A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties

BACKGROUND

Uncertainties in radiotherapy cause deviation from the planned dose distribution and may result in delivering a treatment that fails to meet clinical objectives. The impact of uncertainties is unique to the patient anatomy and the needle locations in HDR prostate brachytherapy. Evaluating this impact during treatment planning is not common practice, relying on margins around the target or organs-at-risk to account for uncertainties.

PURPOSE

A robust evaluation framework for HDR prostate brachytherapy treatment plans was evaluated on 49 patient plans, measuring the range of possible dosimetric outcomes to the patient due to 14 major uncertainties.

METHODS

Patient plans were evaluated for their robustness to uncertainties by simulating probable uncertainty scenarios. Five-thousand probabilistic and 1943 worst-case scenarios per patient were simulated by changing the position and size of structures and length of dwell times from their nominal values. For each uncertainty scenario, the prostate D90 and maximum doses to the urethra, D0.01cc , and rectum, D0.1cc , were calculated.

RESULTS

The D90 was an average 1.16 ± 0.51% (mean ± SD) below nominal values for the probabilistic scenarios; the D0.01cc metric was 2.24 ± 0.90% higher; and D0.1cc was greater by 0.48 ± 0.30%. The D0.01cc and D90 metrics were more sensitive to uncertainties than D0.1cc , with a median of 79.0% and 84.9% of probabilistic scenarios passing the constraints, compared to 96.5%. The median pass-rate for scenarios that passed all three metrics simultaneously was 63.4%.

CONCLUSIONS

Assessing treatment plan robustness improves plan quality assurance, is achievable in less than 1-min, and identifies treatment plans with poor robustness, allowing re-optimization before delivery.

Corrigendum: Comparison of 3DCRT and IMRT out-of-field doses in pediatric patients using Monte Carlo simulations with treatment planning system calculations and measurements

[This corrects the article DOI: 10.3389/fonc.2022.879167.].

A real-time contouring feedback tool for consensus-based contour training

Purpose

Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring.

Methods

We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group.

Results

For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV.

Conclusions

A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

[Deep learning-based dose prediction in radiotherapy planning for head and neck cancer]

OBJECTIVE

To propose an deep learning-based algorithm for automatic prediction of dose distribution in radiotherapy planning for head and neck cancer.

METHODS

We propose a novel beam dose decomposition learning (BDDL) method designed on a cascade network. The delivery matter of beam through the planning target volume (PTV) was fitted with the pre-defined beam angles, which served as an input to the convolution neural network (CNN). The output of the network was decomposed into multiple sub-fractions of dose distribution along the beam directions to carry out a complex task by performing multiple simpler sub-tasks, thus allowing the model more focused on extracting the local features. The subfractions of dose distribution map were merged into a distribution map using the proposed multi-voting mechanism. We also introduced dose distribution features of the regions-of-interest (ROIs) and boundary map as the loss function during the training phase to serve as constraining factors of the network when extracting features of the ROIs and areas of dose boundary. Public datasets of radiotherapy planning for head and neck cancer were used for obtaining the accuracy of dose distribution of the BDDL method and for implementing the ablation study of the proposed method.

RESULTS

The BDDL method achieved a Dose score of 2.166 and a DVH score of 1.178 (P < 0.05), demonstrating its superior prediction accuracy to that of current state-ofthe-art (SOTA) methods. Compared with the C3D method, which was in the first place in OpenKBP-2020 Challenge, the BDDL method improved the Dose score and DVH score by 26.3% and 30%, respectively. The results of the ablation study also demonstrated the effectiveness of each key component of the BDDL method.

CONCLUSION

The BDDL method utilizes the prior knowledge of the delivery matter of beam and dose distribution in the ROIs to establish a dose prediction model. Compared with the existing methods, the proposed method is interpretable and reliable and can be potentially applied in clinical radiotherapy.

Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning

Contouring has become an increasingly important aspect of radiotherapy due to inverse planning. Several studies have suggested that the clinical implementation of automated contouring tools can reduce inter-observer variation while increasing contouring efficiency, thereby improving the quality of radiotherapy treatment and reducing the time between simulation and treatment. In this study, a novel, commercial automated contouring tool based on machine learning, the AI-Rad Companion Organs RT™ (AI-Rad) software (Version VA31) (Siemens Healthineers, Munich, Germany), was assessed against both manually delineated contours and another commercially available automated contouring software, Varian Smart Segmentation™ (SS) (Version 16.0) (Varian, Palo Alto, CA, United States). The quality of contours generated by AI-Rad in Head and Neck (H&N), Thorax, Breast, Male Pelvis (Pelvis_M), and Female Pelvis (Pevis_F) anatomical areas was evaluated both quantitatively and qualitatively using several metrics. A timing analysis was subsequently performed to explore potential time savings achieved by AI-Rad. Results showed that most automated contours generated by AI-Rad were not only clinically acceptable and required minimal editing, but also superior in quality to contours generated by SS in multiple structures. In addition, timing analysis favored AI-Rad over manual contouring, indicating the largest time saving (753s per patient) in the Thorax area. AI-Rad was concluded to be a promising automated contouring solution that generated clinically acceptable contours and achieved time savings, thereby greatly benefiting the radiotherapy process.

Attitudes and access to resources and strategies to improve quality of radiotherapy among US radiation oncologists: A mixed methods study

INTRODUCTION

We aimed to assess contouring-related practices among US radiation oncologists and explore how access to and use of resources and quality improvement strategies vary based on individual- and organization-level factors.

METHODS

We conducted a mixed methods study with a sequential explanatory design. Surveys were emailed to a random 10% sample of practicing US radiation oncologists. Participating physicians were invited to a semi-structured interview. Kruskal-Wallis and Wilcoxon rank-sum tests and a multivariable regression model were used to evaluate associations. Interview data were coded using thematic content analysis.

RESULTS

Survey overall response rate was 24%, and subsequent completion rate was 97%. Contouring-related questions arise in ≥50% of clinical cases among 73% of respondents. Resources accessed first include published atlases (75%) followed by consulting another radiation oncologist (60%). Generalists access consensus guidelines more often than disease-site specialists (P = 0.04), while eContour.org is more often used by generalists (OR 4.3, 95% CI 1.2-14.8) and younger physicians (OR 1.33 for each 5-year increase, 95% CI 1.08-1.67). Common physician-reported barriers to optimizing contour quality are time constraints (58%) and lack of access to disease-site specialists (21%). Forty percent (40%, n = 14) of physicians without access to disease-site specialists indicated it could facilitate the adoption of new treatments. Almost all (97%) respondents have formal peer review, but only 43% have contour-specific review, which is more common in academic centres (P = 0.02).

CONCLUSION

Potential opportunities to improve radiation contour quality include improved access to disease-site specialists and contour-specific peer review. Physician time must be considered when designing new strategies.

Comparison of 3DCRT and IMRT out-of-field doses in pediatric patients using Monte Carlo simulations with treatment planning system calculations and measurements

3DCRT and IMRT out-of-field doses in pediatric patients were compared using Monte Carlo simulations with treatment planning system calculations and measurements.

Purpose

Out-of-field doses are given to healthy tissues, which may allow the development of second tumors. The use of IMRT in pediatric patients has been discussed, as it leads to a "bath" of low doses to large volumes of out-of-field organs and tissues. This study aims to compare out-of-field doses in pediatric patients comparing IMRT and 3DCRT techniques using measurements, Monte Carlo (MC) simulations, and treatment planning system (TPS) calculations.

