Contouring variability of human- and deformable-generated contours in radiotherapy for prostate cancer

Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies

BACKGROUND

While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive.

PURPOSE

The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers.

METHODS

Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (n = 120) and testing set (n = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score.

RESULTS

In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1-3 by two expert radiation oncologists, respectively, meaning only minor edits needed.

CONCLUSIONS

The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.

Clinical evaluation of a deep learning CBCT auto-segmentation software for prostate adaptive radiation therapy

Purpose

Aim of the present study is to characterize a deep learning-based auto-segmentation software (DL) for prostate cone beam computed tomography (CBCT) images and to evaluate its applicability in clinical adaptive radiation therapy routine.

Materials and methods

Ten patients, who received exclusive radiation therapy with definitive intent on the prostate gland and seminal vesicles, were selected. Femoral heads, bladder, rectum, prostate, and seminal vesicles were retrospectively contoured by four different expert radiation oncologists on patients CBCT, acquired during treatment. Consensus contours (CC) were generated starting from these data and compared with those created by DL with different algorithms, trained on CBCT (DL-CBCT) or computed tomography (DL-CT). Dice similarity coefficient (DSC), centre of mass (COM) shift and volume relative variation (VRV) were chosen as comparison metrics. Since no tolerance limit can be defined, results were also compared with the inter-operator variability (IOV), using the same metrics.

Results

The best agreement between DL and CC was observed for femoral heads (DSC of 0.96 for both DL-CBCT and DL-CT). Performance worsened for low-contrast soft tissue organs: the worst results were found for seminal vesicles (DSC of 0.70 and 0.59 for DL-CBCT and DL-CT, respectively). The analysis shows that it is appropriate to use algorithms trained on the specific imaging modality. Furthermore, the statistical analysis showed that, for almost all considered structures, there is no significant difference between DL-CBCT and human operator in terms of IOV.

Conclusions

The accuracy of DL-CBCT is in accordance with CC; its use in clinical practice is justified by the comparison with the inter-operator variability.

Evaluation of safety margins for cone beam CT-based adaptive prostate radiotherapy

Adaptive radiotherapy is characterized by the use of a daily imaging system, such as CBCT (Cone-Beam Computed Tomography) images to re-optimize the treatment based on the daily anatomy and position of the patient. By systematically re-delineating the Clinical Target Volume (CTV) at each fraction, target delineation uncertainty features a random component instead of a pure systematic. The goal of this work is to identify the random and systematic contributions of the delineation error and compute a new relevant Planning Target Volume (PTV) safety margin. 169 radiotherapy sessions from 10 prostate cancer patients treated on the Varian ETHOS treatment system have been analyzed. Intra-patient and inter-patient delineation variabilities were computed in six directions, by considering the prostate as a rigid, non-rotating volume. By doing so, we were able to directly compare the delineations done by the physicians on daily CBCT images with the initial delineation done on the CT-sim and MRI, and sort them by direction using the polar coordinates of the points. The computed variabilities were then used to compute a PTV margin based on Van Herk margin recipe. The total margin computed with random and systematic delineation uncertainties was of 2.7, 2.4, 5.6, 4.8, 4.9 and 3.6 mm in the left, right, anterior, posterior, cranial and caudal directions, respectively. According to our results, the gain offered by the separation of the delineation uncertainty into systematic and random contributions due to the adaptive delineation process justifies a reduction of the PTV margin down to 3 to 5 mm in every direction.

Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

PURPOSE

The purpose of this study was to investigate the relationship between inter-reader variability in manual prostate contour segmentation on magnetic resonance imaging (MRI) examinations and determine the optimal number of readers required to establish a reliable reference standard.

MATERIALS AND METHODS

Seven radiologists with various experiences independently performed manual segmentation of the prostate contour (whole-gland [WG] and transition zone [TZ]) on 40 prostate MRI examinations obtained in 40 patients. Inter-reader variability in prostate contour delineations was estimated using standard metrics (Dice similarity coefficient [DSC], Hausdorff distance and volume-based metrics). The impact of the number of readers (from two to seven) on segmentation variability was assessed using pairwise metrics (consistency) and metrics with respect to a reference segmentation (conformity), obtained either with majority voting or simultaneous truth and performance level estimation (STAPLE) algorithm.

RESULTS

The average segmentation DSC for two readers in pairwise comparison was 0.919 for WG and 0.876 for TZ. Variability decreased with the number of readers: the interquartile ranges of the DSC were 0.076 (WG) / 0.021 (TZ) for configurations with two readers, 0.005 (WG) / 0.012 (TZ) for configurations with three readers, and 0.002 (WG) / 0.0037 (TZ) for configurations with six readers. The interquartile range decreased slightly faster between two and three readers than between three and six readers. When using consensus methods, variability often reached its minimum with three readers (with STAPLE, DSC = 0.96 [range: 0.945-0.971] for WG and DSC = 0.94 [range: 0.912-0.957] for TZ, and interquartile range was minimal for configurations with three readers.

CONCLUSION

The number of readers affects the inter-reader variability, in terms of inter-reader consistency and conformity to a reference. Variability is minimal for three readers, or three readers represent a tipping point in the variability evolution, with both pairwise-based metrics or metrics with respect to a reference. Accordingly, three readers may represent an optimal number to determine references for artificial intelligence applications.

