Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy.

Background and purpose: In online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors. Materials and methods: 243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage. Results: All patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1-76.6) mm for MRI_only segmentation, 9.9 (range: 2.5-38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4-17.8) mm for PSF_1 and 4.8 (range: 1.7-26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014). Conclusion: Patient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.

Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer.

Purpose: Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy. Methods and Materials: The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution. Results: The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage. Conclusions: Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.

Online Adaptive MRI-Guided Radiotherapy for Primary Tumor and Lymph Node Boosting in Rectal Cancer.

The purpose of this study was to characterize the motion and define the required treatment margins of the pathological mesorectal lymph nodes (GTVln) for two online adaptive MRI-guided strategies for sequential boosting. Secondly, we determine the margins required for the primary gross tumor volume (GTVprim). Twenty-eight patients treated on a 1.5T MR-Linac were included in the study. On T2-weighted images for adaptation (MRIadapt) before and verification after irradiation (MRIpost) of five treatment fractions per patient, the GTVln and GTVprim were delineated. With online adaptive MRI-guided radiotherapy, daily plan adaptation can be performed through the use of two different strategies. In an adapt-to-shape (ATS) workflow the interfraction motion is effectively corrected by redelineation and the only relevant motion is intrafraction motion, while in an adapt-to-position (ATP) workflow the margin (for GTVln) is dominated by interfraction motion. The margin required for GTVprim will be identical to the ATS workflow, assuming each fraction would be perfectly matched on GTVprim. The intrafraction motion was calculated between MRIadapt and MRIpost for the GTVln and GTVprim separately. The interfraction motion of the GTVln was calculated with respect to the position of GTVprim, assuming each fraction would be perfectly matched on GTVprim. PTV margins were calculated for each strategy using the Van Herk recipe. For GTVln we randomly sampled the original dataset 20 times, with each subset containing a single randomly selected lymph node for each patient. The resulting margins for ATS ranged between 3 and 4 mm (LR), 3 and 5 mm (CC) and 5 and 6 mm (AP) based on the 20 randomly sampled datasets for GTVln. For ATP, the margins for GTVln were 10-12 mm in LR and AP and 16-19 mm in CC. The margins for ATS for GTVprim were 1.7 mm (LR), 4.7 mm (CC) and 3.2 mm anterior and 5.6 mm posterior. Daily delineation using ATS of both target volumes results in the smallest margins and is therefore recommended for safe dose escalation to the primary tumor and lymph nodes.

Benchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer.

Background/purpose: In daily plan adaptation the radiotherapy treatment plan is adjusted just prior to delivery. A simple approach is taking the planning objectives of the reference plan and directly applying these in re-optimization. Here we present a tested method to verify whether daily adaptation without tweaking of the objectives can maintain the plan quality throughout treatment. Materials/methods: For fifteen rectal cancer patients, automated treatment planning was used to generate plans mimicking manual reference plans on the planning scans. For 74 fraction scans (4-5 per patient) an automated plan and a daily adapted plan were generated, where the latter re-optimizes the reference plan objectives without any tweaking. To evaluate the robustness of the daily adaptation, the adapted plans were compared to the autoplanning plans. Results: Median differences between the autoplanning plans on the planning scans and the reference plans were between -1 and 0.2 Gy. The largest interquartile range (1 Gy) was seen for the Lumbar Skin D2%. For the daily scans the PTV D2% and D98% differences between autoplanning and adapted plans were within ± 0.7 Gy, with mean differences within ± 0.3 Gy. Positive differences indicate higher values were obtained using autoplanning. For the Bowelarea + Bladder and the Lumbar Skin the D2% and Dmean differences were all within ± 2.6 Gy, with mean differences between -0.9 and 0.1 Gy. Conclusion: Automated treatment planning can be used to benchmark daily adaptation techniques. The investigated adaptation workflow can robustly perform high quality adaptations without daily adjusting of the patient-specific planning objectives for rectal cancer radiotherapy.

Effect of intrafraction adaptation on PTV margins for MRI guided online adaptive radiotherapy for rectal cancer.