Materials and methods

A total dose of 54 Gy was prescribed to a PTV in the brain of a pediatric anthropomorphic phantom, for both techniques. To assess the out-of-field organ doses for both techniques, two treatment plans were performed with the 3DCRT and IMRT techniques in TPS. Measurements were carried out in a LINAC using a pediatric anthropomorphic phantom and thermoluminescent dosimeters to recreate the treatment plans, previously performed in the TPS. A computational model of a LINAC, the associated multileaf collimators, and a voxelized pediatric phantom implemented in the Monte Carlo N-Particle 6.1 computer program were also used to perform MC simulations of the out-of-field organ doses, for both techniques.

Results

The results obtained by measurements and MC simulations indicate a significant increase in dose using the IMRT technique when compared to the 3DCRT technique. More specifically, measurements show higher doses with IMRT, namely, in right eye (13,041 vs. 593 mGy), left eye (6,525 vs. 475 mGy), thyroid (79 vs. 70 mGy), right lung (37 vs. 28 mGy), left lung (27 vs. 20 mGy), and heart (31 vs. 25 mGy). The obtained results indicate that out-of-field doses can be seriously underestimated by TPS.

Discussion

This study presents, for the first time, out-of-field dose measurements in a realistic scenario and calculations for IMRT, centered on a voxelized pediatric phantom and an MC model of a medical LINAC, including MLC with log file-based simulations. The results pinpoint significant discrepancies in out-of-field doses for the two techniques and are a cause of concern because TPS calculations cannot accurately predict such doses. The obtained doses may presumably increase the risk of development of second tumors.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer

BACKGROUND AND PURPOSE

Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.

MATERIALS AND METHODS

Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.

RESULTS

This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.

CONCLUSION

Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Criteria for Verification and Replanning Based on the Adaptive Radiotherapy Protocol "Best for Adaptive Radiotherapy" in Head and Neck Cancer

No clear criteria have yet been established to guide decision-making for patient selection and the optimal timing of adaptive radiotherapy (ART) based on image-guided radiotherapy (IGRT). We have developed a novel protocol—the Best for Adaptive Radiotherapy (B-ART) protocol—to guide patient selection for ART. The aim of the present study is to describe this protocol, to evaluate its validity in patients with head and neck (HN) cancer, and to identify the anatomical and clinical predictors of the need for replanning. We retrospectively evaluated 82 patients with HN cancer who underwent helical tomotherapy (HT) and subsequently required replanning due to soft tissue changes upon daily MVCT. Under the proposed criteria, patients with anatomical changes >3 mm on three to four consecutive scans are candidates for ART. We compared the volumes on the initial CT scan (iCT) and the replanning CT (rCT) scan for the clinical target volumes (CTV1, referring to primary tumor or tumor bed and CTV2, metastatic lymph nodes) and for the parotid glands (PG) and body contour (B-body). The patients were stratified by primary tumor localization, clinical stage, and treatment scheme. The main reasons for replanning were: (1) a planning target volume (PTV) outside the body contour (n = 70; 85.4%), (2) PG shrinkage (n = 69; 84.1%), (3) B-body deviations (n = 69; 84.1%), and (4) setup deviations (n = 40; 48.8%). The replanning decision was made, on average, during the fourth week of treatment (n = 47; 57.3%). The mean reductions in the size of the right and left PG volumes were 6.31 cc (20.9%) and 5.98 cc (20.5%), respectively (p < 0.001). The reduction in PG volume was ≥30% in 30 patients (36.6%). The volume reduction in all of the anatomical structures was statistically significant. Four variables—advanced stage disease (T3−T4), chemoradiation, increased weight loss, and oropharyngeal localization—were significantly associated with the need for ART. The B-ART protocol provides clear criteria to eliminate random errors, and to allow for an early response to relevant changes in target volumes.

Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation

Purpose

We aimed to evaluate the accuracy of T₁ and T₂ mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC^®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning.

Methods

The accuracy of T₁ and T₂ measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T₁ and T₂ measurements were performed on 15 sessions with the HNU coil. The T₁ and T₂ synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T₁ and T₂in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared.

Results

The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T₁ values between 490 and 2,385 ms and T₂ values between 25 and 400 ms. MAGiC^® gave discordant results for T₁ values below 220 ms (bias with the reference values, from 38% to 1,620%). T₂ measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T₁ and T₂ by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%).

Conclusion

This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC^®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC^® could potentially be used for prostate qMRI.

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

Automatic segmentation of organs-at-risks of nasopharynx cancer and lung cancer by cross-layer attention fusion network with TELD-Loss

PURPOSE

Radiotherapy is one of the main treatments of nasopharyngeal cancer (NPC) and lung cancer. Accurate segmentation of organs at risks (OARs) in CT images is a key step in radiotherapy planning for NPC and lung cancer. However, the segmentation of OARs is influenced by the highly imbalanced size of organs, which often results in very poor segmentation results for small and difficult-to-segment organs. In addition, the complex morphological changes and fuzzy boundaries of OARs also pose great challenges to the segmentation task. In this paper, we propose a cross-layer attention fusion network (CLAF-CNN) to solve the problem of accurately segmenting OARs.

METHODS

In CLAF-CNN, we integrate the spatial attention maps of the adjacent spatial attention modules to make the segmentation targets more accurately focused, so that the network can capture more target-related features. In this way, the spatial attention modules in the network can be learned and optimized together. In addition, we introduce a new Top-K exponential logarithmic Dice loss (TELD-Loss) to solve the imbalance problem in OAR segmentation. The TELD-Loss further introduces a Top-K optimization mechanism based on Dice loss and exponential logarithmic loss, which makes the network pay more attention to small organs and difficult-to-segment organs, so as to enhance the overall performance of the segmentation model.

RESULTS

We validated our framework on the OAR segmentation datasets of the head and neck and lung CT images in the StructSeg 2019 challenge. Experiments show that the CLAF-CNN outperforms the state-of-the-art attention-based segmentation methods in the OAR segmentation task with average Dice coefficient of 79.65% for head and neck OARs and 88.39% for lung OARs.

CONCLUSIONS

This work provides a new network named CLAF-CNN which contains cross-layer spatial attention map fusion architecture and TELD-Loss for OAR segmentation. Results demonstrated that the proposed method could obtain accurate segmentation results for OARs, which has a potential of improving the efficiency of radiotherapy planning for nasopharynx cancer and lung cancer.

Proximal seminal vesicle displacement and margins for prostate cancer radiotherapy

INTRODUCTION

Guidelines recommend that the proximal seminal vesicles (PrSV) should be included in the clinical target volume for locally advanced prostate cancer patients undergoing radiotherapy. Verification and margins for the prostate may not necessarily account for PrSV displacement. The purpose was to determine the inter-fraction displacement of the PrSV relative to the prostate during radiotherapy.

METHODS

Fiducials were inserted into the prostate, and right and left PrSV (RSV and LSV) in 30 prostate cancer patients. Correctional shifts for the prostate, right and left PrSV and pelvic bones were determined from each patient's 39 daily orthogonal portal images relative to reference digitally reconstructed radiographs.

RESULTS

There was a significant displacement of the RSV relative to the prostate in all directions: on average 0.38 mm (95% confidence interval (CI) 0.26 to 0.50) to the left, 0.80-0.81 mm (CI 0.68 to 0.93) superiorly and 1.51 mm (CI 1.36 to 1.65) posteriorly. The LSV was significantly displaced superiorly to the prostate 1.09-1.13 mm (CI 0.97 to 1.25) and posteriorly 1.81 mm (CI 1.67 to 1.96), but not laterally (mean 0.06, CI -0.06 to 0.18). The calculated PTV margins (left-right, superior-inferior, posterior-anterior) were 4.9, 5.3-5.6 and 4.8 mm for the prostate, 5.2, 7.1-8.0 and 9.7 mm for the RSV, and 7.2, 7.5-7.6 and 8.6 mm for the LSV.