An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning

PURPOSE

Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gland incorporating interobserver variation for radiotherapy treatment planning. The U-Net/VAE generates an ensemble set of segmentations for each image CT slice. A novel outlier mitigation (OM) technique was implemented to enhance the model segmentation accuracy.

METHODS

The primary source dataset (source_prim) consisted of 19 200 CT slices (from 300 patient planning CT image datasets) with manually contoured prostate glands. A smaller secondary source dataset (source_sec) comprised 640 CT slices (from 10 patient CT datasets), where prostate glands were segmented by 5 independent physicians on each dataset to account for interobserver variability. Data augmentation via random rotation (<5 degrees), cropping, and horizontal flipping was applied to each dataset to increase sample size by a factor of 100. A probabilistic hierarchical U-Net with VAE was implemented and pretrained using the augmented source_prim dataset for 30 epochs. Model parameters of the U-Net/VAE were fine-tuned using the augmented source_sec dataset for 100 epochs. After the first round of training, outlier contours in the training dataset were automatically detected and replaced by the most accurate contours (based on Dice similarity coefficient, DSC) generated by the model. The U-Net/OM-VAE was retrained using the revised training dataset. Metrics for comparison included DSC, Hausdorff distance (HD, mm), normalized cross-correlation (NCC) coefficient, and center-of-mass (COM) distance (mm).

RESULTS

Results for U-Net/OM-VAE with outliers replaced in the training dataset versus U-Net/VAE without OM were as follows: DSC = 0.82 ± 0.01 versus 0.80 ± 0.02 (p = 0.019), HD = 9.18 ± 1.22 versus 10.18 ± 1.35 mm (p = 0.043), NCC = 0.59 ± 0.07 versus 0.62 ± 0.06, and COM = 3.36 ± 0.81 versus 4.77 ± 0.96 mm over the average of 15 contours. For the average of 15 highest accuracy contours, values were as follows: DSC = 0.90 ± 0.02 versus 0.85 ± 0.02, HD = 5.47 ± 0.02 versus 7.54 ± 1.36 mm, and COM = 1.03 ± 0.58 versus 1.46 ± 0.68 mm (p < 0.03 for all metrics). Results for the U-Net/OM-VAE with outliers removed were as follows: DSC = 0.78 ± 0.01, HD = 10.65 ± 1.95 mm, NCC = 0.46 ± 0.10, COM = 4.17 ± 0.79 mm for the average of 15 contours, and DSC = 0.88 ± 0.02, HD = 7.00 ± 1.17 mm, COM = 1.58 ± 0.63 mm for the average of 15 highest accuracy contours. All metrics for U-Net/VAE trained on the source_prim and source_sec datasets via pretraining, followed by fine-tuning, show statistically significant improvement over that trained on the source_sec dataset only. Finally, all metrics for U-Net/VAE with or without OM showed statistically significant improvement over those for the standard U-Net.

CONCLUSIONS

A VAE combined with a hierarchical U-Net and an OM strategy (U-Net/OM-VAE) demonstrates promise toward capturing interobserver variability and produces accurate prostate auto-contours for radiotherapy planning. The availability of multiple contours for each CT slice enables clinicians to determine trade-offs in selecting the "best fitting" contour on each CT slice. Mitigation of outlier contours in the training dataset improves prediction accuracy, but one must be wary of reduction in variability in the training dataset.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography

PURPOSE

Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours.

METHODS

Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations.

RESULTS

Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections.

CONCLUSION

We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.

Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies

PURPOSE

The performance of deformable medical image registration (DIR) algorithms has become a major concern.

METHODS

We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines.

RESULTS

One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed.

CONCLUSIONS

This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.

Oesophageal IGRT considerations for SBRT of LA-NSCLC: barium-enhanced CBCT and interfraction motion

Background

To determine the optimal volume of barium for oesophageal localisation on cone-beam CT (CBCT) for locally-advanced non-small cell lung cancers (NSCLC) and quantify the interfraction oesophageal movement relative to tumour.

Methods

Twenty NSCLC patients with mediastinal and/or hilar disease receiving radical radiotherapy were recruited. The first five patients received 25 ml of barium prior to their planning CT and alternate CBCTs during treatment. Subsequent five patient cohorts, received 15 ml, 10 ml and 5 ml. Six observers contoured the oesophagus on each of the 107 datasets and consensus contours were created. Overall 642 observer contours were generated and interobserver contouring reproducibility was assessed. The kappa statistic, dice coefficient and Hausdorff Distance (HD) were used to compare barium-enhanced CBCTs and non-enhanced CBCTs. Oesophageal displacement was assessed using the HD between consensus contours of barium-enhanced CBCTs and planning CTs.

Results

Interobserver contouring reproducibility was significantly improved in barium-enhanced CBCTs compared to non-contrast CBCTs with minimal difference between barium dose levels. Only 10 mL produced a significantly higher kappa (0.814, p = 0.008) and dice (0.895, p = 0.001). The poorer the reproducibility without barium, the greater the improvement barium provided. The median interfraction HD between consensus contours was 4 mm, with 95% of the oesophageal displacement within 15 mm.