PURPOSE: To determine PTV margins for intrafraction motion in MRI-guided online adaptive radiotherapy for rectal cancer and the potential benefit of performing a 2nd adaptation prior to irradiation. METHODS: Thirty patients with rectal cancer received radiotherapy on a 1.5 T MR-Linac. On T2-weighted images for adaptation (MRIadapt), verification prior to (MRIver) and after irradiation (MRIpost) of 5 treatment fractions per patient, the primary tumor GTV (GTVprim) and mesorectum CTV (CTVmeso) were delineated. The structures on MRIadapt were expanded to corresponding PTVs. We determined the required expansion margins such that on average over 5 fractions, 98% of CTVmeso and 95% of GTVprim on MRIpost was covered in 90% of the patients. Furthermore, we studied the benefit of an additional adaptation, just prior to irradiation, by evaluating the coverage between the structures on MRIver and MRIpost. A threshold to assess the need for a secondary adaptation was determined by considering the overlap between MRIadapt and MRIver. RESULTS: PTV margins for intrafraction motion without 2nd adaptation were 6.4 mm in the anterior direction and 4.0 mm in all other directions for CTVmeso and 5.0 mm isotropically for GTVprim. A 2nd adaptation, applied for all fractions where the motion between MRIadapt and MRIver exceeded 1 mm (36% of the fractions) would result in a reduction of the PTVmeso margin to 3.2 mm/2.0 mm. For PTVprim a margin reduction to 3.5 mm is feasible when a 2nd adaptation is performed in fractions where the motion exceeded 4 mm (17% of the fractions). CONCLUSION: We studied the potential benefit of intrafraction motion monitoring and a 2nd adaptation to reduce PTV margins in online adaptive MRIgRT in rectal cancer. Performing 2nd adaptations immediately after online replanning when motion exceeded 1 mm and 4 mm for CTVmeso and GTVprim respectively, could result in a 30-50% margin reduction with limited reduction of dose to the bowel.

Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac.

Purpose. Accurate tumor localization for image-guided liver stereotactic body radiation therapy (SBRT) is challenging due to respiratory motion and poor tumor visibility on conventional x-ray based images. Novel integrated MRI and radiotherapy systems enable direct in-room tumor visualization, potentially increasing treatment accuracy. As these systems currently do not provide a 4D image-guided radiotherapy strategy, we developed a 4D-MRI guided liver SBRT workflow and validated all steps for implementation on the Unity MR-linac.Materials and Methods. The proposed workflow consists of five steps: (1) acquisition of a daily 4D-MRI scan, (2) 4D-MRI to mid-position planning-CT rigid tumor registration, (3) calculation of daily tumor midP misalignment, (4) plan adaptation using adapt-to-position (ATP) with segment-weights optimization and (5) adapted plan delivery. The workflow was first validated in a motion phantom, performing regular motion at different baselines (±5 to ±10 mm) and patient-derived respiratory signals with varying degrees of irregularity. 4D-MRI derived respiratory signals and 4D-MRI to planning CT registrations were compared to the phantom input, and gamma and dose-area-histogram analyses were performed on the delivered dose distributions on film. Additionally, 4D-MRI to CT registration performance was evaluated in patient images using the full-circle method (transitivity analysis). Plan adaption was further analyzedin-silicoby creating adapted treatment plans for 15 patients with oligometastatic liver disease.Results. Phantom trajectories could be reliably extracted from 4D-MRI scans and 4D-MRI to CT registration showed submillimeter accuracy. The DAH-analysis demonstrated excellent coverage of the dose evaluation structures GTV and GTVTD. The median daily rigid 4D-MRI to midP-CT registration precision in patient images was <2 mm. The ATP strategy restored the target dose without increased exposure to the OARs and plan quality was independent from 3D shift distance in the range of 1-26 mm.Conclusions. The proposed 4D-MRI guided strategy showed excellent performance in all workflow tests in preparation of the clinical introduction on the Unity MR-linac.

Substantial Volume Changes and Plan Adaptations During Preoperative Radiation Therapy in Extremity Soft Tissue Sarcoma Patients.

