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.

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-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.

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.

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.

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.

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.

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

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 (DP) and delivered (DA) spinal cord D2% (SCD2%) 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 ΔSCD2% [(DA - DP) D2%] were examined. RESULTS: The mean value for (DA - DP) D2% 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 DP and DA (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 ΔSCD2%. Borderline significance (p = 0.09) was seen for higher N-stage (N2-3) and higher ΔSCD2%. 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 ΔSCD2% was observed (all r2 < 0.1). CONCLUSIONS: Differences between delivered and planned spinal cord D2% 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.

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary 'VoxTox' research programme.

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.

Toxicity profile of bevacizumab in the UK Neurofibromatosis type 2 cohort.

Bevacizumab is considered an established part of the treatment strategies available for schwannomas in patients with Neurofibromatosis type 2 (NF2). In the UK, it is available through NHS National Specialized Commissioning to NF2 patients with a rapidly growing target schwannoma. Regrowth of the tumour on suspension of treatment is often observed resulting in prolonged periods of exposure to bevacizumab to control the disease. Hypertension and proteinuria are common events with bevacizumab use and there are concerns with regards to the long-term risks of prolonged treatment. Dosing, demographic and adverse event (CTCAE 4.03) data from the UK NF2 bevacizumab cohort are reviewed with particular consideration of renal and cardiovascular complications. Eighty patients (48 male:32 female), median age 24.5 years (range 11-66 years), were followed for a median of 32.7 months (range 12.0-60.2 months). The most common adverse events were fatigue, hypertension and infection. A total of 19/80 patients (24 %) had either a grade 2 or grade 3 hypertension event and 14/80 patients (17.5 %) had proteinuria. Of 36 patients followed for 36 months, 78 % were free from hypertension and 86 % were free of proteinuria. Logistic regression modeling identified age and induction dosing regime to be independent predictors of development of hypertension with dose of 7.5 mg/kg 3 weekly and age >30years having higher rates of hypertension. Proteinuria persisted in one of three patients after cessation of bevacizumab. One patient developed congestive heart failure and the details of this case are described. Further work is needed to determine optimal dosing regimes to limit toxicity without impacting on efficacy.

Bevacizumab in neurofibromatosis type 2 (NF2) related vestibular schwannomas: a nationally coordinated approach to delivery and prospective evaluation.

BACKGROUND: NF2 patients develop multiple nervous system tumors including bilateral vestibular schwannomas (VS). The tumors and their surgical treatment are associated with deafness, neurological disability, and mortality.Medical treatment with bevacizumab has been reported to reduce VS growth and to improve hearing. In addition to evaluating these effects, this study also aimed to determine other important consequences of treatment including patient-reported quality of life and the impact of treatment on surgical VS rates. METHODS: Patients treated with bevacizumab underwent serial prospective MRI, audiology, clinical, CTCAE-4.0 adverse events, and NFTI-QOL quality-of-life assessments. Tumor volumetrics were classified according to the REiNs criteria and annual VS surgical rates reviewed. RESULTS: Sixty-one patients (59% male), median age 25 years (range, 10-57), were reviewed. Median follow-up was 23 months (range, 3-53). Partial volumetric tumor response (all tumors) was seen in 39% and 51% had stabilization of previously growing tumors. Age and pretreatment growth rate were predictors of response. Hearing was maintained or improved in 86% of assessable patients. Mean NFTI-QOL scores improved from 12.0 to 10.7 (P < .05). Hypertension was observed in 30% and proteinuria in 16%. Twelve treatment breaks occurred due to adverse events. The rates of VS surgery decreased after the introduction of bevacizumab. CONCLUSION: Treatment with bevacizumab in this large, UK-wide cohort decreased VS growth rates and improved hearing and quality of life. The potential risk of surgical iatrogenic damage was also reduced due to an associated reduction in VS surgical rates. Ongoing follow-up of this cohort will determine the long-term benefits and risks of bevacizumab treatment.

A multicentre study of the evidence for customized margins in photon breast boost radiotherapy.