Conclusions

10 mL of barium significantly improves oesophageal localisation on CBCT with minimal image artifact. The oesophagus moves substantially and unpredictably over a course of treatment, requiring close daily monitoring in the context of hypofractionation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01946-8.

Application of an automated dose accumulation workflow in high-risk prostate cancer - validation and dose-volume analysis between planned and delivered dose

Inter-fraction organ variations cause deviations between planned and delivered doses in patients receiving radiotherapy for prostate cancer. This study compared planned (D_P) vs accumulated doses (D_A) obtained from daily cone-beam computed tomography (CBCT) scans in high-risk- prostate cancer with pelvic lymph nodes irradiation. An intensity-based deformable image registration algorithm used to estimate contours for D_A was validated using geometrical agreement between radiation oncologist's and deformable image registration algorithm propagated contours. Spearman rank correlations (r_s) between geometric measures and changes in organ volumes were evaluated for 20 cases. Dose-volume (DV) differences between D_A and D_P were compared (Wilcoxon rank test, p < 0.05). A novel region-of-interest (ROI) method was developed and mean doses were analyzed. Geometrical measures for the prostate and organ-at-risk contours were within clinically acceptable criteria. Inter-group mean (± SD) CBCT volumes for the rectum were larger compared to planning CT (pCT) (51.1 ± 11.3 cm³vs 46.6 ± 16.1 cm³), and were moderately correlated with variations in pCT volumes, rs = 0.663, p < 0.01. Mean rectum DV for D_A was higher at V30-40 Gy and lower at V70-75 Gy, p < 0.05. Mean bladder CBCT volumes were smaller compared to pCT (198.8 ± 55 cm³vs 211.5 ± 89.1 cm³), and was moderately correlated with pCT volumes, rs = 0.789, p < 0.01. Bladder D_A was higher at V30-65 Gy and lower at V70-75 Gy (p < 0.05). For the ROI method, rectum and bladder D_A were lower at 5 to 10 mm (p < 0.01) as compared to D_P, whilst bladder D_A was higher than D_P at 20 to 50 mm (p < 0.01). Generated D_A demonstrated significant differences in organ-at-risk doses as compared to D_P. A well-constructed workflow incorporating a ROI DV-extraction method has been validated in terms of efficiency and accuracy designed for seamless integration in the clinic to guide future plan adaptation.

How does CBCT reconstruction algorithm impact on deformably mapped targets and accumulated dose distributions?

PURPOSE

We performed quantitative analysis of differences in deformable image registration (DIR) and deformable dose accumulation (DDA) computed on CBCT datasets reconstructed using the standard (Feldkamp-Davis-Kress: FDK_CBCT) and a novel iterative (iterative_CBCT) CBCT reconstruction algorithms.

METHODS

Both FDK_CBCT and iterative_CBCT images were reconstructed for 323 fractions of treatment for 10 prostate cancer patients. Planning CT images were deformably registered to each CBCT image data set. After daily dose distributions were computed, they were mapped to planning CT to obtain deformed doses. Dosimetric and image registration results based CBCT images reconstructed by two algorithms were compared at three levels: (A) voxel doses over entire dose calculation volume, (B) clinical constraint results on targets and sensitive structures, and (C) contours propagated to CBCT images using DIR results based on three algorithms (SmartAdapt, Velocity, and Elastix) were compared with manually delineated contours as ground truth.

RESULTS

(A) Average daily dose differences and average normalized DDA differences between FDK_CBCT and iterative_CBCT were ≤1 cGy. Maximum daily point dose differences increased from 0.22 ± 0.06 Gy (before the deformable dose mapping operation) to 1.33 ± 0.38 Gy after the deformable dose mapping. Maximum differences of normalized DDA per fraction were up to 0.80 Gy (0.42 ± 0.19 Gy). (B) Differences in target minimum doses were up to 8.31 Gy (-0.62 ± 4.60 Gy) and differences in critical structure doses were 0.70 ± 1.49 Gy. (C) For mapped prostate contours based on iterative_CBCT (relative to standard FDK_CBCT), dice similarity coefficient increased by 0.10 ± 0.09 (p < 0.0001), mass center distances decreased by 2.5 ± 3.0 mm (p < 0.00005), and Hausdorff distances decreased by 3.3 ± 4.4 mm (p < 0.00015).

CONCLUSIONS

The new iterative CBCT reconstruction algorithm leads to different mapped volumes of interest, deformed and cumulative doses than results based on conventional FDK_CBCT.

