Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Development of a consensus-based delineation guideline for locally recurrent rectal cancer.

BACKGROUND AND PURPOSE: Neoadjuvant chemoradiotherapy (nCRT) is used in locally recurrent rectal cancer (LRRC) to increase chances of a radical surgical resection. Delineation in LRRC is hampered by complex disease presentation and limited clinical exposure. Within the PelvEx II trial, evaluating the benefit of chemotherapy preceding nCRT for LRRC, a delineation guideline was developed by an expert LRRC team. MATERIALS AND METHODS: Eight radiation oncologists, from Dutch and Swedish expert centres, participated in two meetings, delineating GTV and CTV in six cases. Regions at-risk for re-recurrence or irradical resection were identified by eleven expert surgeons and one expert radiologist. Target volumes were evaluated multidisciplinary. Inter-observer variation was analysed. RESULTS: Inter-observer variation in delineation of LRRC appeared large. Multidisciplinary evaluation per case is beneficial in determining target volumes. The following consensus regarding target volumes was reached. GTV should encompass all tumour, including extension into OAR if applicable. If the tumour is in fibrosis, GTV should encompass the entire fibrotic area. Only if tumour can clearly be distinguished from fibrosis, GTV may be reduced, as long as the entire fibrotic area is covered by the CTV. CTV is GTV with a 1 cm margin and should encompass all at-risk regions for irradical resection or re-recurrence. CTV should not be adjusted towards other organs. Multifocal recurrences should be encompassed in one CTV. Elective nodal delineation is only advised in radiotherapy-naïve patients. CONCLUSION: This study provides a first consensus-based delineation guideline for LRRC. Analyses of re-recurrences is needed to understand disease behaviour and to optimize delineation guidelines accordingly.

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting.

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.

On the cutting edge of glioblastoma surgery: where neurosurgeons agree and disagree on surgical decisions.

OBJECTIVE: The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma. METHODS: The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival. RESULTS: The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions. CONCLUSIONS: Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma.

Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task.

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations.

Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

Timing of glioblastoma surgery and patient outcomes: a multicenter cohort study.

BACKGROUND: The impact of time-to-surgery on clinical outcome for patients with glioblastoma has not been determined. Any delay in treatment is perceived as detrimental, but guidelines do not specify acceptable timings. In this study, we relate the time to glioblastoma surgery with the extent of resection and residual tumor volume, performance change, and survival, and we explore the identification of patients for urgent surgery. METHODS: Adults with first-time surgery in 2012-2013 treated by 12 neuro-oncological teams were included in this study. We defined time-to-surgery as the number of days between the diagnostic MR scan and surgery. The relation between time-to-surgery and patient and tumor characteristics was explored in time-to-event analysis and proportional hazard models. Outcome according to time-to-surgery was analyzed by volumetric measurements, changes in performance status, and survival analysis with patient and tumor characteristics as modifiers. RESULTS: Included were 1033 patients of whom 729 had a resection and 304 a biopsy. The overall median time-to-surgery was 13 days. Surgery was within 3 days for 235 (23%) patients, and within a month for 889 (86%). The median volumetric doubling time was 22 days. Lower performance status (hazard ratio [HR] 0.942, 95% confidence interval [CI] 0.893-0.994) and larger tumor volume (HR 1.012, 95% CI 1.010-1.014) were independently associated with a shorter time-to-surgery. Extent of resection, residual tumor volume, postoperative performance change, and overall survival were not associated with time-to-surgery. CONCLUSIONS: With current decision-making for urgent surgery in selected patients with glioblastoma and surgery typically within 1 month, we found equal extent of resection, residual tumor volume, performance status, and survival after longer times-to-surgery.

Local Dose Effects for Late Gastrointestinal Toxicity After Hypofractionated and Conventionally Fractionated Modern Radiotherapy for Prostate Cancer in the HYPRO Trial.