CONCLUSION

There is a significant displacement of the PrSV relative to the prostate during radiotherapy. Greater margins are recommended for the PrSV compared to the prostate.

CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN)

BACKGROUND

Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T1-weighted MRI from a pre-existing repository of clinical planning CTs.

METHODS

MRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T1 weighted MRI scans.

RESULTS

In our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases.

CONCLUSION

Pelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models.

FDG-PET/CT in the Radiotherapy Treatment Planning of Locally Advanced Anal Cancer: A Monoinstitutional Experience

Aims

Radiotherapy with concurrent 5-fluorouracil/mitomycin-C based chemotherapy has been established as definitive standard therapy approach for anal cancer. Intensity Modulated Radiotherapy (IMRT) leads to a precise treatment of the tumor, allowing dose escalation on Gross Tumor Volume (GTV), with a surrounding healthy tissues sparing. Our study assessed the impact of 18-Fluorodeoxyglucose positron emission tomography (18FDG-PET/CT) on the radiotherapy contouring process and its contribution to lymphatic spread detection, resulting to a personalization of Clinical Target Volume (CTV) and dose prescription.

Methods

Thirty-seven patients, with histologically proven squamous cell carcinoma of the anal canal (SCCAC) were analyzed. All patients were evaluated with history and physical examination, trans-anal endoscopic ultrasound, pelvis magnetic resonance imaging (MRI), computed tomography (CT) scans of the chest, abdomen and pelvis and planning 18FDG-PET/CT. The GTV and CTV were drawn on CT, MRI and 18FDG-PET/CT fused images.

Results

Thirty-four (91%) out of 37 patients presented lymph nodes involvement, in one or more areas, detected on 18FDG-PET/CT and/or MRI. The 18FDG-PET/CT showed positive lymph nodes not detected on MRI imaging (PET+, MRI−) in 14/37 patients (38%). In 14 cases, 18FDG-PET/CT allowed to a dose escalation in the involved nodes. The 18FDG-PET/CT fused images led to change the stage in 5/37(14%) cases: four cases from N0 to N1 (inguinal lymph nodes) and in one case from M0 to M1 (common iliac lymph nodes).

Conclusions

The 18FDG-PET/CT has a potentially relevant impact in staging and target volume delineation/definition in patients affected by anal cancer. In our experience, clinical stage variation occurred in 14% of cases. More investigations are needed to define the role of 18FDG-PET/CT in the target volume delineation of anal cancer.

Investigation of Chromosome 1 Aberrations in the Lymphocytes of Prostate Cancer and Benign Prostatic Hyperplasia Patients by Fluorescence in situ Hybridization

Purpose

Radiotherapy is one of the most common treatments for prostate cancer. Finding a useful predictor of the therapeutic outcome is crucial as it increases the efficacy of treatment planning. This study investigated the individual susceptibility to radiation based on chromosome 1 aberration frequency measured by the FISH (fluorescence in situ hybridization) method.

Patients and Methods

Whole blood samples were collected from 27 prostate cancer (PCa) patients and 32 subjects with benign prostatic hyperplasia (BPH), who were considered as a control group. Samples were irradiated with 2 Gy of x-rays, cultured, harvested, and used in the FISH procedure.

Results

After irradiation, significantly higher levels of all studied chromosome 1 aberrations (except for deletions) in the group of PCa patients were revealed. Furthermore, in the lymphocytes of cancer patients, nearly five-fold higher frequencies of acentric fragments were observed compared to the BPH group. The highest individual radiosensitivities for all estimated biomarkers were seen in PCa patient cells who reported cancer incidence in the immediate family (CIF+).

Conclusion

The differences in chromosome 1 aberrations between PCa and BPH demonstrate that lymphocytes taken from patients with prostate cancer have higher radiosensitivity which might be related to hereditary or familiar inclinations. Therefore, this technique may find future application in searching biomarkers of the cellular radiotherapy response in prostate cancer patients.

Impact of Breast Size on Dosimetric Indices in Proton Versus X-ray Radiotherapy for Breast Cancer

Deep inspiration breath hold (DIBH) radiotherapy is a technique used to manage early stage left-sided breast cancer. This study compared dosimetric indices of patient-specific X-ray versus proton therapy DIBH plans to explore differences in target coverage, radiation doses to organs at risk, and the impact of breast size. Radiotherapy plans of sixteen breast cancer patients previously treated with DIBH radiotherapy were re-planned with hybrid inverse-planned intensity modulated X-ray radiotherapy (h-IMRT) and intensity modulated proton therapy (IMPT). The total prescribed dose was 40.05 Gy in 15 fractions for all cases. Comparisons between the clinical, h-IMRT, and IMPT evaluated doses to target volumes, organs at risk, and correlations between doses and breast size. Although no differences were observed in target volume coverage between techniques, the h-IMRT and IMPT were able to produce more even dose distributions and IMPT delivered significantly less dose to all organs at risk than both X-ray techniques. A moderate negative correlation was observed between breast size and dose to the target in X-ray techniques, but not IMPT. Both h-IMRT and IMPT produced plans with more homogeneous dose distribution than forward-planned IMRT and IMPT achieved significantly lower doses to organs at risk compared to X-ray techniques.

Ninjaflex vs Superflab: A comparison of dosimetric properties, conformity to the skin surface, Planning Target Volume coverage and positional reproducibility for external beam radiotherapy

Background and purpose

When planning and delivering radiotherapy, ideally bolus should be in direct contact with the skin surface. Varying air gaps between the skin surface and bolus material can result in discrepancies between the intended and delivered dose. This study assessed a three‐dimensional (3D) printed flexible bolus to determine whether it could improve conformity to the skin surface, reduce air gaps, and improve planning target volume coverage, compared to a commercial bolus material, Superflab.

Materials and methods

An anthropomorphic head phantom was CT scanned to generate photon and electron treatment plans using virtual bolus. Two 3D printing companies used the material Ninjaflex to print bolus for the head phantom, which we designated Ninjaflex1 and Ninjaflex2. The phantom was scanned a further 15 more times with the different bolus materials in situ allowing plan comparison of the virtual to physical bolus in terms of planning target volume coverage, dose at the prescription point, skin dose, and air gap volumes.

Results

Superflab produced a larger volume and a greater number of air gaps compared to both Ninjaflex1 and Ninjaflex2, with the largest air gap volume of 12.02 cm³. Our study revealed that Ninjaflex1 produced the least variation from the virtual bolus clinical goal values for all modalities, while Superflab displayed the largest variances in conformity, positional accuracy, and clinical goal values. For PTV coverage Superflab produced significant percentage differences for the VMAT and Electron3 plans when compared to the virtual bolus plans. Superflab also generated a significant difference in prescription point dose for the 3D conformal plan.

Conclusion

Compared to Superflab, both Ninjaflex materials improved conformity and reduced the variance between the virtual and physical bolus clinical goal values. Results illustrate that custom‐made Ninjaflex bolus could be useful clinically and may improve the accuracy of the delivered dose.

Hippocampus segmentation in CT using deep learning: impact of MR versus CT-based training contours

Purpose: Hippocampus contouring for radiotherapy planning is performed on MR image data due to poor anatomical visibility on computed tomography (CT) data. Deep learning methods for direct CT hippocampus auto-segmentation exist, but use MR-based training contours. We investigate if these can be replaced by CT-based contours without loss in segmentation performance. This would remove the MR not only from inference but also from training.

Approach: The hippocampus was contoured by medical experts on MR and CT data of 45 patients. Convolutional neural networks (CNNs) for hippocampus segmentation on CT were trained on CT-based or propagated MR-based contours. In both cases, their predictions were evaluated against the MR-based contours considered as the ground truth. Performance was measured using several metrics, including Dice score, surface distances, and contour Dice score. Bayesian dropout was used to estimate model uncertainty.