Development of an automated radiotherapy dose accumulation workflow for locally advanced high-risk prostate cancer - A technical report

An automated dose accumulation and contour propagation workflow using daily cone beam computed tomography (CBCTs) images for prostate cases that require pelvic lymph nodes irradiation (PLNs) was developed. This workflow was constructed using MIM® software with the intention to provide accurate dose transformations for plans with two different isocentres, whereby two sequential treatment phases were prescribed. The pre-processing steps for data extractions from treatment plans, CBCTs, determination of couch shift information and management of missing CBCTs are described. To ensure that the imported translational couch shifts were in the correct orientation and readable in MIM, phantom commissioning was performed. For dose transformation, rigid registration with corrected setup shifts and scaled fractional dose was performed for pCT to daily CBCTs, which were then deformed onto CBCT1 . Fractional dose summation resulted in the final accumulated dose for the patient allowing differences in dosimetry between the planned and accumulated dose to be analysed. Contour propagations of the prostate, bladder and rectum were performed within the same workflow. Transformed contours were then deformed onto daily CBCTs to generate trending reports for analysis, including Dice Similarity Coefficient (DSC) and Mean Distance to Agreement (MDA). Results obtained from phantom commissioning (DSC = 0.96, MDA = 0.89 mm) and geometrical analysis of the propagated contours for twenty patients; prostate (DSC: 0.9 ± 0.0, MDA: 1.0 ± 0.3 mm), rectum (DSC: 0.8 ± 0.1, mm, MDA: 1.7 ± 0.6 mm) and bladder (DSC: 0.8 ± 0.1, MDA: 2.8 ± 1.0 mm) were within clinically accepted tolerances for both DSC (>0.8) and MDA (< 0.3 mm). The developed workflow is being performed on a larger patient cohort for predictive model building, with the goal of correlating observed toxicity with the actual accumulated dose received by the patient.

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model

OBJECTIVE. Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation. MATERIALS AND METHODS. A retrospective cohort of 648 patients who underwent prostate MRI between February 2015 and November 2018 at a single center was used for training and validation. A deep learning approach combining 2D and 3D architecture was used for training, which incorporated transfer learning. A data augmentation strategy was used that was specific to the deformations, intensity, and alterations in image quality seen on radiology images. Five independent datasets, four of which were from outside centers, were used for testing, which was conducted with and without fine-tuning of the original model. The Dice similarity coefficient was used to evaluate model performance. RESULTS. When prostate segmentation models utilizing transfer learning were applied to the internal validation cohort, the mean Dice similarity coefficient was 93.1 for whole prostate and 89.0 for transition zone segmentations. When the models were applied to multiple test set cohorts, the improvement in performance achieved using data augmentation alone was 2.2% for the whole prostate models and 3.0% for the transition zone segmentation models. However, the best test-set results were obtained with models fine-tuned on test center data with mean Dice similarity coefficients of 91.5 for whole prostate segmentation and 89.7 for transition zone segmentation. CONCLUSION. Transfer learning allowed for the development of a high-performing prostate segmentation model, and data augmentation and fine-tuning approaches improved performance of a prostate segmentation model when applied to datasets from external centers.

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

Importance

Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions.

Objective

To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy.

Design, Setting, and Participants

This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020.

Main Outcomes and Measures

The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch.

Results

A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time.

Conclusions and Relevance

In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

OBJECTIVES

Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration.

METHODS

A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018.

RESULTS

There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively).

CONCLUSIONS

This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.

Study of Asymmetric Margins in Prostate Cancer Radiation Therapy Using Fuzzy Logic

PURPOSE

The purpose of present study is to estimate asymmetric margins of prostate target volume based on biological limitations with help of knowledge based fuzzy logic considering the effect of organ motion and setup errors.

MATERIALS AND METHODS

A novel application of fuzzy logic modelling technique considering radiotherapy uncertainties including setup, delineation and organ motion was used in this study to derive margins. The new margin was applied in prostate cancer treatment planning and the results compared very well to current techniques Here volumetric modulated arc therapy treatment plans using stepped increments of asymmetric margins of planning target volume (PTV) were performed to calculate the changes in prostate radiobiological indices and results were used to formulate the rule based and membership function for Mamdani-type fuzzy inference system. The optimum fuzzy rules derived from input data, the clinical goals and knowledge-based conditions imposed on the margin limits. The PTV margin obtained using the fuzzy model was compared to the commonly used margin recipe.

RESULTS

For total displacement standard errors ranging from 0 to 5 mm the fuzzy PTV margin was found to be up to 0.5 mm bigger than the vanHerk derived margin, however taking the modelling uncertainty into account results in a good match between the PTV margin calculated using our model and the one based on van Herk et al. formulation for equivalent errors of up to 5 mm standard deviation (s. d.) at this range. When the total displacement standard errors exceed 5 mm s. d., the fuzzy margin remained smaller than the van Herk margin.

CONCLUSION

The advantage of using knowledge based fuzzy logic is that a practical limitation on the margin size is included in the model for limiting the dose received by the critical organs. It uses both physical and radiobiological data to optimize the required margin as per clinical requirement in real time or adaptive planning, which is an improvement on most margin models which mainly rely on physical data only.

Optimizing HR-pQCT workflow: a comparison of bias and precision error for quantitative bone analysis

Manual correction of automatically generated contours for high-resolution peripheral quantitative computed tomography can be time consuming and introduces precision error. However, bias related to the automated protocol is unknown. This study provides insight into error bias that is present when using uncorrected contours and inter-operator precision error based on operator training.

INTRODUCTION

High-resolution peripheral quantitative computed tomography workflow includes manually correcting contours generated by the manufacturer's automated protocol. There is interest in minimizing corrections to save time and reduce precision error; however, bias related to the automated protocol is unknown. This study quantifies error bias when contours are uncorrected and identifies the impact of operator training on bias and precision error.