Purpose: Late gastrointestinal (GI) toxicity after radiotherapy for prostate cancer may have significant impact on the cancer survivor's quality of life. To date, little is known about local dose-effects after modern radiotherapy including hypofractionation. In the current study we related the local spatial distribution of radiation dose in the rectum to late patient-reported gastrointestinal (GI) toxicities for conventionally fractionated (CF) and hypofractionated (HF) modern radiotherapy in the randomized HYPRO trial. Material and Methods: Patients treated to 78 Gy in 2 Gy fractions (n = 298) or 64.6 Gy in 3.4 Gy fractions (n = 295) with available late toxicity questionnaires (n ≥ 2 within 1-5 years post-treatment) and available 3D planning data were eligible for this study. The majority received intensity modulated radiotherapy (IMRT). We calculated two types of dose surface maps: (1) the total delineated rectum with its central axis scaled to unity, and (2) the delineated rectum with a length of 7 cm along its central axis aligned on the prostate's half-height point (prostate-half). For each patient-reported GI symptom, dose difference maps were constructed by subtracting average co-registered EQD2 (equivalent dose in 2 Gy) dose maps of patients with and without the symptom of interest, separately for HF and CF. P-values were derived from permutation tests. We evaluated patient-reported moderate to severe GI symptoms. Results: Observed incidences of rectal bleeding and increased stool frequency were significantly higher in the HF group. For rectal bleeding (p = 0.016), mucus discharge (p = 0.015), and fecal incontinence (p = 0.001), significant local dose-effects were observed in HF patients but not in CF patients. For rectal pain, similar local dose-effects (p < 0.05) were observed in both groups. No significant local dose-effects were observed for increased stool frequency. Total rectum mapping vs. prostate-half mapping showed similar results. Conclusion: We demonstrated significant local dose-effect relationships for patient-reported late GI toxicity in patients treated with modern RT. HF patients were at higher risk for increased stool frequency and rectal bleeding, and showed the most pronounced local dose-effects in intermediate-high dose regions. These findings suggest that improvement of current treatment optimization protocols could lead to clinical benefit, in particular for HF treatment.

Quantifying eloquent locations for glioblastoma surgery using resection probability maps.

OBJECTIVE: Decisions in glioblastoma surgery are often guided by presumed eloquence of the tumor location. The authors introduce the "expected residual tumor volume" (eRV) and the "expected resectability index" (eRI) based on previous decisions aggregated in resection probability maps. The diagnostic accuracy of eRV and eRI to predict biopsy decisions, resectability, functional outcome, and survival was determined. METHODS: Consecutive patients with first-time glioblastoma surgery in 2012-2013 were included from 12 hospitals. The eRV was calculated from the preoperative MR images of each patient using a resection probability map, and the eRI was derived from the tumor volume. As reference, Sawaya's tumor location eloquence grades (EGs) were classified. Resectability was measured as observed extent of resection (EOR) and residual volume, and functional outcome as change in Karnofsky Performance Scale score. Receiver operating characteristic curves and multivariable logistic regression were applied. RESULTS: Of 915 patients, 674 (74%) underwent a resection with a median EOR of 97%, functional improvement in 71 (8%), functional decline in 78 (9%), and median survival of 12.8 months. The eRI and eRV identified biopsies and EORs of at least 80%, 90%, or 98% better than EG. The eRV and eRI predicted observed residual volumes under 10, 5, and 1 ml better than EG. The eRV, eRI, and EG had low diagnostic accuracy for functional outcome changes. Higher eRV and lower eRI were strongly associated with shorter survival, independent of known prognostic factors. CONCLUSIONS: The eRV and eRI predict biopsy decisions, resectability, and survival better than eloquence grading and may be useful preoperative indices to support surgical decisions.

Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training.

PURPOSE: To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets. MATERIALS AND METHODS: In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49-69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. The convolutional neural network, DeepMedic, was trained on combinations of complete and incomplete data with and without site-specific data. Sparsified training was introduced, which randomly simulated missing sequences during training. The effects of sparsified training and center-specific training were tested using Wilcoxon signed rank tests for paired measurements. RESULTS: A model trained exclusively on BraTS data reached a median Dice score of 0.81 for segmentation on BraTS test data but only 0.49 on the clinical data. Sparsified training improved performance (adjusted P < .05), even when excluding test data with missing sequences, to median Dice score of 0.67. Inclusion of site-specific data during sparsified training led to higher model performance Dice scores greater than 0.8, on par with a model based on all complete and incomplete data. For the model using BraTS and clinical training data, inclusion of site-specific data or sparsified training was of no consequence. CONCLUSION: Accurate and automatic segmentation of glioblastoma on clinical scans is feasible using a model based on large, heterogeneous, and partially incomplete datasets. Sparsified training may boost the performance of a smaller model based on public and site-specific data.Supplemental material is available for this article.Published under a CC BY 4.0 license.

