into brain tumors. Afatinib is a potent, irreversible inhibitor of EGFR / HER2 / HER4 and takes approximately 8 days to achieve steady-state concentrations in cancer patients.

Aims

CamBMT1 has been designed to investigate the delivery of afatinib into brain metastases and whether this might be enhanced by low dose-radiotherapy.

Patient Population

Key eligibility criteria

Patients with operable brain metastases from breast or lung primaries for whom neurosurgical resection would be standard of care, as determined by the local multi-disciplinary team. ECOG PS 0, 1 or 2.

Trial design

After a phase 1b safety run- in, the phase 2 part of the trial randomises patients (n=60) into 3 pre-operative arms:

Arm 1afatinib alone for 11 days, then neurosurgery on day 12Arm 2afatinib for 11 days plus a single 2 Gy fraction on day 10, then neurosurgery on day 12Arm 3afatinib for 11 days plus a single 4 Gy fraction on day 10, then neurosurgery on day 12

Primary endpoint: to compare steady-state afatinib concentration in resected brain metastases, following afatinib administered alone, or in combination with radiotherapy (2 Gy or 4 Gy). Afatinib concentrations are measured in the resected brain metastases and in plasma.

Secondary endpoints: safety of afatinib administration in combination with radiotherapy; and multi-sequence MRI (optional) to detect changes in perfusion, vascular density, blood-brain-barrier permeability and interstitial pressure.

Exploratory endpoints: molecular profiling of resected brain metastases, for comparison with paired primary lung and breast cancers; the establishment and study of patient-derived xenografts.

Statistical methods

With 20 patients randomised in each of 3 arms in the phase 2 part of CamBMT1, the trial has a power of 84% at a significance level of 20% (one-sided) to detect an increase in afatinib concentrations with targeted radiotherapy, measured as a Cohen's D (standardised mean difference) ≥0.5.

Accrual

By the end of q2 2016, phase 1b had nearly completed enrolment. The randomised phase 2 part of CamBMT1 is due to open by q4 2016 at additional Experimental Cancer Medicine Centres.

Acknowledgments

CamBMT1 is funded by Cancer Research UK, the Brain Tumour Charity and Boehringer-Ingelheim.

Citation Format: Baird RD, Ramenatte N, Watts C, Jonson A, Jones L, Biggs H, Harrison E, Oberg I, Bullen G, Williams M, Qian W, Gilbert F, Jodrell D, Caldas C, Karabatsou K, Dunn L, Jena R, Whitfield G, Chalmers A, Jefferies S, Price S. Cambridge brain mets trial 1 (CamBMT1): A proof-of-principle phase 1b / randomised phase 2 study of afatinib penetration into brain metastases for patients undergoing neurosurgical resection, both with and without prior low-dose, targeted radiotherapy [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr OT1-04-01.

Time Dependence of Radiation-induced Hypothalamic-Pituitary Axis Dysfunction in Adults Treated for Non-pituitary, Intracranial Neoplasms

AIMS

Hypothalamic-pituitary axis (HPA) dysfunction is a sequela of cranial radiotherapy. The purpose of this study was to use endocrine data from existing publications to characterise the baseline endocrine status, the effects of radiotherapy on the HPA during the first follow-up year and the time dependence of radiation-induced HPA dysfunction in patients treated with radiotherapy for non-pituitary intracranial neoplasms.

MATERIALS AND METHODS

A systematic search of databases was carried out for articles that reported the results of endocrine testing for patients aged 16 years and older who were treated with neurosurgery for non-pituitary intracranial neoplasms or radiotherapy for nasopharyngeal neoplasms. To analyse the radiotherapy-related changes in hormone levels over time, long-term prospective endocrine data from nasopharyngeal studies were normalised to baseline hormone data and fitted to an exponential decay model. This process was repeated with normalisation to year 1 hormone data.

RESULTS

Eight unique articles met eligibility criteria. HPA dysfunction occurred in 21.6-64.7% of patients who were assessed for endocrinopathies following neurosurgery. Studies on the early effects of radiotherapy on nasopharyngeal patients showed statistically significant changes in growth hormone, luteinizing hormone and follicle stimulating hormone levels during the first year of follow-up. Time dependence modelling showed that normalisation to year 1 hormone levels yield exponential equations with stronger measures of goodness of fit.

CONCLUSION

HPA dysfunction in patients treated for non-pituitary intracranial neoplasms is probably a result of both neurosurgery and radiotherapy treatments. Although statistically significant endocrine changes can occur during this first year of follow-up, those documented at year 1 may be more predictive of subsequent HPA dysfunction.