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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.
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Neoplasias Encefálicas/diagnóstico por imagen , Glioblastoma/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Neoplasias Encefálicas/mortalidad , Neoplasias Encefálicas/fisiopatología , Neoplasias Encefálicas/terapia , Consenso , Progresión de la Enfermedad , Supervivencia sin Enfermedad , Femenino , Estudios de Seguimiento , Glioblastoma/mortalidad , Glioblastoma/fisiopatología , Glioblastoma/terapia , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Estudios RetrospectivosRESUMEN
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.
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Glioblastoma , Humanos , Europa (Continente) , Glioblastoma/diagnóstico por imagen , Glioblastoma/cirugía , Glioblastoma/patología , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodos , Neoplasia Residual/diagnóstico por imagen , Redes Neurales de la Computación , Estudios Multicéntricos como Asunto , Conjuntos de Datos como AsuntoRESUMEN
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.
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Recurrencia Local de Neoplasia , Neoplasias del Recto , Humanos , Consenso , Neoplasias del Recto/diagnóstico por imagen , Neoplasias del Recto/terapia , Variaciones Dependientes del Observador , Fibrosis , Planificación de la Radioterapia Asistida por ComputadorRESUMEN
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.
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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.
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Neoplasias Encefálicas/cirugía , Glioblastoma/cirugía , Neurocirujanos , Procedimientos Neuroquirúrgicos/métodos , Adulto , Anciano , Biopsia , Mapeo Encefálico , Toma de Decisiones Clínicas , Estudios de Cohortes , Femenino , Lóbulo Frontal/patología , Lóbulo Frontal/cirugía , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Lóbulo Parietal/patología , Lóbulo Parietal/cirugía , Probabilidad , Análisis de Supervivencia , Resultado del TratamientoRESUMEN
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.
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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.
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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.
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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.
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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.
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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.
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Mapeo Encefálico/métodos , Neoplasias Encefálicas/cirugía , Glioblastoma/cirugía , Procedimientos Neuroquirúrgicos/métodos , Adulto , Anciano , Biopsia/métodos , Neoplasias Encefálicas/patología , Femenino , Glioblastoma/patología , Humanos , Estimación de Kaplan-Meier , Estado de Ejecución de Karnofsky , Masculino , Persona de Mediana Edad , Neoplasia Residual , Probabilidad , Curva ROC , Reproducibilidad de los Resultados , Análisis de Supervivencia , Resultado del TratamientoRESUMEN
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.
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Biometría/métodos , Neoplasias Encefálicas/cirugía , Glioblastoma/cirugía , Procedimientos Neuroquirúrgicos/estadística & datos numéricos , Pautas de la Práctica en Medicina/estadística & datos numéricos , Adulto , Teorema de Bayes , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Encéfalo/cirugía , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/patología , Simulación por Computador , Glioblastoma/diagnóstico por imagen , Glioblastoma/patología , Humanos , Imagenología Tridimensional , Imagen por Resonancia Magnética , Cadenas de Markov , Método de Montecarlo , Curva ROC , Estudios Retrospectivos , Resultado del TratamientoRESUMEN
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.
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Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/cirugía , Glioblastoma/diagnóstico , Glioblastoma/cirugía , Neuroimagen , Grupo de Atención al Paciente , Anciano , Biopsia , Toma de Decisiones Clínicas , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Neuroimagen/métodos , Procedimientos Neuroquirúrgicos/métodos , Procedimientos Neuroquirúrgicos/normasRESUMEN
PURPOSE: To investigate whether a large rectum filling visible on the planning CT scan was associated with a decrease in freedom from any failure (FFF) and freedom from clinical failure (FFCF) for prostate cancer patients. METHODS AND MATERIALS: Patients from the Dutch trial (78 Gy vs. 68 Gy) with available acute toxicity data were analyzed (n = 549). A 10-mm margin was applied for the first 68 Gy and 0-5 mm for the 10-Gy boost. The dose in the seminal vesicles (SVs) was prescribed within four treatment groups according to the estimated risk of SV involvement. Two potential risk factors (RFs) for a geometric miss were defined: (1) an anorectal volume > or = 90 cm(3) and > or = 25% of treatment-time diarrhea (RF1); and (2) the mean cross-sectional area of the anorectum (RF2). We tested whether these were significant predictors for FFF and FFCF within each treatment group. RESULTS: Significant results were observed only for patients with a risk of SV involvement > 25% (dose of 68-78 Gy to the SVs, n = 349). We found a decrease in FFF (p = 0.001) and FFCF (p = 0.01) for the 87 patients with RF1 (for RF2, p = 0.02 and p = 0.01, respectively). The estimated decrease in the FFCF rate at 5 years was 15%. CONCLUSION: Tumor control was significantly decreased in patients with a risk of SV involvement > 25% and at risk of geometric miss. Current image guidance techniques offer several solutions to geometrically optimize the treatment. Additional research is needed to evaluate whether geometric misses can be prevented using these techniques.