Voxelwise statistical methods to localize practice variation in brain tumor surgery.

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.

Comparing Glioblastoma Surgery Decisions Between Teams Using Brain Maps of Tumor Locations, Biopsies, and Resections.

PURPOSE: The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity, which depends on the location within the brain. A standard to compare these decisions is lacking. We present a volumetric voxel-wise method for direct comparison between two multidisciplinary teams of glioblastoma surgery decisions throughout the brain. METHODS: Adults undergoing first-time glioblastoma surgery from 2012 to 2013 performed by two neuro-oncologic teams were included. Patients had had a diagnostic biopsy or resection. Preoperative tumors and postoperative residues were segmented on magnetic resonance imaging in three dimensions and registered to standard brain space. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to compare patient referral bias, indication variation, and treatment variation. To evaluate the quality of care, subgroups of differentially resected brain regions were analyzed for survival and functional outcome. RESULTS: One team included 101 patients, and the other included 174; 91 tumors were biopsied, and 181 were resected. Patient characteristics were largely comparable between teams. Distributions of tumor locations were dissimilar, suggesting referral bias. Distributions of biopsies were similar, suggesting absence of indication variation. Differentially resected regions were identified in the anterior limb of the right internal capsule and the right caudate nucleus, indicating treatment variation. Patients with (n = 12) and without (n = 6) surgical removal in these regions had similar overall survival and similar permanent neurologic deficits. CONCLUSION: Probability maps of tumor location, biopsy, and resection provide additional information that can inform surgical decision making across multidisciplinary teams for patients with glioblastoma.

Earliest radiological progression in glioblastoma by multidisciplinary consensus review.

BACKGROUND: Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression. METHODS: In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria. RESULTS: ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38-0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19-0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL). CONCLUSION: PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment.

Local Protocol Variations for Image Guided Radiation Therapy in the Multicenter Dutch Hypofractionation (HYPRO) Trial: Impact of Rectal Balloon and MRI Delineation on Anorectal Dose and Gastrointestinal Toxicity Levels.

PURPOSE: The phase 3 HYpofractionated irradiation for PROstate cancer (HYPRO) trial randomized patients with intermediate- to high-risk localized prostate cancer to conventionally fractionated (78 Gy in 39 fractions) or hypofractionated (64.6 Gy in 19 fractions) radiation therapy. Differences in techniques and treatment protocols were present between participating centers. This study aimed to compare dose parameters and patient-reported gastrointestinal symptoms between these centers. METHODS AND MATERIALS: From the trial population, we selected patients (N=572) from 4 treatment centers who received image guided (IG) intensity modulated radiation therapy (IMRT). Center A (n=242) applied planning target volume (PTV) margins of 5 to 6 mm and was considered the reference center. In center B (n=170, 7-mm margins), magnetic resonance imaging (MRI) was integrated in treatment planning. An endorectal balloon (ERB) was applied in center C (n=85, 7-mm margins). Center D (n=75) applied the largest PTV margins of 8 mm. The study protocol provided identical anorectal dose constraints, and local protocols were applied for further treatment optimization. Anorectal dose-surface histograms were compared by applying t tests. Rectal complaints during follow-up (6 months to 4 years) were compared in a generalized linear model, adjusting for age, follow-up, treatment arm, and hormone therapy. RESULTS: Favorable anorectal dose distributions were found for center B (MRI delineation) and center C (ERB application) as compared with centers A and D. These were associated with significantly lower incidences of patient-reported complaints of rectal incontinence, use of incontinence pads, and rectal discomfort in these centers. Furthermore, lower incidences of increased stool frequency (≥4 per day) and mucous loss were observed for center C. CONCLUSIONS: Despite comparable IG-IMRT techniques and predefined dose constraints, pronounced differences in dose distributions and toxicity rates were observed. MRI delineation and ERB application were associated with favorable rectal dose parameters and toxicity profiles, whereas a 2- to 3-mm difference in PTV margins did not translate into observed differences. We conclude that choices for treatment optimization of IG-IMRT are important and clinically relevant for patients since these affect symptoms experienced in daily life.

