Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR

Impact of diaphragm motion on dosimetry in lower thoracic spine stereotactic body radiotherapy

BACKGROUND AND PURPOSE

Free-breathing computed tomography (FBCT) used in treatment planning for lower thoracic (Th8-Th12) spine stereotactic body radiotherapy (SBRT) can cause deviations between planned and irradiated doses due to diaphragm movement (DM). This study analyzed the dosimetric impact of DM on lower thoracic spine SBRT.

MATERIALS AND METHODS

Data were collected from 19 patients who underwent FBCT and four-dimensional CT (4DCT) during the same session. The 4DCT data were divided into ten respiratory phases (0-90%), and an average CT (AveCT) was reconstructed from them. Using FBCT, target and normal tissues near the diaphragm were contoured and spine SBRT plans with 24-Gy doses in two fractions were created. These plans were applied to each phase of CT and AveCT, with doses recalculated using the same parameters. Actual treatment doses (Deformed AveCT) were estimated by accumulating doses across each 4DCT phase using deformable image registration on the AveCT. Dose-volume histogram (DVH) indices were compared between the FBCT, AveCT, 0% phase, 50% phase, and Deformed AveCT plans.

RESULTS

The mean differences in DVH indices for target and normal tissues were within 2.4 and 2.1%, respectively, when the diaphragm displacement was between -1.6 cm and 2.0 cm, as compared with FBCT. DM displacement showed moderate to strong correlations with DVH differences.

CONCLUSION

Our results indicate that DM has a minor impact on DVH indices if the diaphragm remains within 1.5 cm of the FBCT position.

Evaluation of the availability of single-position treatment with a rotating gantry and the validity of deformable image registration dose assessment for pancreatic cancer carbon-ion radiotherapy

BACKGROUND

This study aimed to evaluate the clinical acceptability of rotational gantry-based single-position carbon-ion radiotherapy (CIRT) to reduce the gastrointestinal (GI) dose in pancreatic cancer. We also evaluated the usefulness of the deformable image registration (DIR)-based dosimetry method for CIRT.

MATERIAL AND METHODS

Fifteen patients with pancreatic cancer were analyzed. The treatment plans were developed for four beam angles in the supine (SP plan) and prone (PR plan) positions. In the case of using multiple positions, the treatment plan was created with two angles for each of the supine and prone position (SP + PR plan). Dose evaluation for multiple positions was performed in two ways: by directly adding the values of the DVH parameters for each position treatment plan (DVH sum), and by calculating the DVH parameters from the accumulative dose distribution created using DIR (DIR sum). The D2cc and D6cc of the stomach and duodenum were recorded for each treatment plan and dosimetry method and compared.

RESULTS

There were no significant differences among any of the treatment planning and dosimetry methods (p > 0.05). The DVH parameters for the stomach and duodenum were higher in the PR plan and SP plan, respectively, and DVH sum tended to be between the SP and PR plans. DVH sum and DIR sum, DVH sum tended to be higher for D2cc and DIR sum tended to be higher for D6cc.

CONCLUSION

There were no significant differences in the GI dose, which suggests that treatment with a simple workflow performed in one position should be clinically acceptable. In CIRT, DIR-based dosimetry should be carefully considered because of the potential for increased uncertainty due to the steep dose distributions.

Ultra-hypofractionated prostate cancer radiotherapy: Dosimetric impact of real-time intrafraction prostate motion and daily anatomical changes

PURPOSE

To retrospectively assess the differences between planned and delivered dose during ultra-hypofractionated (UHF) prostate cancer treatments, by evaluating the dosimetric impact of daily anatomical variations alone, and in combination with prostate intrafraction motion.

METHODS

Prostate intrafraction motion was recorded with a transperineal ultrasound probe in 15 patients treated by UHF radiotherapy (36.25 Gy/5 fractions). The dosimetric objective was to cover 99 % of the clinical target volume with the 100 % prescription isodose line. After treatment, planning CT (pCT) images were deformably registered onto daily Cone Beam CT to generate pseudo-CT for dose accumulation (accumulated CT, aCT). The interplay effect was accounted by synchronizing prostatic shifts and beam geometry. Finally, the shifted dose maps were accumulated (moved-accumulated CT, maCT).