OBJECTIVE: To determine if subsets of patients may benefit from smaller or larger margins when using laser setup and bony anatomy verification of breast tumour bed (TB) boost radiotherapy (RT). METHODS: Verification imaging data acquired using cone-beam CT, megavoltage CT or two-dimensional kilovoltage imaging on 218 patients were used (1574 images). TB setup errors for laser-only setup (dlaser) and for bony anatomy verification (dbone) were determined using clips implanted into the TB as a gold standard for the TB position. Cases were grouped by centre-, patient- and treatment-related factors, including breast volume, TB position, seroma visibility and surgical technique. Systematic (Σ) and random (σ) TB setup errors were compared between groups, and TB planning target volume margins (MTB) were calculated. RESULTS: For the study population, Σlaser was between 2.8 and 3.4 mm, and Σbone was between 2.2 and 2.6 mm, respectively. Females with larger breasts (p = 0.03), easily visible seroma (p ≤ 0.02) and open surgical technique (p ≤ 0.04) had larger Σlaser. Σbone was larger for females with larger breasts (p = 0.02) and lateral tumours (p = 0.04). Females with medial tumours (p < 0.01) had smaller Σbone. CONCLUSION: If clips are not used, margins should be 8 and 10 mm for bony anatomy verification and laser setup, respectively. Individualization of TB margins may be considered based on breast volume, TB and seroma visibility. ADVANCES IN KNOWLEDGE: Setup accuracy using lasers and bony anatomy is influenced by patient and treatment factors. Some patients may benefit from clip-based image guidance more than others.

Accumulated dose to the rectum, measured using dose-volume histograms and dose-surface maps, is different from planned dose in all patients treated with radiotherapy for prostate cancer.

OBJECTIVE: We sought to calculate accumulated dose (DA) to the rectum in patients treated with radiotherapy for prostate cancer. We were particularly interested in whether dose-surface maps (DSMs) provide additional information to dose-volume histograms (DVHs). METHODS: Manual rectal contours were obtained for kilovoltage and daily megavoltage CT scans for 10 participants from the VoxTox study (380 scans). Daily delivered dose recalculation was performed using a ray-tracing algorithm. Delivered DVHs were summated to create accumulated DVHs. The rectum was considered as a cylinder, cut and unfolded to produce daily delivered DSMs; these were summated to produce accumulated DSMs. RESULTS: Accumulated dose-volumes were different from planned in all participants. For one participant, all DA levels were higher and all volumes were larger than planned. For four participants, all DA levels were lower and all volumes were smaller than planned. For each of these four participants, ≥1% of pixels on the accumulated DSM received ≥5 Gy more than had been planned. CONCLUSION: Differences between accumulated and planned dose-volumes were seen in all participants. DSMs were able to identify differences between DA and planned dose that could not be appreciated from the DVHs. Further work is needed to extract the dose data embedded in the DSMs. These will be correlated with toxicity as part of the VoxTox Programme. ADVANCES IN KNOWLEDGE: DSMs are able to identify differences between DA and planned dose that cannot be appreciated from DVHs alone and should be incorporated into future studies investigating links between DA and toxicity.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials.

INTRODUCTION: The dose-volume effect of radiation therapy on breast tissue is poorly understood. We estimate NTCP parameters for breast fibrosis after external beam radiotherapy. MATERIALS AND METHODS: We pooled individual patient data of 5856 patients from 2 trials including whole breast irradiation followed with or without a boost. A two-compartment dose volume histogram model was used with boost volume as the first compartment and the remaining breast volume as second compartment. Results from START-pilot trial (n=1410) were used to test the predicted models. RESULTS: 26.8% patients in the Cambridge trial (5 years) and 20.7% patients in the EORTC trial (10 years) developed moderate-severe breast fibrosis. The best fit NTCP parameters were BEUD3(50)=136.4 Gy, γ50=0.9 and n=0.011 for the Niemierko model and BEUD3(50)=132 Gy, m=0.35 and n=0.012 for the Lyman Kutcher Burman model. The observed rates of fibrosis in the START-pilot trial agreed well with the predicted rates. CONCLUSIONS: This large multi-centre pooled study suggests that the effect of volume parameter is small and the maximum RT dose is the most important parameter to influence breast fibrosis. A small value of volume parameter 'n' does not fit with the hypothesis that breast tissue is a parallel organ. However, this may reflect limitations in our current scoring system of fibrosis.

Hadron therapy information sharing prototype.

The European PARTNER project developed a prototypical system for sharing hadron therapy data. This system allows doctors and patients to record and report treatment-related events during and after hadron therapy. It presents doctors and statisticians with an integrated view of adverse events across institutions, using open-source components for data federation, semantics, and analysis. There is a particular emphasis upon semantic consistency, achieved through intelligent, annotated form designs. The system as presented is ready for use in a clinical setting, and amenable to further customization. The essential contribution of the work reported here lies in the novel data integration and reporting methods, as well as the approach to software sustainability achieved through the use of community-supported open-source components.