Beyond the margin recipe: the probability of correct target dosage and tumor control in the presence of a dose limiting structure.

In the past, hypothetical spherical target volumes and ideally conformal dose distributions were analyzed to establish the safety of planning target volume (PTV) margins. In this work we extended these models to estimate how alternative methods of shaping dose distributions could lead to clinical improvements. Based on a spherical clinical target volume (CTV) and Gaussian distributions of systematic and random geometrical uncertainties, idealized 3D dose distributions were optimized to exhibit specific stochastic properties. A nearby spherical organ at risk (OAR) was introduced to explore the benefit of non-spherical dose distributions. Optimizing for the same minimum dose safety criterion as implied by the generally accepted use of a PTV, the extent of the high dose region in one direction could be reduced by half provided that dose in other directions is sufficiently compensated. Further reduction of this unilateral dosimetric margin decreased the target dose confidence, however the actual minimum CTV dose at 90% confidence typically exceeded the minimum PTV dose by 20% of prescription. Incorporation of smooth dose-effect relations within the optimization led to more concentrated dose distributions compared to the use of a PTV, with an improved balance between the probability of tumor cell kill and the risk of geometrical miss, and lower dose to surrounding tissues. Tumor control rate improvements in excess of 20% were found to be common for equal integral dose, while at the same time evading a nearby OAR. These results were robust against uncertainties in dose-effect relations and target heterogeneity, and did not depend on 'shoulders' or 'horns' in the dose distributions.

A population based statistical model for daily geometric variations in the thorax.

PURPOSE: To develop a population based statistical model of the systematic interfraction geometric variations between the planning CT and first treatment week of lung cancer patients for inclusion as uncertainty term in future probabilistic planning. MATERIALS AND METHODS: Deformable image registrations between the planning CT and first week CBCTs of 235 lung cancer patients were used to generate deformation vector fields (DVFs) representing the geometric variations of lung cancer patients. Using a second deformable registration step, the average DVF per patient was mapped to an average patient CT. Subsequently, the dominant modes of systematic geometric variations were extracted using Principal Component Analysis (PCA). For evaluation a leave-one-out cross-validation was performed. RESULTS: The first three PCA components mainly described cranial-caudal, anterior-posterior, and left-right variations, respectively. Fifty and 112 components were needed to describe correspondingly 75% and 90% of the variance. An overall systematic variation of 3.6mm SD was observed and could be described with an accuracy of about 1.0mm with the PCA model. CONCLUSIONS: A PCA based model for systematic geometric variations in the thorax was developed, and its accuracy determined. Such a model can serve as a basis for probability based treatment planning in lung cancer patients.

Effects of anatomical changes on pencil beam scanning proton plans in locally advanced NSCLC patients.

BACKGROUND AND PURPOSE: Daily anatomical variations can cause considerable differences between delivered and planned dose. This study simulates and evaluates these effects in spot-scanning proton therapy for lung cancer patients. MATERIALS AND METHODS: Robust intensity modulated treatment plans were designed on the mid-position CT scan for sixteen locally advanced lung cancer patients. To estimate dosimetric uncertainty, deformable registration was performed on their daily CBCTs to generate 4DCT equivalent scans for each fraction and to map recomputed dose to a common frame. RESULTS: Without adaptive planning, eight patients had an undercoverage of the targets of more than 2GyE (maximum of 14.1GyE) on the recalculated treatment dose from the daily anatomy variations including respiration. In organs at risk, a maximum increase of 4.7GyE in the D1 was found in the mediastinal structures. The effect of respiratory motion alone is smaller: 1.4GyE undercoverage for targets and less than 1GyE for organs at risk. CONCLUSIONS: Daily anatomical variations over the course of treatment can cause considerable dose differences in the robust planned dose distribution. An advanced planning strategy including knowledge of anatomical uncertainties would be recommended to improve plan robustness against interfractional variations. For large anatomical changes, adaptive therapy is mandatory.