Results: CNNs trained on propagated MR contours (median Dice 0.67) significantly outperform those trained on CT contours (0.59) and also experts contouring manually on CT (0.59). Differences between the latter two are not significant. Training on MR contours results in lower model uncertainty than training on CT contours. All contouring methods (manual or CNN) on CT perform significantly worse than a CNN segmenting the hippocampus directly on MR (median Dice 0.76). Additional data augmentation by rigid transformations improves the quantitative results but the difference remains significant.

Conclusions: CT-based training contours for CT hippocampus segmentation cannot replace propagated MR-based contours without significant loss in performance. However, if MR-based contours are used, the resulting segmentations outperform experts in contouring the hippocampus on CT.

Comparison of Dose Distribution in Regional Lymph Nodes in Whole-Breast Radiotherapy vs. Whole-Breast Plus Regional Lymph Node Irradiation: An In Silico Planning Study in Participating Institutions of the Phase III Randomized Trial (KROG 1701)

Simple Summary

The purpose of the current in silico planning study is to compare radiation doses of whole-breast irradiation (WBI) and whole-breast plus regional lymph node irradiation (WBI+RNI) administered to the regional lymph nodes (RLN) in pN1 breast cancer. Twenty-four participating institutions were asked to create plans of WBI and WBI+RNI for two dummy cases. In all RLN regions including supraclavicular lymph node, axillary lymph node, and internal mammary lymph node, the radiation dose to the RLN was higher in WBI+RNI plan than WBI plan.

Abstract

The purpose of the current in silico planning study is to compare radiation doses of whole-breast irradiation (WBI) and whole-breast plus regional lymph node irradiation (WBI+RNI) administered to the regional lymph nodes (RLN) in pN1 breast cancer. Twenty-four participating institutions were asked to create plans of WBI and WBI+RNI for two dummy cases. To compare target coverage between the participants, an isodose line equal to 90% of the prescribed dose was converted to an isodose contour (contour_{90% iso})_. The relative nodal dose (RND) was obtained using the ratio of RLN dose to the target dose. The Fleiss’s kappa values which represent inter-observer agreement of contour_{90% iso} were over 0.68. For RNI, 6 institutions included axillary lymph node (ALN), supraclavicular lymph node (SCN), and internal mammary lymph node (IMN), while 18 hospitals included only ALN and SCN. The median RND between the WBI and WBI+RNI were as follows: 0.64 vs. 1.05 (ALN level I), 0.27 vs. 1.08 (ALN level II), 0.02 vs. 1.12 (ALN level III), 0.01 vs. 1.12 (SCN), and 0.54 vs. 0.82 (IMN). In all nodal regions, the RND was significantly lower in WBI than in WBI+RNI (p < 0.01). In this study, we could identify the nodal dose difference between WBI and WBI+RNI.

Analysis of Geometric Performance and Dosimetric Impact of Using Automatic Contour Segmentation for Radiotherapy Planning

Purpose: To analyze geometric discrepancy and dosimetric impact in using contours generated by auto-segmentation (AS) against manually segmented (MS) clinical contours. Methods: A 48-subject prostate atlas was created and another 15 patients were used for testing. Contours were generated using a commercial atlas-based segmentation tool and compared to their clinical MS counterparts. The geometric correlation was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dosimetric relevance was evaluated for a subset of patients by assessing the DVH differences derived by optimizing plan dose using the AS and MS contours, respectively, and evaluating with respect to each. A paired t-test was employed for statistical comparison. The discrepancy in plan quality with respect to clinical dosimetric endpoints was evaluated. The analysis was repeated for head/neck (HN) with a 31-subject atlas and 15 test cases. Results: Dice agreement between AS and MS differed significantly across structures: from (L:0.92/R: 0.91) for the femoral heads to seminal vesical of 0.38 in the prostate cohort, and from 0.98 for the brain, to 0.36 for the chiasm of the HN group. Despite the geometric disagreement, the paired t-tests showed the lack of statistical evidence for systematic differences in dosimetric plan quality yielded by the AS and MS approach for the prostate cohort. In HN cases, statistically significant differences in dosimetric endpoints were observed in structures with small volumes or elongated shapes such as cord (p = 0.01) and esophagus (p = 0.04). The largest absolute dose difference of 11 Gy was seen in the mean pharynx dose. Conclusion: Varying AS performance among structures suggests a differential approach of using AS on a subset of structures and focus MS on the rest. The discrepancy between geometric and dosimetric-end-point driven evaluation also indicates the clinical utility of AS contours in optimization and evaluating plan quality despite of suboptimal geometrical accuracy.

Single click automated breast planning with iterative optimization

Purpose

To present the development of an in‐house coded solution for treatment planning of tangential breast radiotherapy that creates single click plans by emulating the iterative optimization process of human dosimetrists.

Method

One hundred clinical breast cancer patients were retrospectively planned with an automated planning (AP) code incorporating the hybrid intensity‐modulated radiotherapy (IMRT) approach. The code automates all planning processes including plan generation, beam generation, gantry and collimator angle determination, open segments and dynamic IMRT fluence and calculations. Thirty‐nine dose volume histogram (DVH) metrics taken from three international recommendations were compared between the automated and clinical plans (CP), along with median interquartile analysis of the DVH distributions. Total planning time and delivery QA were also compared between the plan sets.

Results

Of the 39 planning metrics analyzed 23 showed no significant difference between clinical and automated planning techniques. Of the 16 metrics with statistically significant variations, 2 were improved in the clinical plans in comparison to 14 improved in the AP plans. Automated plans produced a greater number of ideal plans against international guidelines as per EviQ (AP:77%, CP:68%), RTOG 1005 (AP:80%, CP:71%), and London Cancer references (AP:80%, CP:75%). Delivery QA results for both techniques were equivalent. Automated planning techniques resulted in an average reduction in planning time from 23 to 5 minutes.

Conclusion

We have introduced an automated planning code with iterative optimization that produces equivalent quality plans to manual clinical planning. The resultant change in workflow results in a reduction in treatment planning times.

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

Novel application of vinylpolysiloxane hearing aid impression mold as patient-specific bolus for head and neck cancer radiotherapy

Hearing aid impression material composed of vinylpolysiloxane is an ideal bolus material which may be used to aid in delivery of adjuvant radiation to complex surgical defects of the head and neck. It is affordable, easily accessed, and efficient.

High Spatial Resolution Digital Positron Emission Tomography Images With Dedicated Source-to-background Algorithm for Radiotherapy Planning

BACKGROUND/AIM

To evaluate the utility of high spatial resolution digital positron emission tomography images with the source-to-background ratio (SBR) algorithm for gross tumour volume (GTV) delineation.

MATERIALS AND METHODS

The bowl and spheres (10-37 mm) were filled with fluoro-2-deoxy-D-glucose to achieve 4-16 times background radioactivity. The images were reconstructed using three isotropic voxel sizes. The SBR and percentage threshold (TH) to SUV_max were calculated. The plots between SBR and TH were fitted using a regression equation. The contoured volumes (CVs) of the spheres were calculated by applying TH.

RESULTS

TH was 38.6+75.0/SBR for 4 mm voxel size; 39.6+37.0/SBR for 2 mm; and 38.8+35.2/SBR for 1 mm. The mean relative errors between CV and true volume for 4, 2, and 1 mm voxel sizes were 15%, 7%, and 7%, respectively.

CONCLUSION

The present technique is useful for GTV delineation with reduced contouring error.

Generalization vs. Specificity: In Which Cases Should a Clinic Train its Own Segmentation Models?