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Diarrea/complicaciones , Neoplasias de la Próstata/radioterapia , Recto/diagnóstico por imagen , Anciano , Intervalos de Confianza , Diarrea/epidemiología , Estudios de Seguimiento , Humanos , Masculino , Neoplasias de la Próstata/sangre , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Dosificación Radioterapéutica , Radioterapia Conformacional , Recto/patología , Factores de Riesgo , Vesículas Seminales/patología , Vesículas Seminales/efectos de la radiación , Tomografía Computarizada por Rayos XRESUMEN
The purpose of this work was the development of a probabilistic planning method with biological cost functions that does not require the definition of margins. Geometrical uncertainties were integrated in tumor control probability (TCP) and normal tissue complication probability (NTCP) objective functions for inverse planning. For efficiency reasons random errors were included by blurring the dose distribution and systematic errors by shifting structures with respect to the dose. Treatment plans were made for 19 prostate patients following four inverse strategies: Conformal with homogeneous dose to the planning target volume (PTV), a simultaneous integrated boost using a second PTV, optimization using TCP and NTCP functions together with a PTV, and probabilistic TCP and NTCP optimization for the clinical target volume without PTV. The resulting plans were evaluated by independent Monte Carlo simulation of many possible treatment histories including geometrical uncertainties. The results showed that the probabilistic optimization technique reduced the rectal wall volume receiving high dose, while at the same time increasing the dose to the clinical target volume. Without sacrificing the expected local control rate, the expected rectum toxicity could be reduced by 50% relative to the boost technique. The improvement over the conformal technique was larger yet. The margin based biological technique led to toxicity in between the boost and probabilistic techniques, but its control rates were very variable and relatively low. During evaluations, the sensitivity of the local control probability to variations in biological parameters appeared similar for all four strategies. The sensitivity to variations of the geometrical error distributions was strongest for the probabilistic technique. It is concluded that probabilistic optimization based on tumor control probability and normal tissue complication probability is feasible. It results in robust prostate treatment plans with an improved balance between local control and rectum toxicity, compared to conventional techniques.
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Artefactos , Simulación por Computador , Neoplasias de la Próstata/radioterapia , Planificación de la Radioterapia Asistida por Computador , Humanos , Masculino , Método de Montecarlo , ProbabilidadRESUMEN
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.
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Neoplasias Pulmonares/radioterapia , Modelos Estadísticos , Análisis de Componente Principal , Tórax/efectos de la radiación , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Masculino , Persona de Mediana Edad , Probabilidad , Terapia de Protones/métodos , Planificación de la Radioterapia Asistida por Computador/métodos , Tomografía Computarizada por Rayos XRESUMEN
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.
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Neoplasias/radioterapia , Dosis de Radiación , Radioterapia Conformacional/métodos , Humanos , Distribución Normal , Órganos en Riesgo/efectos de la radiación , Probabilidad , Radiometría , Dosificación Radioterapéutica , Planificación de la Radioterapia Asistida por Computador , Radioterapia Conformacional/efectos adversos , RiesgoRESUMEN
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.
Asunto(s)
Canal Anal/efectos de la radiación , Neoplasias de la Próstata/radioterapia , Traumatismos por Radiación/prevención & control , Radioterapia Guiada por Imagen/métodos , Radioterapia de Intensidad Modulada/métodos , Recto/efectos de la radiación , Adulto , Anciano , Anciano de 80 o más Años , Instituciones Oncológicas , Protocolos Clínicos , Incontinencia Fecal/prevención & control , Humanos , Imagen por Resonancia Magnética , Masculino , Persona de Mediana Edad , Países Bajos , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Hipofraccionamiento de la Dosis de Radiación , Radioterapia Guiada por Imagen/efectos adversos , Radioterapia Guiada por Imagen/instrumentación , Radioterapia de Intensidad Modulada/efectos adversos , Radioterapia de Intensidad Modulada/instrumentación , Tomografía Computarizada por Rayos XRESUMEN
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.
Asunto(s)
Neoplasias de la Próstata/radioterapia , Radioterapia Conformacional/efectos adversos , Radioterapia Guiada por Imagen/efectos adversos , Radioterapia de Intensidad Modulada/efectos adversos , Anciano , Estudios de Cohortes , Tracto Gastrointestinal/efectos de la radiación , Humanos , Masculino , Medición de Resultados Informados por el Paciente , Modelos de Riesgos Proporcionales , Estudios Prospectivos , Dosificación Radioterapéutica , Sistema Urogenital/efectos de la radiaciónRESUMEN
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.