RESULTS

No significant change in daily CTV volumes was observed. Conversely, CTV V100% was 98.2 ± 0.8 % and 94.7 ± 2.6 % on aCT and maCT, respectively, compared with 99.5 ± 0.2 % on pCT (p < 0.0001). Bladder volume was smaller than planned in 76 % of fractions and D5cc was 33.8 ± 3.2 Gy and 34.4 ± 3.4 Gy on aCT (p = 0.02) and maCT (p = 0.01) compared with the pCT (36.0 ± 1.1 Gy). The rectum was smaller than planned in 50.3 % of fractions, but the dosimetric differences were not statistically significant, except for D1cc, found smaller on the maCT (33.2 ± 3.2 Gy, p = 0.02) compared with the pCT (35.3 ± 0.7 Gy).

CONCLUSIONS

Anatomical variations and prostate movements had more important dosimetric impact than anatomical variations alone, although, in some cases, the two phenomena compensated. Therefore, an efficient IGRT protocol is required for treatment implementation to reduce setup errors and control intrafraction motion.

Revolutionizing radiation therapy: the role of AI in clinical practice

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Fractionation versus Adaptation for Compensation of Target Volume Changes during Online Adaptive Radiotherapy for Bladder Cancer: Answers from a Prospective Registry

Online adaptive radiotherapy (ART) allows adaptation of the dose distribution to the anatomy captured by with pre-adaptation imaging. ART is time-consuming, and thus intra-fractional deformations can occur. This prospective registry study analyzed the effects of intra-fraction deformations of clinical target volume (CTV) on the equivalent uniform dose (EUDCTV) of focal bladder cancer radiotherapy. Using margins of 5-10 mm around CTV on pre-adaptation imaging, intra-fraction CTV-deformations found in a second imaging study reduced the 10th percentile of EUDCTV values per fraction from 101.1% to 63.2% of the prescribed dose. Dose accumulation across fractions of a series was determined with deformable-image registration and worst-case dose accumulation that maximizes the correlation of cold spots. A strong fractionation effect was demonstrated-the EUDCTV was above 95% and 92.5% as determined by the two abovementioned accumulation methods, respectively, for all series of dose fractions. A comparison of both methods showed that the fractionation effect caused the EUDCTV of a series to be insensitive to EUDCTV-declines per dose fraction, and this could be explained by the small size and spatial variations of cold spots. Therefore, ART for each dose fraction is unnecessary, and selective ART for fractions with large inter-fractional deformations alone is sufficient for maintaining a high EUDCTV for a radiotherapy series.

The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review

OBJECTIVES

To provide an overview and meta-analysis of different techniques adopted to accomplish kVCBCT for dose calculation and automated segmentation.

METHODS

A systematic review and meta-analysis were performed on eligible studies demonstrating kVCBCT-based dose calculation and automated contouring of different tumor features. Meta-analysis of the performance was accomplished on the reported γ analysis and dice similarity coefficient (DSC) score of both collected results as three subgroups (head and neck, chest, and abdomen).

RESULTS

After the literature scrutinization (n = 1008), 52 papers were recognized for the systematic review. Nine studies of dosimtric studies and eleven studies of geometric analysis were suitable for inclusion in meta-analysis. Using kVCBCT for treatment replanning depends on a method used. Deformable Image Registration (DIR) methods yielded small dosimetric error (≤2%), γ pass rate (≥90%) and DSC (≥0.8). Hounsfield Unit (HU) override and calibration curve-based methods also achieved satisfactory yielded small dosimetric error (≤2%) and γ pass rate ((≥90%), but they are prone to error due to their sensitivity to a vendor-specific variation in kVCBCT image quality.

CONCLUSIONS

Large cohorts of patients ought to be undertaken to validate methods achieving low levels of dosimetric and geometric errors. Quality guidelines should be established when reporting on kVCBCT, which include agreed metrics for reporting on the quality of corrected kVCBCT and defines protocols of new site-specific standardized imaging used when obtaining kVCBCT images for adaptive radiotherapy.