As artificial intelligence for image segmentation becomes increasingly available, the question whether these solutions generalize between different hospitals and geographies arises. The present study addresses this question by comparing multi-institutional models to site-specific models. Using CT data sets from four clinics for organs-at-risk of the female breast, female pelvis and male pelvis, we differentiate between the effect from population differences and differences in clinical practice. Our study, thus, provides guidelines to hospitals, in which case the training of a custom, hospital-specific deep neural network is to be advised and when a network provided by a third-party can be used. The results show that for the organs of the female pelvis and the heart the segmentation quality is influenced solely on bases of the training set size, while the patient population variability affects the female breast segmentation quality above the effect of the training set size. In the comparison of site-specific contours on the male pelvis, we see that for a sufficiently large data set size, a custom, hospital-specific model outperforms a multi-institutional one on some of the organs. However, for small hospital-specific data sets a multi-institutional model provides the better segmentation quality.

A super-resolution framework for the reconstruction of T2-weighted (T2w) time-resolved (TR) 4DMRI using T1w TR-4DMRI as the guidance

PURPOSE

The purpose of this study was to develop T2-weighted (T2w) time-resolved (TR) four-dimensional magnetic resonance imaging (4DMRI) reconstruction technique with higher soft-tissue contrast for multiple breathing cycle motion assessment by building a super-resolution (SR) framework using the T1w TR-4DMRI reconstruction as guidance.

METHODS

The multi-breath T1w TR-4DMRI was reconstructed by deforming a high-resolution (HR: 2 × 2 × 2 mm³ ) volumetric breath-hold (BH, 20s) three-dimensional magnetic resonance imaging (3DMRI) image to a series of low-resolution (LR: 5 × 5 × 5 mm³ ) 3D cine images at a 2Hz frame rate in free-breathing (FB, 40 s) using an enhanced Demons algorithm, namely [T1^BH →FB] reconstruction. Within the same imaging session, respiratory-correlated (RC) T2w 4DMRI (2 × 2 × 2 mm³ ) was acquired based on an internal navigator to gain HR T2w (T2^HR ) in three states (full exhalation and mid and full inhalation) in ~5 min. Minor binning artifacts in the RC-4DMRI were automatically identified based on voxel intensity correlation (VIC) between consecutive slices as outliers (VIC < VIC^mean -σ) and corrected by deforming the artifact slices to interpolated slices from the adjacent slices iteratively until no outliers were identified. A T2^HR image with minimal deformation (<1 cm at the diaphragm) from the T1^BH image was selected for multi-modal B-Spline deformable image registration (DIR) to establish the T2^HR -T1^BH voxel correspondence. Two approaches to reconstruct T2w TR-4DMRI were investigated: (A) T2^HR →[T1^BH →FB]: to deform T2w HR to T1w BH only as T1w TR-4DMRI was reconstructed, and combine the two displacement vector fields (DVFs) to reconstruct T2w TR-4DMRI, and (B) [T2^HR ←T1^BH ]→FB: to deform T1w BH to T2w HR first and apply the deformed T1w BH to reconstruct T2w TR-4DMRI. The reconstruction times were similar, 8-12 min per volume. To validate the two methods, T2w- and T1w-mapped 4D XCAT digital phantoms were utilized with three synthetic spherical tumors (ϕ = 2.0, 3.0, and 4.0 cm) in the lower or mid lobes as the ground truth to evaluate the tumor location (the center of mass, COM), size (volume ratio, %V), and shape (Dice index). Six lung cancer patients were scanned under an IRB-approved protocol and the T2w TR-4DMRI images reconstructed from the two methods were compared based on the preservation of the three tumor characteristics. The local tumor-contained image quality was also characterized using the VIC and structure similarity (SSIM) indexes.

RESULTS

In the 4D digital phantom, excellent tumor alignment after T2^HR -T1^HR DIR is achieved: ∆COM = 0.8 ± 0.5 mm, %V = 1.06 ± 0.02, and Dice = 0.91 ± 0.03, in both deformation directions using the DIR-target image as the reference. In patients, binning artifacts are corrected with improved image quality: average VIC increases from 0.92 ± 0.03 to 0.95 ± 0.01. Both T2w TR-4DMRI reconstruction methods produce similar tumor alignment errors ∆COM = 2.9 ± 0.6 mm. However, method B ([T2^HR ←T1^BH ]→FB) produces superior results in preserving more T2w tumor features with a higher %V = 0.99 ± 0.03, Dice = 0.81 ± 0.06, VIC = 0.85 ± 0.06, and SSIM = 0.65 ± 0.10 in the T2w TR-4DMRI images.

CONCLUSIONS

This study has demonstrated the feasibility of T2w TR-4DMRI reconstruction with high soft-tissue contrast and adequately-preserved tumor position, size, and shape in multiple breathing cycles. The T2w-centric DIR (method B) produces a superior solution for the SR-based framework of T2w TR-4DMRI reconstruction with highly preserved tumor characteristics and local image features, which are useful for tumor delineation and motion management in radiation therapy.

Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

Impact of hybrid FDG-PET/CT on gross tumor volume definition of cervical esophageal cancer: reducing interobserver variation

Intensity-modulated radiation therapy is being increasingly used to treat cervical esophageal cancer (CEC); however, delineating the gross tumor volume (GTV) accurately is essential for its successful treatment. The use of computed tomography (CT) images to determine the GTV produces a large degree of interobserver variation. In this study, we evaluated whether the use of [18F]-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET)/CT fused images reduced interobserver variation, compared with CT images alone, to determine the GTV in patients with CEC. FDG-PET/CT scans were obtained for 10 patients with CEC, imaged positioned on a flat tabletop with a pillow. Five radiation oncologists independently defined the GTV for the primary tumors using routine clinical data; they contoured the GTV based on CT images (GTVCT), followed by contouring based on FDG-PET/CT fused images (GTVPET/CT). To determine the geometric observer variation, we calculated the conformality index (CI) from the ratio of the intersection of the GTVs to their union. The interobserver CI was compared using Wilcoxon's signed rank test. The mean (±SD) interobserver CIs of GTVCT and GTVPET/CT were 0.39 ± 0.15 and 0.58 ± 0.10, respectively (P = 0.005). Our results suggested that FDG-PET/CT images reduced interobserver variation when determining the GTV in patients with CEC. FDG-PET/CT may increase the consistency of the radiographically determined GTV in patients with CEC.

Adaptive Radiotherapy Enabled by MRI Guidance

Adaptive radiotherapy (ART) strategies systematically monitor variations in target and neighbouring structures to inform treatment-plan modification during radiotherapy. This is necessary because a single plan designed before treatment is insufficient to capture the actual dose delivered to the target and adjacent critical structures during the course of radiotherapy. Magnetic resonance imaging (MRI) provides superior soft-tissue image contrast over current standard X-ray-based technologies without additional radiation exposure. With integrated MRI and radiotherapy platforms permitting motion monitoring during treatment delivery, it is possible that adaption can be informed by real-time anatomical imaging. This allows greater treatment accuracy in terms of dose delivered to target with smaller, individualised treatment margins. The use of functional MRI sequences would permit ART to be informed by imaging biomarkers, so allowing both personalised geometric and biological adaption. In this review, we discuss ART solutions enabled by MRI guidance and its potential gains for our patients across tumour types.

Evaluation of deep learning methods for parotid gland segmentation from CT images

The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is ∼0.83 for all three models. A patch-based approach for class balancing seems promising for false-positive reduction. The 2-D ensemble and 3-D U-Net are applied to the test data of the 2015 MICCAI challenge on head and neck autosegmentation. Both deep learning methods generalize well onto independent data (Dice 0.865 and 0.88) and are superior to a selection of model- and atlas-based methods with respect to the Dice coefficient. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed for training. We evaluate the performance after training with different-sized training sets and observe no significant increase in the Dice coefficient for more than 250 training cases.

[Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy]

In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99∓13.31)% to (32.55∓22.27)% and of the bladder from (44.37∓4.08)% to (28.99∓15.25)%.

Influence of respiratory motion management technique on radiation pneumonitis risk with robotic stereotactic body radiation therapy

PURPOSE/OBJECTIVES

For lung stereotactic body radiation therapy (SBRT), real-time tumor tracking (RTT) allows for less radiation to normal lung compared to the internal target volume (ITV) method of respiratory motion management. To quantify the advantage of RTT, we examined the difference in radiation pneumonitis risk between these two techniques using a normal tissue complication probability (NTCP) model.

MATERIALS/METHOD

20 lung SBRT treatment plans using RTT were replanned with the ITV method using respiratory motion information from a 4D-CT image acquired at the original simulation. Risk of symptomatic radiation pneumonitis was calculated for both plans using a previously derived NTCP model. Features available before treatment planning that identified significant increase in NTCP with ITV versus RTT plans were identified.

RESULTS

Prescription dose to the planning target volume (PTV) ranged from 22 to 60 Gy in 1-5 fractions. The median tumor diameter was 3.5 cm (range 2.1-5.5 cm) with a median volume of 14.5 mL (range 3.6-59.9 mL). The median increase in PTV volume from RTT to ITV plans was 17.1 mL (range 3.5-72.4 mL), and the median increase in PTV/lung volume ratio was 0.46% (range 0.13-1.98%). Mean lung dose and percentage dose-volumes were significantly higher in ITV plans at all levels tested. The median NTCP was 5.1% for RTT plans and 8.9% for ITV plans, with a median difference of 1.9% (range 0.4-25.5%, pairwise P < 0.001). Increases in NTCP between plans were best predicted by increases in PTV volume and PTV/lung volume ratio.

CONCLUSIONS

The use of RTT decreased the risk of radiation pneumonitis in all plans. However, for most patients the risk reduction was minimal. Differences in plan PTV volume and PTV/lung volume ratio may identify patients who would benefit from RTT technique before completing treatment planning.

Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

A Palliative Radiation Oncology Consult Service's Impact on Care of Advanced Cancer Patients

INTRODUCTION

Palliative radiation therapy (PRT) is a commonly utilized intervention for symptom palliation among patients with metastatic cancer, yet it is under-recognized as a distinct area of subspecialty within radiation oncology.

OBJECTIVE

We developed a multidisciplinary service model within radiation oncology called the Palliative Radiation Oncology Consult (PROC) service to improve the quality of cancer care for advanced cancer patients. We assessed the service's impact on patient-related and healthcare utilization outcomes.

DESIGN

Patients were included in this observational cohort study if they received PRT at a single tertiary care hospital between 2009 and 2017. We compared outcomes of patients treated after (post-intervention group) to those treated before (control group) PROC's establishment using unadjusted and propensity score adjusted analyses.

RESULTS

Of the 450 patients in the cohort, 154 receive PRT pre- and 296 after PROC's establishment. In comparison to patients treated pre-PROC, post-PROC patients were more likely to undergo single-fraction radiation (RR: 7.74, 95% CI: 3.84-15.57) and hypofraction (2-5 fraction) radiation (RR: 10.74, 95% CI: 5.82-19.83), require shorter hospital stays (21 vs. 26.5 median days, p = 0.01), and receive more timely specialty-level palliative care (OR: 2.65, 95% CI: 1.56-4.49). Despite shortened treatments, symptom relief was similar (OR: 1.35, 95% CI: 0.80-2.28).

CONCLUSION

The PROC service was associated with more efficient radiation courses, substantially reduced hospital length of stays, and more timely palliative care consultation, without compromising symptom improvements. These results suggest that a multidisciplinary care delivery model can lead to enhanced quality of care for advanced cancer patients.

18-Fluorodeoxy-Glucose Positron Emission Tomography- Computed Tomography (18-FDG-PET/CT) for Gross Tumor Volume (GTV) Delineation in Gastric Cancer Radiotherapy

Purpose:

Evaluation of the 18-fluorodeoxy-glucose positron emission tomography-computed tomography (18-FDG-PET/_CT) for gross tumor volume (GTV) delineation in gastric cancer patients undergoing radiotherapy.

Methods:

In this study, 29 gastric cancer patients (17 unresectable and 7 inoperable) were initially enrolled for radical chemoradiotherapy (45Gy/25 fractions + chemotherapy based on 5 fluorouracil) or radiotherapy alone (45Gy/25 fractions) with planning based on the 18-FDG-PET/CT images. Five patients were excluded due to excess blood glucose levels (1), false-negative positron emission tomography (1) and distant metastases revealed by 18-FDG-PET/CT (3). The analysis involved measurement of metabolic tumor volumes (MTVs) performed on PET/CT workstations. Different threshold levels of the standardized uptake value (SUV) and liver uptake were set to obtain MTVs. Secondly, GTV_PET values were derived manually using the positron emission tomography (PET) dataset blinded to the computed tomography (CT) data. Subsequently, GTV_CT values were delineated using a radiotherapy planning system based on the CT scans blinded to the PET data. The referenced GTV_CT values were correlated with the GTV_PET and were compared with a conformality index (CI).

Results:

The mean CI was 0.52 (range, 0.12-0.85). In 13/24 patients (54%), the GTV_PET was larger than GTV_CT, and in the remainder, GTV_PET was smaller. Moreover, the cranio-caudal diameter of GTV_PET in 16 cases (64%) was larger than that of GTV_CT, smaller in 7 cases (29%), and unchanged in one case. Manual PET delineation (GTVPET) achieved the best correlation with GTV_CT (Pearson correlation = 0.76, p <0.0001). Among the analyzed MTVs, a statistically significant correlation with GTV_CT was revealed for MTV_10%SUVmax (r = 0.63; p = 0.0014), MTV_liv (r = 0.60; p = 0.0021), MTV_SUV2.5 (r = 0.54; p = 0.0063); MTV_20%SUVmax (r = 0.44; p = 0.0344); MTV_30%SUVmax (r = 0.44; p = 0.0373).

Conclusion:

18-FDG-PET/_CT in gastric cancer radiotherapy planning may affect the GTV delineation.

Visualization of 4D multimodal imaging data and its applications in radiotherapy planning

PURPOSE

To explore the benefit of using 4D multimodal visualization and interaction techniques for defined radiotherapy planning tasks over a treatment planning system used in clinical routine (C-TPS) without dedicated 4D visualization.

METHODS

We developed a 4D visualization system (4D-VS) with dedicated rendering and fusion of 4D multimodal imaging data based on a list of requirements developed in collaboration with radiation oncologists. We conducted a user evaluation in which the benefits of our approach were evaluated in comparison to C-TPS for three specific tasks: assessment of internal target volume (ITV) delineation, classification of tumor location in peripheral or central, and assessment of dose distribution. For all three tasks, we presented test cases for which we measured correctness, certainty, consistency followed by an additional survey regarding specific visualization features.

RESULTS

Lower quality of the test ITVs (ground truth quality was available) was more likely to be detected using 4D-VS. ITV ratings were more consistent in 4D-VS and the classification of tumor location had a higher accuracy. Overall evaluation of the survey indicates 4D-VS provides better spatial comprehensibility and simplifies the tasks which were performed during testing.

CONCLUSIONS

The use of 4D-VS has improved the assessment of ITV delineations and classification of tumor location. The visualization features of 4D-VS have been identified as helpful for the assessment of dose distribution during user testing.