METHODS

Forty-five radii and tibiae scans across a representative range of bone density were analyzed using the automated and manually corrected contours of three operators, with training ranging from beginner to expert, and compared with a "ground truth" to estimate bias. Inter-operator precision was measured across operators.

RESULTS

The tibia had greater error bias than the radius when contours were uncorrected, with compartmental bone mineral densities and cortical microarchitecture having greatest biases, which could have significant implications for interpretation of studies using this skeletal site. Bias and precision error were greatest when contours were corrected by the beginner operator; however, when this operator was removed, bias was no longer present and inter-operator precision was between 0.01 and 3.74% for all parameters except cortical porosity.

CONCLUSION

These findings establish the need for manual correction and provide guidance on operator training needed to maximize workflow efficiency.

Optimizing planning CT using past CT images for prostate cancer volumetric modulated arc therapy

This study aimed to evaluate a new method to optimize planning computed tomography (CT) using three-dimensional (3D) displacement error between the planning and diagnosed past CT scans. Thirty-two patients undergoing volumetric modulated arc therapy for prostate cancer were evaluated for a 3D displacement error between bone- and prostate-matching spatial coordinates using multiple acquisition planning CT (MPCT) scans. Each MPCT image and a past CT image were used to perform rigid image registration (RIR) and deformable image registration (DIR), and the 3D displacement error was calculated. Correlations of the 3D displacement error in each MPCT scan and between the MPCT and past CT were evaluated based on RIR and DIR, respectively. The 3D displacement error in the MPCT images exhibited moderate correlation with the 3D displacement error between MPCT and past CT for both RIR (adjusted r² = 0.495) and DIR (adjusted r² = 0.398). In the correlation analysis between MPCT and past CT, image pairs with 3D displacement errors ≥ 6 mm were significantly different from those with errors < 6 mm (p < 0.0001). Past CT images were different from the planning CT images, which can be attributed to setup tools, flat-top plates, and physical differences due to the presence or absence of urine as well as prescription effects. The relationship between bone and prostate exhibited small deviations between the planning and past CT regardless of pretreatment. The prostate, which only has a slight effect on the displacement between it and bladder volume, was covered with a stiff pelvic bone. As a result, MPCT images exhibited correlations with past CT images of various difference states such as body positions. Finally, large 3D displacement errors in prostate position were caused by pelvic tension and stress, which can be detected using diagnosed past CT images instead of requiring MPCT scans. By comparing past and planning CT images, the random displacement error in the planning CT scan can be avoided by evaluating 3D displacement errors. The new method using the past CT images can estimate the displacement error of the prostate during the treatment period with 1 plan CT scan only, and it helps improve the treatment accuracy.

Male pelvic multi-organ segmentation aided by CBCT-based synthetic MRI

To develop an automated cone-beam computed tomography (CBCT) multi-organ segmentation method for potential CBCT-guided adaptive radiation therapy workflow. The proposed method combines the deep leaning-based image synthesis method, which generates magnetic resonance images (MRIs) with superior soft-tissue contrast from on-board setup CBCT images to aid CBCT segmentation, with a deep attention strategy, which focuses on learning discriminative features for differentiating organ margins. The whole segmentation method consists of 3 major steps. First, a cycle-consistent adversarial network (CycleGAN) was used to estimate a synthetic MRI (sMRI) from CBCT images. Second, a deep attention network was trained based on sMRI and its corresponding manual contours. Third, the segmented contours for a query patient was obtained by feeding the patient's CBCT images into the trained sMRI estimation and segmentation model. In our retrospective study, we included 100 prostate cancer patients, each of whom has CBCT acquired with prostate, bladder and rectum contoured by physicians with MRI guidance as ground truth. We trained and tested our model with separate datasets among these patients. The resulting segmentations were compared with physicians' manual contours. The Dice similarity coefficient and mean surface distance indices between our segmented and physicians' manual contours (bladder, prostate, and rectum) were 0.95  ±  0.02, 0.44  ±  0.22 mm, 0.86  ±  0.06, 0.73  ±  0.37 mm, and 0.91  ±  0.04, 0.72  ±  0.65 mm, respectively. We have proposed a novel CBCT-only pelvic multi-organ segmentation strategy using CBCT-based sMRI and validated its accuracy against manual contours. This technique could provide accurate organ volume for treatment planning without requiring MR images acquisition, greatly facilitating routine clinical workflow.

Observer uncertainties of soft tissue-based patient positioning in IGRT

Purpose

There remain uncertainties due to inter‐ and intraobserver variability in soft‐tissue‐based patient positioning even with the use of image‐guided radiation therapy (IGRT). This study aimed to reveal observer uncertainties of soft‐tissue‐based patient positioning on cone‐beam computed tomography (CBCT) images for prostate cancer IGRT.