PURPOSE: Many authors suggest that extremity soft tissue sarcomas (ESTS) do not change significantly in size during preoperative radiation therapy (RT). This cone beam computed tomography study investigates the justification to deliver the entire course with 1 initial RT plan by observing anatomic changes during RT. METHODS AND MATERIALS: Between 2015 and 2017, 99 patients with ESTS were treated with either curative (n = 80) or palliative intent (n = 19) with a regimen of at least 6 fractions. The clinical target volume to planning target volume margin was 1 cm. Action levels were assigned by radiation technicians. An extremity contour change of >1 cm and/or tumor size change >0.5 cm required a physician's action before the next fraction. RESULTS: A total of 982 cone beam computed tomography logfiles were studied. In 41 of 99 patients, the dose coverage of the initial treatment plan was fully satisfactory throughout the RT course. However, action levels were observed in 58 patients (59%). In 41 of these 58 patients, a contour increase of 5 to 23 mm was noted (29 tumor size increase only, 3 extremity contour increase, and 9 both). In 21 of 58 patients, a decrease of 5 to 33 mm was observed (20 tumor size decrease only and 1 tumor size decrease and extremity contour decrease). In 4 cases, contours initially increased and subsequently decreased. In 33 of 41 patients with increasing contours, the dose distribution adequately covered gross tumor volume because of the 1 cm planning target volume margin applied. For the remaining 8 patients (8%), the plan needed to be adapted. CONCLUSIONS: ESTS volumes may change substantially during RT in 59% of all patients, leading to plan adaptations resulting from increased volumes in 8%. Daily critical observation of these patients is mandatory to avoid geographic misses because of increases in size and overdosing of normal tissues when masses shrink.

Monitoring early changes in rectal tumor morphology and volume during 5 weeks of preoperative chemoradiotherapy - An evaluation with sequential MRIs.

PURPOSE: To assess early changes in rectal tumor volume and morphology on sequential MRIs performed during 5 weeks of chemoradiotherapy. MATERIALS AND METHODS: Thirteen patients underwent weekly T2W-MRI during 5 weeks of preoperative radiotherapy (total 50 Gy), starting after the first week of radiation. Two radiologists visually evaluated tumor volume and morphology and one reader manually segmented tumors for each time point to quantitatively calculate tumor volumes. Evolution in tumor volume/morphology was assessed over time and compared between good responders (tumor regression grade (TRG) 1-2) and poor responders (TRG 3-5). RESULTS: Tumor volumes decreased significantly during radiation. Early signs of response were also visually apparent: in the majority of good responders an early fibrotic transformation (week 2-3) as well as a visually estimated early volume reduction of >1/3 (week 1-2), was observed while these early changes only occurred in a minority of poor responders. CONCLUSION: Results of this exploratory pilot study suggest that changes in rectal tumor morphology (fibrosis) and volume can already be observed early during radiation, both when measured quantitatively and when assessed visually. These changes appear to be indicative of the final treatment outcome.

Breast-shape changes during radiation therapy after breast-conserving surgery.

BACKGROUND & PURPOSE: With the introduction of more conformal techniques for breast cancer radiation therapy (RT), motion management is becoming increasingly important. We studied the breast-shape variability during RT after breast-conserving surgery (BCS). MATERIALS & METHODS: Planning computed tomography (CT) and follow-up cone-beam CT (CBCT) scans were available for 71 fractions of 17 patients undergoing RT after BCS. First, the CT and the CBCT scans were registered on bones. Subsequently, breast-contour data were generated. The CBCT contours were analyzed in 3D in terms of deviations (mean and standard deviation) relative to the contour of the CT scan for the upper medial, lower medial, upper lateral, and lower lateral breast quadrants, and the axilla. RESULTS: Regional systematic and random standard deviations of the breast quadrants varied between 1.5 and 2.1 mm and 1.0-1.6 mm, respectively, and were larger for the axilla (3.0 mm). An absolute average shape change of  ≥4.0 mm in at least one region was present in 21/71 fractions (30%), predominantly in breast volumes > 800 cc (p = <0.01). Furthermore, seroma was associated with larger shape changes (p = 0.04). CONCLUSIONS: Breast-shape variability varies between anatomic locations. Changes in the order of 4 mm are frequently observed during RT, especially for large breasts. This should be taken into account in the development of protocols for partial breast irradiation and boost treatment.