MRI-based radiotherapy planning method using rigid image registration technique combined with outer body correction scheme: a feasibility study

An alternative pseudo CT generation method for magnetic resonance image (MRI)-based radiotherapy planning was investigated in the work. A pseudo CT was initially generated using the rigid image registration between the planning MRI and previously acquired diagnostic CT scan. The pseudo CT generated was then refined to have the same morphology with that of the referenced planning image scan by applying the outer body correction scheme. This method was applied to some sample of brain image data and the feasibility of the method was assessed by comparing dosimetry results with those from the current gold standard CT-based calculations. Validation showed that nearly the entire pixel doses calculated from pseudo CT were agreed well with those from actual planning CT within 2% in dosimetric and 1mm in geometric uncertainty ranges. The results demonstrated that the method suggested in the study was sufficiently accurate, and thus could be applicable to MRI-based brain radiotherapy planning.

Highly Conformal Craniospinal Radiotherapy Techniques Can Underdose the Cranial Clinical Target Volume if Leptomeningeal Extension through Skull Base Exit Foramina is not Contoured

AIMS

Craniospinal irradiation (CSI) remains a crucial treatment for patients with medulloblastoma. There is uncertainty about how to manage meningeal surfaces and cerebrospinal fluid (CSF) that follows cranial nerves exiting skull base foramina. The purpose of this study was to assess plan quality and dose coverage of posterior cranial fossa foramina with both photon and proton therapy.

MATERIALS AND METHODS

We analysed the radiotherapy plans of seven patients treated with CSI for medulloblastoma and primitive neuro-ectodermal tumours and three with ependymoma (total n = 10). Four had been treated with a field-based technique and six with TomoTherapy™. The internal acoustic meatus (IAM), jugular foramen (JF) and hypoglossal canal (HC) were contoured and added to the original treatment clinical target volume (Plan_CTV) to create a Test_CTV. This was grown to a test planning target volume (Test_PTV) for comparison with a Plan_PTV. Using Plan_CTV and Plan_PTV, proton plans were generated for all 10 cases. The following dosimetry data were recorded: conformity (dice similarity coefficient) and homogeneity index (D₂ - D₉₈/D₅₀) as well as median and maximum dose (D_2%) to Plan_PTV, V_95% and minimum dose (D_99.9%) to Plan_CTV and Test_CTV and Plan_PTV and Test_PTV, V_95% and minimum dose (D_98%) to foramina PTVs.

RESULTS

Proton and TomoTherapy™ plans were more conformal (0.87, 0.86) and homogeneous (0.07, 0.04) than field-photon plans (0.79, 0.17). However, field-photon plans covered the IAM, JF and HC PTVs better than proton plans (P = 0.002, 0.004, 0.003, respectively). TomoTherapy™ plans covered the IAM and JF better than proton plans (P = 0.000, 0.002, respectively) but the result for the HC was not significant. Adding foramen CTVs/PTVs made no difference for field plans. The mean D_min dropped 3.4% from Plan_PTV to Test_PTV for TomoTherapy™ (not significant) and 14.8% for protons (P = 0.001).

CONCLUSIONS

Highly conformal CSI techniques may underdose meninges and CSF in the dural reflections of posterior fossa cranial nerves unless these structures are specifically included in the CTV.

Tangential intensity modulated radiation therapy (IMRT) to the intact breast

INTRODUCTION

Inverse-planned intensity modulated radiation therapy (IP-IMRT) has potential benefits over other techniques for tangential intact breast radiotherapy. Possible benefits include increased homogeneity, faster planning time, less inter-planner variability and lower doses to organs at risk (OAR). We therefore conducted a pilot study of previously treated intact breast patients to compare the current forward-planned 'field-in-field' technique (FP-IMRT) with an IP-IMRT alternative.

METHODS

The IP-IMRT plans of 20 patients were generated from a template created for the planning system. All patients were prescribed adjuvant whole breast radiotherapy using a hypofractionated regimen of 40.05 Gy in 15 fractions over 3 weeks. Plans were assessed based on visual inspection of coverage as well as statistical analysis and compared to the clinically acceptable FP-IMRT plans. Patients were planned retrospectively in Monaco 3.2^® using a laterality-specific, tangential planning template. Minor adjustments were made as necessary to meet the planning criteria in the protocol. Dose coverage, maximums, homogeneity indices and doses to OAR were recorded.

RESULTS

The IP-IMRT plans provided more consistent coverage (38.18 Gy vs. 36.08 Gy of D95; P = 0.005), a comparable though higher average maximum (D2 = 42.52 Gy vs. 42.08 Gy; P = 0.0001), more homogeneous plans (homogeneity index = 0.908 vs. 0.861; P = 0.01) and somewhat lower V20 heart and lung doses (0.11% vs. 0.89% for heart; 5.4% vs. 7.52% for lung) than FP-IMRT (P > 0.05).

CONCLUSION

Clinically acceptable plans have been generated using the IP-IMRT templates in Monaco. Improvements in consistency and quality were seen when compared to the FP-IMRT plans. The template-based process is an efficient method to inversely plan IMRT for breast patients.

Modern Radiology in the Management of Head and Neck Cancer

The accurate staging of head and neck cancer is vital to direct appropriate management strategies and to deliver the best radiation therapy and surgery. Initial challenges in head and neck cancer imaging include determination of T- and N-stage, stage migration with detection of metastatic disease and identification of primary disease in the patient presenting with nodal metastases. In follow-up, imaging has an important role in assessing patients who may require salvage surgery after radiotherapy and assessing clinical change that may represent either residual/recurrent disease or radiation effects. This overview gathers recent evidence on the optimal use of currently readily available imaging modalities (ultrasound, computed tomography, magnetic resonance imaging and positron emission tomography-computed tomography) in the context of head and neck squamous cell cancers.

Newer positron emission tomography radiopharmaceuticals for radiotherapy planning: an overview

Positron emission tomography-computed tomography (PET-CT) has changed cancer imaging in the last decade, for better. It can be employed for radiation treatment planning of different cancers with improved accuracy and outcomes as compared to conventional imaging methods. (18)F-fluorodeoxyglucose remains the most widely used though relatively non-specific cancer imaging PET tracer. A wide array of newer PET radiopharmaceuticals has been developed for targeted imaging of different cancers. PET-CT with such new PET radiopharmaceuticals has also been used for radiotherapy planning with encouraging results. In the present review we have briefly outlined the role of PET-CT with newer radiopharmaceuticals for radiotherapy planning and briefly reviewed the available literature in this regard.

Identifying radiotherapy target volumes in brain cancer by image analysis

To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.

Four-dimensional MRI using three-dimensional radial sampling with respiratory self-gating to characterize temporal phase-resolved respiratory motion in the abdomen

PURPOSE

To develop a four-dimensional MRI (4D-MRI) technique to characterize the average respiratory tumor motion for abdominal radiotherapy planning.

METHODS

A continuous spoiled gradient echo sequence was implemented with 3D radial trajectory and 1D self-gating for respiratory motion detection. Data were retrospectively sorted into different respiratory phases based on their temporal locations within a respiratory cycle, and each phase was reconstructed by means of a self-calibrating CG-SENSE program. Motion phantom, healthy volunteer and patient studies were performed to validate the respiratory motion detected by the proposed method against that from a 2D real-time protocol.

RESULTS

The proposed method successfully visualized the respiratory motion in phantom and human subjects. The 4D-MRI and real-time 2D-MRI yielded comparable superior-inferior (SI) motion amplitudes (intraclass correlation = 0.935) with up-to one pixel mean absolute differences in SI displacements over 10 phases and high cross-correlation between phase-resolved displacements (phantom: 0.985; human: 0.937-0.985). Comparable anterior-posterior and left-right displacements of the tumor or gold fiducial between 4D and real-time 2D-MRI were also observed in the two patients, and the hysteresis effect was shown in their 3D trajectories.