Methods

Twenty‐six patients (7–8 fractions/patient, total number of 204 fractions) who underwent IGRT for prostate cancer were selected. Six radiation therapists retrospectively measured prostate cancer location errors (PCLEs) of soft‐tissue‐based patient positioning between planning CT (pCT) and pretreatment CBCT (pre‐CBCT) images after automatic bone‐based registration. Observer uncertainties were evaluated based on residual errors, which denoted the differences between soft‐tissue and reference positioning errors. Reference positioning errors were obtained as PCLEs of contour‐based patient positioning between pCT and pre‐CBCT images. Intraobserver variations were obtained from the difference between the first and second soft‐tissue‐based patient positioning repeated by the same observer for each fraction. Systematic and random errors of inter‐ and intraobserver variations were calculated in anterior–posterior (AP), superior–inferior (SI), and left–right (LR) directions. Finally, clinical target volume (CTV)‐to‐planning target volume (PTV) margins were obtained from systematic and random errors of inter‐ and intraobserver variations in AP, SI, and LR directions.

Results

Interobserver variations in AP, SI, and LR directions were 0.9, 0.9, and 0.5 mm, respectively, for the systematic error, and 1.8, 2.2, and 1.1 mm, respectively, for random error. Intraobserver variations were <0.2 mm in all directions. CTV‐to‐PTV margins in AP, SI, and LR directions were 3.5, 3.8, and 2.1 mm, respectively.

Conclusion

Intraobserver variability was sufficiently small and would be negligible. However, uncertainties due to interobserver variability for soft‐tissue‐based patient positioning using CBCT images should be considered in CTV‐to‐PTV margins.

Contrast enhanced oesophageal avoidance for stereotactic body radiotherapy: Barium vs. Gastrografin

INTRODUCTION

SABR may facilitate treatment in a greater proportion of locally-advanced NSCLC patients, just as it has for early-stage disease. The oesophagus is one of the key dose-limiting organs and visualization during IGRT would better ensure toxicity is avoided. As the oesophagus is poorly seen on CBCT, we assessed the extent to which this is improved using two oral contrast agents.

MATERIALS & METHODS

Six patients receiving radiotherapy for Stage I-III NSCLC were assigned to receive 50 mL Gastrografin or 50 mL barium sulphate prior to simulation and pre-treatment CBCTs. Three additional patients who did not receive contrast were included as a control group. Oesophageal visibility was determined by assessing concordance between six experienced observers in contouring the organ. 36 datasets and 216 contours were analysed. A STAPLE contour was created and compared to each individual contour. Descriptive statistics were used and a Kappa statistic, Dice Coefficient and Hausdorff distance were calculated and compared using a t-test. Contrast-induced artefact was assessed by observer scoring.

RESULTS

Both contrast agents significantly improved the consistency of oesophagus localisation on CBCT across all comparison metrics compared to CBCTs without contrast. Barium performed significantly better than Gastrografin with improved kappa statistics (p = 0.007), dice coefficients (p < 0.001) and Hausdorff distances (p = 0.002), although at a cost of increased image artefact.

DISCUSSION

Barium produced lower delineation uncertainties but more image artefact, compared to Gastrografin and no contrast. It is feasible to use oral contrast as a tool in IGRT to help guide clinicians and therapists with online matching and monitoring of the oesophageal position.

Implementing user-defined atlas-based auto-segmentation for a large multi-centre organisation: the Australian Experience

INTRODUCTION

Contouring has become an increasingly important aspect of radiation therapy due to inverse planning, and yet is extremely time-consuming. To improve contouring efficiency and reduce potential inter-observer variation, the atlas-based auto-segmentation (ABAS) function in Velocity was introduced to ICON cancer centres (ICC) throughout Australia as a solution for automatic contouring.

METHODS

This paper described the implementation process of the ABAS function and the construction of user-defined atlas sets and compared the contouring efficiency before and after the introduction of ABAS.

RESULTS

The results indicate that the main limitation to the ABAS performance was Velocity's sub-optimal atlas selection method. Three user-defined atlas sets were constructed. Results suggested that the introduction of the ABAS saved at least 5 minutes of manual contouring time (P < 0.05), although further verification was required due to limitations in the data collection method. The pilot rollout adopting a 'champion' approach was successful and provided an opportunity to improve the user-defined atlases prior to the national implementation.

CONCLUSION

The implementation of user-defined ABAS for head and neck (H&N) and female thorax patients at ICCs was successful, which achieved at least 5 minutes of efficiency gain.

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.

Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation

PURPOSE

This study aimed to evaluate the clinical utility of a novel iterative cone beam computed tomography (CBCT) reconstruction algorithm for prostate and head and neck (HN) cancer.

METHODS AND MATERIALS

A total of 10 patients with HN and 10 patients with prostate cancer were analyzed. For each patient, raw CBCT acquisition data were used to reconstruct images with a currently available algorithm (FDK_CBCT) and novel iterative algorithm (Iterative_CBCT). Quantitative contouring variation analysis was performed using structures delineated by several radiation oncologists. For prostate, observers contoured the prostate, proximal 2 cm seminal vesicles, bladder, and rectum. For HN, observers contoured the brain stem, spinal canal, right-left parotid glands, and right-left submandibular glands. Observer contours were combined to form a reference consensus contour using the simultaneous truth and performance level estimation method. All observer contours then were compared with the reference contour to calculate the Dice coefficient, Hausdorff distance, and mean contour distance (prostate contour only). Qualitative image quality analysis was performed using a 5-point scale ranging from 1 (much superior image quality for Iterative_CBCT) to 5 (much inferior image quality for Iterative_CBCT).