Pre-clinical experience of an adaptive plan library strategy in radiotherapy of rectal cancer: An inter-observer study.

BACKGROUND AND PURPOSE: The clinical target volume (CTV) in radiotherapy of rectal cancer is subject to large deformations. With a plan library strategy, the treatment may be adapted to these deformations. The purpose of this study was to determine feasibility and consistency in plan selection for a plan library strategy in radiotherapy of rectal cancer. MATERIAL AND METHODS: Thirty rectal cancer patients were included in this retrospective study with in total 150 CBCT scans. A library of CTVs was constructed with in-house built software using population statistics on daily rectal deformations. The library consisted of five plans based on: the original CTV, two larger, and two smaller CTVs. An inter-observer study (study-I) was performed to test the consistency in plan choices between four observers (all RTTs). After five months the observers were asked to re-evaluate (study-II) the same set of scans based on refined guidelines. RESULTS: In study-I the observers reached accordance with the majority choice in 69% of cases. This improved to 87% in study-II. The consensus meeting revealed that inconsistency in choices mainly arose from inadequate instructions, which were later clarified and formulated more accurately. CONCLUSION: Plan selection based on daily CBCT scans for rectal cancer patients is feasible, and can be performed consistently by well-trained RTTs.

Inter- and intra-fractional bladder motion during radiotherapy for bladder cancer: a comparison of full and empty bladders.

PURPOSE: To compare inter- and intra-fraction bladder volume variations and bladder wall motion during radiotherapy (RT) for bladder cancer with full and empty bladder protocols. MATERIALS AND METHODS: Bladder volumes, filling rates and bladder wall movement were retrospectively analyzed for 24 patients with at least 4 sets of delineable pre and post treatment cone beam CT (CBCT)-scans. Eight patients were treated with an 'empty bladder' (EB) protocol and sixteen patients with a 'full bladder' (FB) protocol. RESULTS: 24 planning CT-scans and 356 CBCT-scans (178 sets) were analyzed. The average time between pre and post irradiation CBCT was 8min (range 6-18min). Median filling rate was 1.94ml/min and did not differ between EB and FB. Random variation in bladder volume and inter-fraction wall movement was slightly but not significantly larger for FB, whereas intra-fraction bladder wall movement was slightly but not significantly smaller for FB. The largest inter- and intra-fraction bladder wall movement was found in the cranial anterior direction. CONCLUSION: Empty and full bladder protocols show similar inter- and intra-fraction wall motion, and therefore treatment choices could be purely based on organ at risk criteria.

Estimation of heart-position variability in 3D-surface-image-guided deep-inspiration breath-hold radiation therapy for left-sided breast cancer.

PURPOSE: To investigate the heart position variability in deep-inspiration breath-hold (DIBH) radiation therapy (RT) for breast cancer when 3D surface imaging would be used for monitoring the BH depth during treatment delivery. For this purpose, surface setup data were compared with heart setup data. MATERIALS AND METHODS: Twenty patients treated with DIBH-RT after breast-conserving surgery were included. Retrospectively, heart registrations were performed for cone-beam computed tomography (CBCT) to planning CT. Further, breast-surface registrations were performed for a surface, captured concurrently with CBCT, to planning CT. The resulting setup errors were compared with linear regression analysis. Furthermore, geometric uncertainties of the heart (systematic [Σ] and random [σ]) were estimated relative to the surface registration. Based on these uncertainties planning organ at risk volume (PRV) margins for the heart were calculated: 1.3Σ-0.5σ. RESULTS: Moderate correlation between surface and heart setup errors was found: R(2)=0.64, 0.37, 0.53 in left-right (LR), cranio-caudal (CC), and in anterior-posterior (AP) direction, respectively. When surface imaging would be used for monitoring, the geometric uncertainties of the heart (cm) are [Σ=0.14, σ=0.14]; [Σ=0.66, σ=0.38]; [Σ=0.27, σ=0.19] in LR; CC; AP. This results in PRV margins of 0.11; 0.67; 0.25 cm in LR; CC; AP. CONCLUSION: When DIBH-RT after breast-conserving surgery is guided by the breast-surface position then PRV margins should be used to take into account the heart-position variability relative to the breast-surface.