Late Side Effects After Image Guided Intensity Modulated Radiation Therapy Compared to 3D-Conformal Radiation Therapy for Prostate Cancer: Results From 2 Prospective Cohorts.

PURPOSE: Technical developments in the field of external beam radiation therapy (RT) enabled the clinical introduction of image guided intensity modulated radiation therapy (IG-IMRT), which improved target conformity and allowed reduction of safety margins. Whether this had an impact on late toxicity levels compared to previously applied three-dimensional conformal radiation therapy (3D-CRT) is currently unknown. We analyzed late side effects after treatment with IG-IMRT or 3D-CRT, evaluating 2 prospective cohorts of men treated for localized prostate cancer to investigate the hypothesized reductions in toxicity. METHODS AND MATERIALS: Patients treated with 3D-CRT (n=189) or IG-IMRT (n=242) to 78 Gy in 39 fractions were recruited from 2 Dutch randomized trials with identical toxicity scoring protocols. Late toxicity (>90 days after treatment) was derived from self-assessment questionnaires and case report forms, according to Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG-EORTC) scoring criteria. Grade ≥2 endpoints included gastrointestinal (GI) rectal bleeding, increased stool frequency, discomfort, rectal incontinence, proctitis, and genitourinary (GU) obstruction, increased urinary frequency, nocturia, urinary incontinence, and dysuria. The Cox proportional hazards regression model was used to compare grade ≥2 toxicities between both techniques, adjusting for other modifying factors. RESULTS: The 5-year cumulative incidence of grade ≥2 GI toxicity was 24.9% for IG-IMRT and 37.6% following 3D-CRT (adjusted hazard ratio [HR]: 0.59, P=.005), with significant reductions in proctitis (HR: 0.37, P=.047) and increased stool frequency (HR: 0.23, P<.001). GU grade ≥2 toxicity levels at 5 years were comparable with 46.2% and 36.4% following IG-IMRT and 3D-CRT, respectively (adjusted HR: 1.19, P=.33). Other strong predictors (P<.01) of grade ≥2 late toxicity were baseline complaints, acute toxicity, and age. CONCLUSIONS: Treatment with IG-IMRT reduced the risk of late grade ≥2 complications, whereas GU toxicities remained comparable. This clinically relevant observation demonstrates that IMRT and image-guidance should therefore be the preferred treatment option, provided that margin reduction is implemented with caution.

Dose-surface maps identifying local dose-effects for acute gastrointestinal toxicity after radiotherapy for prostate cancer.

BACKGROUND AND PURPOSE: We evaluated dose distributions in the anorectum and its relation to acute gastrointestinal toxicities using dose surface maps in an image-guided (IG) IMRT and 3D-conformal radiotherapy (3D-CRT) population. MATERIAL AND METHODS: For patients treated to 78 Gy with IG-IMRT (n=260) or 3D-CRT (n=215), for whom acute toxicity data were available, three types of surface maps were calculated: (1) total anorectum using regular intervals along a central axis with perpendicular slices, (2) the rectum next to the prostate, and (3) the anal canal (horizontal slicing). For each toxicity, an average dose map was calculated for patients with and without the toxicity and subsequently dose difference maps were constructed, 3D-CRT and IG-IMRT separately. P-values were based on permutation tests. RESULTS: Dose distributions in patients with grade ⩾2 acute proctitis were significantly different from dose distributions in patients without toxicity, for IG-IMRT and 3D-CRT. At the cranial and posterior rectal site, in areas receiving moderate dose levels (≈25-50 Gy), dose differences up to 10 Gy were identified for IG-IMRT. For pain, cramps, incontinence, diarrhea and mucus loss significant differences were found as well. CONCLUSIONS: We demonstrated significant relationships between acute rectal toxicity and local dose distributions. This may serve as a basis for subsequent dose-effect modeling in IG-IMRT, and improved dose constraints in current clinical practice.