RESULTS

The Iterative_CBCT data sets resulted in a prostate contour Dice coefficient improvement of approximately 2.4% (P = .029). The average prostate contour Dice coefficient for the Iterative_CBCT data sets was improved for all patients, with improvements up to approximately 10% for 1 patient. The mean contour distance results indicate an approximate 15% reduction in mean contouring error for all prostate regions. For the parotid contours, Iterative_CBCT data sets resulted in a Hausdorff distance improvement of approximately 2 mm (P < .01) and an approximate 2% improvement in Dice coefficient (P = .03). The Iterative_CBCT data sets were scored as equivalent or of better image quality for 97.3% (prostate) and 90.0% (HN) of the patient data sets.

CONCLUSIONS

Observers noted an improvement in image uniformity, noise level, and overall image quality for Iterative_CBCT data sets. In addition, expert observers displayed an improved ability to consistently delineate soft tissue structures, such as the prostate and parotid glands. Thus, the novel iterative reconstruction algorithm analyzed in this study is capable of improving the visualization for prostate and HN cancer image guided radiation therapy.

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN)

PURPOSE

Recent advances in deep neural networks (DNNs) have unlocked opportunities for their application for automatic image segmentation. We have evaluated a DNN-based algorithm for automatic segmentation of the prostate gland on a large cohort of patient images.

METHODS AND MATERIALS

Planning-CT data sets for 1114 patients with prostate cancer were retrospectively selected and divided into 2 groups. Group A contained 1104 data sets, with 1 physician-generated prostate gland contour for each data set. Among these image sets, 771 were used for training, 193 for validation, and 140 for testing. Group B contained 10 data sets, each including prostate contours delineated by 5 independent physicians and a consensus contour generated using the STAPLE method in the CERR software package. All images were resampled to a spatial resolution of 1 × 1 × 1.5 mm. A region (128 × 128 × 64 voxels) containing the prostate was selected to train a DNN. The best-performing model on the validation data sets was used to segment the prostate on all testing images. Results were compared between DNN and physician-generated contours using the Dice similarity coefficient, Hausdorff distances, regional contour distances, and center-of-mass distances.

RESULTS

The mean Dice similarity coefficients between DNN-based prostate segmentation and physician-generated contours for test data in Group A, Group B, and Group B-consensus were 0.85 ± 0.06 (range, 0.65-0.93), 0.85 ± 0.04 (range, 0.80-0.91), and 0.88 ± 0.03 (range, 0.82-0.92), respectively. The Hausdorff distance was 7.0 ± 3.5 mm, 7.3 ± 2.0 mm, and 6.3 ± 2.0 mm for Group A, Group B, and Group B-consensus, respectively. The mean center-of-mass distances for all 3 data set groups were within 5 mm.

CONCLUSIONS

A DNN-based algorithm was used to automatically segment the prostate for a large cohort of patients with prostate cancer. DNN-based prostate segmentations were compared to the consensus contour for a smaller group of patients; the agreement between DNN segmentations and consensus contour was similar to the agreement reported in a previous study. Clinical use of DNNs is promising, but further investigation is warranted.

LEFMIS: locally-oriented evaluation framework for medical image segmentation algorithms

This article proposes a novel framework for the locally-oriented evaluation of segmentation algorithms (LEFMIS). The presented approach is robust and takes into account local inter/intra-observer variability and the anisotropy of medical images. What is more, the framework makes it possible to distinguish types of error locally. These features are crucial in the context of cancer image data. The proposed framework is based on use of the signed anisotropic Euclidean distance transform and the distance projection. It can be used easily in many different applications with or without additional expert outlines (both inter- and intra-observer variability). The performance of the proposed framework is depicted using both artificial and kidney cancer CT data with experts' manual outlines. In the article, in the case of artificial data, it is presented that the manual outlines dispersion is symmetric in relation to the truth border. The effectiveness of the selected segmentation algorithm was analysed in the context of kidney cancer using computed tomography data. For the calculated local inter-observer variability, 80.11% of the surface points generated by the kidney segmentation algorithm are within one expert outline standard deviation and 97.96% are within five. An error distribution shift in the direction of type I error equivalent was also observed. Finally, the significance of the local estimation of error type differences is presented. The article shows the greater usefulness and flexibility of the proposed framework in comparison to the state-of-the-art methods. The exemplary usage of the LEFMIS with or without inter-/intra-observer variability is also presented.