Fusion of planning CT and cystoscopy images for bladder tumor delineation: a feasibility study.

PURPOSE: Bladder tumor delineation and localization during treatment are challenging problems in radiotherapy for bladder cancer. The purpose of this study is to investigate improvement of tumor delineation by the fusion of cystoscopy images with the planning CT-scan using lipiodol markers injected around the visible tumor during cystoscopy. METHODS: A registration method was developed for the fusion of cystoscopy images with a planning CT-scan and was tested on a phantom and retrospectively on the imaging data of four bladder cancer patients. For the patients, small deposits of lipiodol were injected at the visible margin of the tumor or previous transurethral resection site during cystoscopy. These deposits were clearly visible on the planning CT-scan and served as markers for both tumor delineation and image guidance of the radiotherapy treatment. Here, the markers were used for the registration of cystoscopy images with the planning CT-scan. The registration procedure works as follows: First, coarse registrations were made to orient the cystoscopy image correctly, using the center of gravity of the markers, the center of the CT bladder, and one of N markers as fiducial points in a point matching procedure. Starting from these N orientations, full registrations are performed taking lens deformation into account. Since a cystoscopy image is 2D, each pixel corresponds to a line-of-sight. The distances between the CT markers and the lines-of-sight of the cystoscopy markers were minimized. The final cost function (the root mean square distance between corresponding CT markers and lines-of-sight) was used to quantify the quality of the registration. The registration with the lowest final cost was considered to represent the correct orientation. The CT-based tumor delineation was finally backprojected onto the cystoscopy image. RESULTS: The fusion of cystoscopy images with a planning CT-scan succeeded for the phantom and three out of four patients. The fiducial registration error (FRE) for the phantom image registration based on five markers was 1.1 mm, while the target registration error was 1.2-1.7 mm. The FREs for the patient images were 0.1-3.6 mm. The registration procedure failed for one patient, since it was not possible to indicate unambiguously the corresponding lipiodol marker locations in the cystoscopy image and the planning CT-scan. The difference between the CT and cystoscopy defined tumor outlines clearly exceeded the registration accuracy. CONCLUSIONS: Registration of cystoscopy images and planning CT-scan is feasible and allows for improvement of tumor delineation. However, the lipiodol injection protocol needs to be improved to facilitate identification of markers on both cystoscopy images and planning CT-scans.

Volume changes in soft tissue sarcomas during preoperative radiotherapy of extremities evaluated using cone-beam CT.

OBJECTIVE: The objective of this study is to quantify volume changes in the gross target volume (GTV) during preoperative radiotherapy for extremity soft tissue sarcomas (ESTS). METHODS: Twenty-seven patients with ESTS, treated with preoperative radiotherapy, were included in this study. Weekly cone-beam CT scans acquired for setup correction were used for GTV delineation in order to quantify volume changes over the course of treatment. Age, anatomical location, tumour type and tumour volume were evaluated as predictive factors for volume changes. Finally, the optimal time point for adaptive intervention was quantified. RESULTS: A GTV increase to a maximum of 28 % occurred in five patients. Thirteen patients showed no change and nine patients (all diagnosed with myxoid liposarcoma (MLS)) showed a GTV decrease to a maximum of 57 % of the GTV volume at start of treatment. In the multivariate analysis, only the relative volume change for tumour type was significant (p = 0.001). The optimal time point for adaptive intervention in non-MLS patients was the first week and for MLS patients the third week. CONCLUSIONS: Volume changes were quantified during preoperative RT of ESTS. Volume decrease was observed only in MLS patients. Individualised treatment resulting in plan adaptations could result in a clinically useful volume reduction for MLS patients.

Cone beam CT assisted irradiation of painful vertebral metastases without prior virtual simulation: a quick and patient friendly procedure.