ADVANCES IN KNOWLEDGE

This review gives useful knowledge about methods making kVCBCT feasible for kVCBCT-based adaptive radiotherapy, simplifying patient pathway and reducing concomitant imaging dose to the patient.

The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed

BACKGROUND AND PURPOSE

For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non-correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial-, region-, and intensity-based) were jointly used in DIR algorithm for improved accuracy.

MATERIALS AND METHODS

Three types of metrics were combined to form a single-objective function in DIR algorithm. Fiducial-based metric was used to minimize the distance between the corresponding point sets of two images. Region-based metric was used to improve the overlap between the corresponding areas of two images. Intensity-based metric was used to maximize the correlation between the corresponding voxel intensities of two images. The two CT images, one before surgery and the other after surgery, were acquired from the same patient in the same radiotherapy treatment position. Twenty patients who underwent breast-conserving surgery and postoperative radiotherapy were enrolled in this study.

RESULTS

For target registration error, the difference between the proposed and the conventional registration methods was statistically significant for soft tissue (2.06 vs. 7.82, p = 0.00024 < 0.05) and body boundary (3.70 vs. 6.93, p = 0.021 < 0.05). For visual assessment, the proposed method achieved better matching result for soft tissue and body boundary.

CONCLUSIONS

Comparing to the conventional method, the registration accuracy of the proposed method was significantly improved. This method provided a feasible way for target volume delineation of tumor bed in postoperative radiotherapy of breast cancer patients.

Evaluation of dose accumulation methods and workflows utilising cone beam computed tomography images

INTRODUCTION

Various adaptive radiation therapy (ART) methods have emerged, with little consensus amongst the literature as to which is most appropriate. This study aimed to compare dose mapping (DM) versus Monte Carlo recalculation (MCR), using cone beam computed tomography (CBCT) images when utilised in automated ART dose accumulation workflows in the MIM Maestro software package.

METHODS

The treatment plans for 38 cancer patients (19 prostate and 19 head and neck cases) were used to perform DM or MCR retrospectively upon CBCTs acquired during treatment, which were then deformably registered to the planning CT (DR-pCT) to facilitate dose accumulation. Dose-volume and region-of-interest data were extracted for the planning target volumes and organs at risk. Intraclass correlation (ICC) values and Bland-Altman plots were utilised to compare DM versus MCR doses on the CBCT images as well as CBCT versus DR-pCT doses.

RESULTS

When comparing DM and MCR on CBCTs, the differences across dose level mean dose differences were mostly within a ±5% level of agreement based on the Bland-Altman plots, with over 67% of ICC values over 0.9 and indicative of good correlation. When these distributions were deformed back to the planning CT, the agreement was reduced considerably, with larger differences (exceeding ±5%) resulting from workflow-related issues.

CONCLUSION

The results emphasise the need to consider and make adaptations to minimise the effect of workflows on algorithm performance. Manual user intervention, refined departmental protocols and further developments to the MIM Maestro software will enhance the use of this tool.

Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT-on-rails IGRT and patient-reported outcome scores

PURPOSE

To estimate the clinical impact of differences between delivered and planned dose using dose metrics and normal tissue complication probability (NTCP) modeling.

METHODS

Forty-six consecutive patients with prostate adenocarcinoma between 2010 and 2015 treated with intensity-modulated radiation therapy (IMRT) and who had undergone computed tomography on rails imaging were included. Delivered doses to bladder and rectum were estimated using a contour-based deformable image registration method. The bladder and rectum NTCP were calculated using dose-response parameters applied to planned and delivered dose distributions. Seven urinary and gastrointestinal symptoms were prospectively collected using the validated prostate cancer symptom indices patient reported outcome (PRO) at pre-treatment, weekly treatment, and post-treatment follow-up visits. Correlations between planned and delivered doses against PRO were evaluated in this study.