The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy

Deformable image registration (DIR) is the key process for contour propagation and dose accumulation in adaptive radiation therapy (ART). However, currently, ART suffers from a lack of understanding of “robustness” of the process involving the image contour based on DIR and subsequent dose variations caused by algorithm itself and the presetting parameters. The purpose of this research is to evaluate the DIR caused variations for contour propagation and dose accumulation during ART using the RayStation treatment planning system. Ten head and neck cancer patients were selected for retrospective studies. Contours were performed by a single radiation oncologist and new treatment plans were generated on the weekly CT scans for all patients. For each DIR process, four deformation vector fields (DVFs) were generated to propagate contours and accumulate weekly dose by the following algorithms: (a) ANACONDA with simple presetting parameters, (b) ANACONDA with detailed presetting parameters, (c) MORFEUS with simple presetting parameters, and (d) MORFEUS with detailed presetting parameters. The geometric evaluation considered DICE coefficient and Hausdorff distance. The dosimetric evaluation included D₉₅, D_max, D_mean, D_min, and Homogeneity Index. For geometric evaluation, the DICE coefficient variations of the GTV were found to be 0.78 ± 0.11, 0.96 ± 0.02, 0.64 ± 0.15, and 0.91 ± 0.03 for simple ANACONDA, detailed ANACONDA, simple MORFEUS, and detailed MORFEUS, respectively. For dosimetric evaluation, the corresponding Homogeneity Index variations were found to be 0.137 ± 0.115, 0.006 ± 0.032, 0.197 ± 0.096, and 0.006 ± 0.033, respectively. The coherent geometric and dosimetric variations also consisted in large organs and small organs. Overall, the results demonstrated that the contour propagation and dose accumulation in clinical ART were influenced by the DIR algorithm, and to a greater extent by the presetting parameters. A quality assurance procedure should be established for the proper use of a commercial DIR for adaptive radiation therapy.

Novel Wavelet-Based Segmentation of Prostate CBCT Images with Implanted Calypso Transponders

Segmentation of prostate Cone Beam CT (CBCT) images is an essential step towards real-time adaptive radiotherapy (ART). It is challenging for Calypso patients, as more artifacts generated by the beacon transponders are present on the images. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. For a given CBCT, a Moving Window-Based Double Haar (MWDH) transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied on the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary (segmented) image of the object of interest is therefore obtained. 5 hypofractionated Calypso prostate patients with daily CBCT were studied. DICE, Sensitivity, Inclusiveness and ΔV were used to evaluate the segmentation result.

Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration

The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.

Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy

Automated atlas-based segmentation (ABS) algorithms present the potential to reduce the variability in volume delineation. Several vendors offer software that are mainly used for cranial, head and neck, and prostate cases. The present study will compare the contours produced by a radiation oncologist to the contours computed by different automated ABS algorithms for prostate bed cases, including femoral heads, bladder, and rectum. Contour comparison was evaluated by different metrics such as volume ratio, Dice coefficient, and Hausdorff distance. Results depended on the volume of interest showed some discrepancies between the different software. Automatic contours could be a good starting point for the delineation of organs since efficient editing tools are provided by different vendors. It should become an important help in the next few years for organ at risk delineation.

Radiobiologically optimized couch shift: A new localization paradigm using cone-beam CT for prostate radiotherapy

PURPOSE

To present a novel positioning strategy which optimizes radiation delivery by utilizing radiobiological response knowledge and evaluate its use during prostate external beam radiotherapy.

METHODS

Five patients with low or intermediate risk prostate cancer were evaluated retrospectively in this IRB-approved study. For each patient, a VMAT plan with one 358° arc was generated on the planning CT (PCT) to deliver 78 Gy in 39 fractions. Five representative pretreatment cone beam CTs (CBCT) were selected for each patient. The CBCT images were registered to PCT by a human observer, which consisted of an initial automated registration with three degrees-of-freedom, followed by manual adjustment for agreement at the prostate/rectal wall interface. To determine the optimal treatment position for each CBCT, a search was performed centering on the observer-matched position (OM-position) utilizing a score function based on radiobiological and dosimetric indices (EUDprostate, D99prostate, NTCPrectum, and NTCPbladder) for the prostate, rectum, and bladder. We termed the optimal treatment position the radiobiologically optimized couch shift position (ROCS-position).

RESULTS

The dosimetric indices, averaged over the five patients' treatment plans, were (mean ± SD) 79.5 ± 0.3 Gy (EUDprostate), 78.2 ± 0.4 Gy (D99prostate), 11.1% ± 2.7% (NTCPrectum), and 46.9% ± 7.6% (NTCPbladder). The corresponding values from CBCT at the OM-positions were 79.5 ± 0.6 Gy (EUDprostate), 77.8 ± 0.7 Gy (D99prostate), 12.1% ± 5.6% (NTCPrectum), and 51.6% ± 15.2% (NTCPbladder), respectively. In comparison, from CBCT at the ROCS-positions, the dosimetric indices were 79.5 ± 0.6 Gy (EUDprostate), 77.3 ± 0.6 Gy (D99prostate), 8.0% ± 3.3% (NTCPrectum), and 46.9% ± 15.7% (NTCPbladder). Excessive NTCPrectum was observed on Patient 5 (19.5% ± 6.6%) corresponding to localization at OM-position, compared to the planned value of 11.7%. This was mitigated with radiobiologically optimized localization, resulting in a reduced NTCPrectum value of 11.3% ± 3.5%. Overall, the treatment position optimization resulted in similar target dose coverage with reduced risk to rectum.

CONCLUSIONS

These encouraging results illustrate the potential advantage of applying radiobiologically optimized correction for online image-guided radiotherapy of prostate patients.