A procedure is described using diagnostic CT and/or MRI scans to simulate treatment fields for painful vertebral metastases. Cone beam CT guidance subsequently corrects patient setup. Our first 100 patients are analyzed and compared to another 100 patients after conventional simulation. This procedure proved to be quick and patient friendly.

Assessment of set-up variability during deep inspiration breath hold radiotherapy for breast cancer patients by 3D-surface imaging.

PURPOSE: To quantify set-up uncertainties during voluntary deep inspiration breath hold (DIBH) radiotherapy using 3D-surface imaging in patients with left sided breast cancer. MATERIAL AND METHODS: Nineteen patients were included. Cone-beam CT-scan (CBCT) was used for online set-up correction while patients were instructed to perform a voluntary DIBH. The reproducibility of the DIBH during treatment was monitored with 2D-fluoroscopy and portal imaging. Simultaneously, a surface imaging system was used to capture 3D-surfaces throughout CBCT acquisition and delivery of treatment beams. Retrospectively, all captured surfaces were registered to the planning-CT surface. Interfraction, intra-fraction and intra-beam set-up variability were quantified in left-right, cranio-caudal and anterior-posterior direction. RESULTS: Inter-fraction systematic (Σ) and random (σ) translational errors (1SD) before and after set-up correction were between 0.20-0.50 cm and 0.09-0.22 cm, respectively, whereas rotational Σ and σ errors were between 0.08 and 1.56°. The intra-fraction Σ and σ errors were ≤ 0.14 cm and ≤ 0.47°. The intra-beam SD variability was ≤ 0.08 cm and ≤ 0.28° in all directions. CONCLUSION: Quantification of 3D set-up variability in DIBH RT showed that patients are able to perform a very stable and reproducible DIBH within a treatment fraction. However, relatively large inter-fraction variability requires online image guided set-up corrections.

Accuracy evaluation of a 3-dimensional surface imaging system for guidance in deep-inspiration breath-hold radiation therapy.

PURPOSE: To investigate the applicability of 3-dimensional (3D) surface imaging for image guidance in deep-inspiration breath-hold radiation therapy (DIBH-RT) for patients with left-sided breast cancer. For this purpose, setup data based on captured 3D surfaces was compared with setup data based on cone beam computed tomography (CBCT). METHODS AND MATERIALS: Twenty patients treated with DIBH-RT after breast-conserving surgery (BCS) were included. Before the start of treatment, each patient underwent a breath-hold CT scan for planning purposes. During treatment, dose delivery was preceded by setup verification using CBCT of the left breast. 3D surfaces were captured by a surface imaging system concurrently with the CBCT scan. Retrospectively, surface registrations were performed for CBCT to CT and for a captured 3D surface to CT. The resulting setup errors were compared with linear regression analysis. For the differences between setup errors, group mean, systematic error, random error, and 95% limits of agreement were calculated. Furthermore, receiver operating characteristic (ROC) analysis was performed. RESULTS: Good correlation between setup errors was found: R(2)=0.70, 0.90, 0.82 in left-right, craniocaudal, and anterior-posterior directions, respectively. Systematic errors were ≤0.17 cm in all directions. Random errors were ≤0.15 cm. The limits of agreement were -0.34-0.48, -0.42-0.39, and -0.52-0.23 cm in left-right, craniocaudal, and anterior-posterior directions, respectively. ROC analysis showed that a threshold between 0.4 and 0.8 cm corresponds to promising true positive rates (0.78-0.95) and false positive rates (0.12-0.28). CONCLUSIONS: The results support the application of 3D surface imaging for image guidance in DIBH-RT after BCS.

Semiautomatic bladder segmentation on CBCT using a population-based model for multiple-plan ART of bladder cancer.