RESULTS

Planned mean doses to bladder and rectum were 44.9 ± 13.6 Gy and 42.8 ± 7.3 Gy, while delivered doses were 46.1 ± 13.4 Gy and 41.3 ± 8.7 Gy, respectively. D_10cc for rectum was 64.1 ± 7.6 Gy for planned and 60.1 ± 9.3 Gy for delivered doses. NTCP values of treatment plan were 22.3% ± 8.4% and 12.6% ± 5.9%, while those for delivered doses were 23.2% ± 8.4% and 9.9% ± 8.3% for bladder and rectum, respectively. Seven of 25 patients with follow-up data showed urinary complications (28%) and three had rectal complications (12%). Correlations of NTCP values of planned and delivered doses with PRO follow-up data were random for bladder and moderate for rectum (0.68 and 0.67, respectively).

CONCLUSION

Sensitivity of bladder to clinical variations of dose accumulation indicates that an automated solution based on a DIR that considers inter-fractional organ deformation could recommend intervention. This is intended to achieve additional rectum sparing in cases that indicate higher than expected dose accumulation early during patient treatment in order to prevent acute severity of bowel symptoms.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Cone beam CT-based dose accumulation and analysis of delivered dose to the dominant intraprostatic lesion in primary radiotherapy of prostate cancer

Background

Evaluation of delivered dose to the dominant intraprostatic lesion (DIL) for moderately hypofractionated radiotherapy of prostate cancer by cone beam computed tomography (CBCT)-based dose accumulation and target coverage analysis.

Methods

Twenty-three patients with localized prostate cancer treated with moderately hypofractionated prostate radiotherapy with simultaneous integrated boost (SIB) between December 2016 and February 2020 were retrospectively analyzed. Included patients were required to have an identifiable DIL on bi-parametric planning magnetic resonance imaging (MRI). After import into the RayStation treatment planning system and application of a step-wise density override, the fractional doses were computed on each CBCT and were consecutively mapped onto the planning CT via a deformation vector field derived from deformable image registration. Fractional doses were accumulated for all CBCTs and interpolated for missing CBCTs, resulting in the delivered dose for PTV_DIL, PTV_Boost, PTV, and the organs at risk. The location of the index lesions was recorded according to the sector map of the Prostate Imaging Reporting and Data System (PIRADS) Version 2.1. Target coverage of the index lesions was evaluated and stratified for location.

Results

In total, 338 CBCTs were available for analysis. Dose accumulation target coverage of PTV_DIL, PTV_Boost, and PTV was excellent and no cases of underdosage in D_Mean, D_95%, D_02%, and D_98% could be detected. Delivered rectum D_Mean did not significantly differ from the planned dose. Bladder mean D_Mean was higher than planned with 19.4 ± 7.4 Gy versus 18.8 ± 7.5 Gy, p < 0.001. The penile bulb showed a decreased delivered mean D_Mean with 29.1 ± 14.0 Gy versus 29.8 ± 14.4 Gy, p < 0.001. Dorsal DILs, defined as DILs in the posterior medial peripheral zone of the prostate, showed a significantly lower delivered dose with a mean D_Mean difference of 2.2 Gy (95% CI 1.3–3.1 Gy, p < 0.001) compared to ventral lesions.

Conclusions

CBCT-based dose accumulation showed an adequate delivered dose to the dominant intraprostatic lesion and organs at risk within planning limits. Cautious evaluation of the target coverage for index lesions adjacent to the rectum is warranted to avoid underdosage.

Evaluation of performance of pelvic CT-MR deformable image registration using two software programs

We assessed the accuracy of deformable image registration (DIR) accuracy between CT and MR images using an open-source software (Elastix, from Utrecht Medical Center) and a commercial software (Velocity AI Ver. 3.2.0 from Varian Medical Systems, Palo Alto, CA, USA) software. Five male patients' pelvic regions were studied using publicly available CT, T1-weighted (T1w) MR, and T2-weighted (T2w) MR images. In the cost function of the Elastix, we used six DIR parameter settings with different regularization weights (Elastix0, Elastix0.01, Elastix0.1, Elastix1, Elastix10, and Elastix100). We used MR Corrected Deformable algorithm for Velocity AI. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) for the prostate, bladder, rectum and left and right femoral heads were used to evaluate DIR accuracy. Except for the bladder, most algorithms produced good DSC and MDA results for all organs. In our study, the mean DSCs for the bladder ranged from 0.75 to 0.88 (CT-T1w) and from 0.72 to 0.76 (CT-T2w). Similarly, the mean MDA ranges were 2.4 to 4.9 mm (CT-T1w), 4.6 to 5.3 mm (CT-T2w). For the Elastix, CT-T1w was outperformed CT-T2w for both DSCs and MDAs at Elastix0, Elastix0.01, and Elastix0.1. In the case of Velocity AI, no significant differences in DSC and MDA of all organs were observed. This implied that the DIR accuracy of CT and MR images might differ depending on the sequence used.