CONCLUSION

We demonstrated the feasibility of the proposed 4D-MRI technique to characterize abdominal respiratory motion, which may provide valuable information for radiotherapy planning.

The role of PET-CT in radiotherapy planning of solid tumours

Background

PET-CT is becoming more and more important in various aspects of oncology. Until recently it was used mainly as part of diagnostic procedures and for evaluation of treatment results. With development of personalized radiotherapy, volumetric and radiobiological characteristics of individual tumour have become integrated in the multistep radiotherapy (RT) planning process. Standard anatomical imaging used to select and delineate RT target volumes can be enriched by the information on tumour biology gained by PET-CT. In this review we explore the current and possible future role of PET-CT in radiotherapy treatment planning. After general explanation, we assess its role in radiotherapy of those solid tumours for which PET-CT is being used most.

Conclusions

In the nearby future PET-CT will be an integral part of the most radiotherapy treatment planning procedures in an every-day clinical practice. Apart from a clear role in radiation planning of lung cancer, with forthcoming clinical trials, we will get more evidence of the optimal use of PET-CT in radiotherapy planning of other solid tumours.

Comparing the monoisocentric and dual isocentric techniques in chest wall radiotherapy of mastectomy patients

The monoisocentric (MIT) and dual isocentric (DIT) techniques are compared for the mastectomy patients undergoing chest wall radiotherapy, and a new practical method is suggested for determining the dose calculation reference point to be used in the MIT. Data of 18 mastectomy patients having chest wall radiotherapy were used. To find the appropriate dose calculation reference point for the MIT, the target tissue was divided into nine regions with 17 points as the appropriate candidates. After finding the best reference point for the MIT, dose calculations were made for each patient based on the MIT and DIT to determine the dose distributions of the target volume and organs at risk. The lateral component of the dose calculation reference point was found to be located at one-third of the distance between the geometrical center and the lateral border of the chest wall in the lateral direction toward the outer border. The longitudinal component of this point was found to be located at the geometrical center of the chest wall with a depth located around 2-3 cm under the patients' skin. There was no significant difference between the two radiotherapy planning techniques (MIT and DIT) regarding the dose distributions in the organs at risk and the 95% of the prescribed dose coverage of the target tissue. However, a significant difference for the 105% of the prescribed dose coverage, maximum dose delivered to the target tissue, and the level 2 lymph nodes dose was found, with the DIT showing higher values. Because of the good matching and no superposition observed between the treatment fields in the MIT, it was expected and confirmed that the hot and cold regions (with higher and lower doses than the prescribed dose) with the MIT are significantly fewer than that of the DIT. Therefore, to perform a better conformal radiotherapy for the patients having mastectomy, it could be recommended to use the MIT instead of the DIT and other conventional techniques.

Defining bowel dose volume constraints for bladder radiotherapy treatment planning

AIMS

Increases to radiotherapy dose are constrained by normal tissue effects. The relationship between bowel dose volume data and late bowel toxicity in patients with muscle-invasive bladder cancer treated with radical radiotherapy was assessed.

MATERIALS AND METHODS

The bowel was contoured retrospectively on radiotherapy plans of 47 patients recruited to the BC2001 trial (CRUK/01/004). The relationship between bowel volume at various dose levels and prospectively collected late bowel toxicity was explored.

RESULTS

Fifteen per cent and 6% of patients experienced grade 1 and grade 2 or more late bowel toxicity, respectively. The mean bowel volume was significantly less at doses ≥50 Gy in those treated with reduced high dose volume radiotherapy compared with standard radiotherapy. The probability of late bowel toxicity increased as bowel volume increased (P ≤ 0.05 for dose levels 30-50 Gy). No grade 2 or more late bowel toxicity was observed in patients with bowel volumes under the thresholds given in the model that predict for 25% probability of late bowel toxicity.

CONCLUSIONS

There is a dose volume effect for late bowel toxicity in radical bladder radiotherapy. We have modelled the probability of late bowel toxicity from absolute bowel volumes to guide clinicians in assessing radical bladder radiotherapy plans. Thresholds predicting for a 25% probability of late bowel toxicity are proposed as dose volume constraints.

Defining the target in cancer of the oesophagus: direct radiotherapy planning with fluorodeoxyglucose positron emission tomography-computed tomography

AIMS

Target definition in radiotherapy treatment planning (RTP) of oesophageal cancer is challenging and guided by a combination of diagnostic modalities. This planning study aimed to evaluate the contribution of single positron emission tomography-computed tomography (PET-CT) in the treatment position to RTP.

MATERIALS AND METHODS

Nineteen patients referred for radiotherapy from April to December 2008 were retrospectively identified. Two sets of target volumes were delineated using the planning CT and the (18)F-fluoro-deoxy-D-glucose ((18)F-FDG) PET-CT data sets, respectively. Target volumes were compared in length, volume and geographic conformality. Radiotherapy plans were generated and compared for both data sets.

RESULTS

PET-CT planning target volume (PET-CT(PTV)) was larger than the CT target (CT(PTV)) in 12 cases and smaller in seven. The median PTV conformality index was 0.82 (range 0.44-0.98). Radiotherapy plans conforming to normal tissue dose constraints were achieved for both sets of PTV in 16 patients (three patients could not be treated to the prescription dose with either technique due to very large target volumes and significant risk of normal tissue toxicity). Previously undetected locoregional nodal involvement seen on PET-CT in three cases was localised and included in the PTV. In nine cases, the CTPTV plan delivered less than 95% dose to 95% of the PET-CT(PTV), raising concern about potential for geographical miss.

CONCLUSION

A single scan with diagnostic PET-CT in the treatment position for RTP allows greater confidence in anatomical localisation and interpretation of biological information. The use of PET-CT may result in larger PTV volumes in selected cases, but did not exclude patients from radical treatment within accepted normal tissue tolerance.

Evaluation of atlas-based auto-segmentation software in prostate cancer patients

Introduction

The performance and limitations of an atlas-based auto-segmentation software package (ABAS; Elekta Inc.) was evaluated using male pelvic anatomy as the area of interest.

Methods

Contours from 10 prostate patients were selected to create atlases in ABAS. The contoured regions of interest were created manually to align with published guidelines and included the prostate, bladder, rectum, femoral heads and external patient contour. Twenty-four clinically treated prostate patients were auto-contoured using a randomised selection of two, four, six, eight or ten atlases. The concordance between the manually drawn and computer-generated contours were evaluated statistically using Pearson's product–moment correlation coefficient (r) and clinically in a validated qualitative evaluation. In the latter evaluation, six radiation therapists classified the degree of agreement for each structure using seven clinically appropriate categories.

Results

The ABAS software generated clinically acceptable contours for the bladder, rectum, femoral heads and external patient contour. For these structures, ABAS-generated volumes were highly correlated with ‘as treated’ volumes, manually drawn; for four atlases, for example, bladder r = 0.988 (P < 0.001), rectum r = 0.739 (P < 0.001) and left femoral head r = 0.560 (P < 0.001). Poorest results were seen for the prostate (r = 0.401, P < 0.05) (four atlases); however this was attributed to the comparison prostate volume being contoured on magnetic resonance imaging (MRI) rather than computed tomography (CT) data. For all structures, increasing the number of atlases did not consistently improve accuracy.

Conclusions

ABAS-generated contours are clinically useful for a range of structures in the male pelvis. Clinically appropriate volumes were created, but editing of some contours was inevitably required. The ideal number of atlases to improve generated automatic contours is yet to be determined.