The aim of this study is to develop a novel semiautomatic bladder segmentation approach for selecting the appropriate plan from the library of plans for a multiple-plan adaptive radiotherapy (ART) procedure. A population-based statistical bladder model was first built from a training data set (95 bladder contours from 8 patients). This model was then used as constraint to segment the bladder in an independent validation data set (233 CBCT scans from the remaining 22 patients). All 3D bladder contours were converted into parametric surface representations using spherical harmonic expansion. Principal component analysis (PCA) was applied in the spherical harmonic-based shape parameter space to calculate the major variation of bladder shapes. The number of dominating PCA modes was chosen such that 95% of the total shape variation of the training data set was described. The automatic segmentation started from the bladder contour of the planning CT of each patient, which was modified by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to best fit the bladder boundary in the localization CBCT image. A cost function was defined to measure the goodness of fit of the segmentation on the localization CBCT image. The segmentation was obtained by minimizing this cost function using a simplex optimizer. After automatic segmentation, a fast manual correction method was provided to correct those bladders (parts) that were poorly segmented. Volume- and distance-based metrics and the accuracy of plan selection from multiple plans were evaluated to quantify the performance of the automatic and semiautomatic segmentation methods. For the training data set, only seven PCA modes were needed to represent 95% of the bladder shape variation. The mean CI overlap and residual error (SD) of automatic bladder segmentation over all of the validation data were 70.5% and 0.39 cm, respectively. The agreement of plan selection between automatic bladder segmentation and manual delineation was 56.7%. The automatic segmentation and visual assessment took on average 7.8 and 9.7 s, respectively. In 53.4% of the cases, manual correction was performed after automatic segmentation. The manual correction improved the mean CI overlap, mean residual error and plan selection agreement to 77.7%, 0.30 cm and 80.7%, respectively. Manual correction required on average 8.4 markers and took on average 35.5 s. The statistical shape-based segmentation approach allows automatic segmentation of the bladder on CBCT with moderate accuracy. Limited user intervention can quickly and reliably improve the bladder contours. This segmentation method is suitable to select the appropriate plan for multiple-plan ART of bladder cancer.

3D surface imaging for monitoring intrafraction motion in frameless stereotactic body radiotherapy of lung cancer.

PURPOSE: To investigate the accuracy of surface imaging for monitoring intrafraction motion purposes in frameless stereotactic body radiotherapy (SBRT) of lung cancer by comparison with cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Thirty-six patients (18 males, 18 females) were included. During each fraction, three CBCT scans were acquired; CBCT1: before treatment, CBCT2: after correction for tumor misalignment, and CBCT3: after treatment. Intrafraction motion was derived by registering CBCT2 and CBCT3 to the mid-ventilation planning CT scan. Surfaces were captured concurrently with CBCT acquisitions. Retrospectively, for each set of surfaces, an average surface was created: Surface1, Surface2, and Surface3. Subsequently, Surface3 was registered to Surface2 to assess intrafraction motion. For the differences between CBCT- and surface-imaging-derived 3D intrafraction motions, group mean, systematic error, random error and limits of agreement (LOA) were calculated. RESULTS: Group mean, systematic and random errors were smaller for females than for males: 0.4 vs. 1.3, 1.3 vs. 3.1, and 1.7 vs. 3.3 mm respectively. For female patients deviations between CBCT-tumor- and 3D-surface-imaging-derived intrafraction motions were between -3.3 and 4.3 mm (95% LOA). For male patients these were substantially larger: -5.9-9.5mm. CONCLUSION: Surface imaging is a promising technology for monitoring intrafraction motion purposes in SBRT for female patients.

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model.

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation CBCT sampled on the segmentation contour candidate. The cost function measured the goodness of fit of the segmentation on the validation image and was minimized using a simplex optimizer. For each validation CBCT image, the segmentations were done five times using a different reference CBCT. The one with the lowest cost function was selected as the final bladder segmentation. Volume- and distance-based metrics and the accuracy of plan selection were evaluated to quantify the performance. Two to four PCA modes were needed to represent the bladder shape variation with less than 0.1 cm average residual error for the training data of each patient. The automatically segmented bladders had a 78.5% mean conformity index with the manual delineations. The mean SD of the local residual error over all patients was 0.24 cm. The agreement of plan selection between automatic and manual bladder segmentations was 77.5%. PCA is an efficient method to describe patient-specific bladder deformation. The statistical-shape-based segmentation approach is robust to handle the relatively poor CBCT image quality and allows for fast and reliable automatic segmentation of the bladder on CBCT for selecting the appropriate plan from a library of plans.