The effects of rotational setup errors in total body irradiation using helical tomotherapy

Purpose

Helical tomotherapy (HT) is a form of intensity‐modulated radiation therapy that is employed in total body irradiation (TBI). Because TBI targets the whole body, accurate setup positioning at the edge of the treatment volume is made difficult by the whole‐body rotational posture. The purpose of this study is to clarify the tolerance for rotational setup error (SE) in the vertical direction. In addition, we perform a retrospective analysis of actually irradiated dose distributions using previous patients’ irradiation data.

Methods

To clarify the effects of rotational SE on the dose distribution, the planned CT images of 10 patients were rotated by 1–5° in the vertical (pitch) direction to create a pseudo‐rotational SE image. Then, the effect of the magnitude of the rotational SE on the dose distribution was simulated. In addition, the irradiated dose to the patients was analyzed by obtaining recalculated dose distributions using megavoltage CT images acquired before treatment.

Results

The simulation results showed that the average value of the lung volume receiving at least 10 Gy did not exceed the allowable value when the SE value was ≤2°. When the rotational SE was ≤3°, it was possible to maintain the clinical target volume dose heterogeneity within ±10% of the prescribed dose, which is acceptable according to the guidelines. A retrospective analysis of previous patients’ irradiation data showed their daily irradiation dose distribution. The dose to the clinical target volume was reduced by up to 3.4% as a result of the residual rotational SE. Although whole‐course retrospective analyses showed a statistically significant increase in high‐dose areas, the increase was only approximately 1.0%.

Conclusions

Dose errors induced by rotational SEs of ≤2° were acceptable in this study.

Atlas of sentinel lymph nodes in early breast cancer using single-photon emission computed tomography: implication for lymphatic contouring

PURPOSE

to determine the localization of sentinel lymph nodes (SLNs) in a large cohort of patients with breast cancer and validate the European Society for Therapeutic Radiology and Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), and Radiotherapy Comparative Effectiveness (RADCOMP) guidelines on regional lymph node clinical target volume (CTV-LN) delineation.

MATERIALS AND METHODS

A total of 254 women with cT1-3N0-1M0 breast cancer underwent single-photon emission computed tomography (SPECT-CT) visualization of SLNs after intra- and peritumoral injection of 99mTc-radiocolloids. All SPECT-CT images were fused with reference simulation computed tomography. A 3D atlas of SLNs was created and used for evaluation of CTV-LN defined by contouring guidelines.

RESULTS

SPECT-CT visualized 532 SLNs that were localized in axillary level I in 67.5%, level II in 15.4%, level III in 7.3%, internal mammary in 8.5%, and supraclavicular in 1.3% cases. The majority of level II-IV and internal mammary SLNs were inside the recommended CTV-LN. Axillary level I SLNs were covered by ESTRO and RTOG contours in 85% and 85% cases, respectively. "Out of contours" SLNs were mostly detected in lateral subgroup of level I LN (18.5%), while 98%-99% of anterior pectoral and central axillary SLNs were covered by CTV-LN. Internal mammary SLNs were visualized in 33 cases and were outside ESTRO and RTOG contours in 3 and 6 observations, respectively.

CONCLUSION

SPECT-CT atlas of SLNs demonstrated that in most cases ESTRO and RTOG guidelines correctly represented CTV-LNs with the exception of lateral subgroup of SLNs.

Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT

In radiation therapy, a CT image is used to manually delineate the organs and plan the treatment. During the treatment, a cone beam CT (CBCT) is often acquired to monitor the anatomical modifications. For this purpose, automatic organ segmentation on CBCT is a crucial step. However, manual segmentations on CBCT are scarce, and models trained with CT data do not generalize well to CBCT images. We investigate adversarial networks and intensity-based data augmentation, two strategies leveraging large databases of annotated CTs to train neural networks for segmentation on CBCT. Adversarial networks consist of a 3D U-Net segmenter and a domain classifier. The proposed framework is aimed at encouraging the learning of filters producing more accurate segmentations on CBCT. Intensity-based data augmentation consists in modifying the training CT images to reduce the gap between CT and CBCT distributions. The proposed adversarial networks reach DSCs of 0.787, 0.447, and 0.660 for the bladder, rectum, and prostate respectively, which is an improvement over the DSCs of 0.749, 0.179, and 0.629 for "source only" training. Our brightness-based data augmentation reaches DSCs of 0.837, 0.701, and 0.734, which outperforms the morphons registration algorithms for the bladder (0.813) and rectum (0.653), while performing similarly on the prostate (0.731). The proposed adversarial training framework can be used for any segmentation application where training and test distributions differ. Our intensity-based data augmentation can be used for CBCT segmentation to help achieve the prescribed dose on target and lower the dose delivered to healthy organs.

Assessment of daily dose accumulation for robustly optimized intensity modulated proton therapy treatment of prostate cancer

PURPOSE

To implement a daily CBCT based dose accumulation technique in order to assess ideal robust optimization (RO) parameters for IMPT treatment of prostate cancer.

METHODS

Ten prostate cancer patients previously treated with VMAT and having daily CBCT were included. First, RO-IMPT plans were created with ± 3 mm and ± 5 mm patient setup and ± 3% proton range uncertainties, respectively. Second, the planning CT (pCT) was deformably registered to the CBCT to create a synthetic CT (sCT). Both daily and weekly sampling strategies were employed to determine optimal dose accumulation frequency. Doses were recalculated on sCTs for both ± 3 mm/±3% and ± 5 mm/±3% uncertainties and were accumulated back to the pCT. Accumulated doses generated from ± 3 mm/±3% and ± 5 mm/±3% RO-IMPT plans were evaluated using the clinical dose volume constraints for CTV, bladder, and rectum.

RESULTS

Daily accumulated dose based on both ± 3mm/±3% and ±5 mm/±3% uncertainties for RO-IMPT plans resulted in satisfactory CTV coverage (RO-IMPT_3mm/3% CTV_V95 = 99.01 ± 0.87% vs. RO-IMPT_5mm/3% CTV_V95 = 99.81 ± 0.2%, P = 0.002). However, the accumulated dose based on ± 3 mm/3% RO-IMPT plans consistently provided greater OAR sparing than ±5 mm/±3% RO-IMPT plans (RO-IMPT_3mm/3% rectum_V65Gy = 2.93 ± 2.39% vs. RO-IMPT_5mm/3% rectum_V65Gy = 4.38 ± 3%, P < 0.01; RO-IMPT_3mm/3% bladder_V65Gy = 5.2 ± 7.12% vs. RO-IMPT_5mm/3% bladder_V65Gy = 7.12 ± 9.59%, P < 0.01). The gamma analysis showed high dosimetric agreement between weekly and daily accumulated dose distributions.

CONCLUSIONS

This study demonstrated that for RO-IMPT optimization, ±3mm/±3% uncertainty is sufficient to create plans that meet desired CTV coverage while achieving superior sparing to OARs when compared with ± 5 mm/±3% uncertainty. Furthermore, weekly dose accumulation can accurately estimate the overall dose delivered to prostate cancer patients.

Trajectory log analysis and cone-beam CT-based daily dose calculation to investigate the dosimetric accuracy of intensity-modulated radiotherapy for gynecologic cancer

This study evaluated unexpected dosimetric errors caused by machine control accuracy, patient setup errors, and patient weight changes/internal organ deformations. Trajectory log files for 13 gynecologic plans with seven‐ or nine‐beam dynamic multileaf collimator (MLC) intensity‐modulated radiation therapy (IMRT), and differences between expected and actual MLC positions and MUs were evaluated. Effects of patient setup errors on dosimetry were estimated by in‐house software. To simulate residual patient setup errors after image‐guided patient repositioning, planned dose distributions were recalculated (blurred dose) after the positions were randomly moved in three dimensions 0–2 mm (translation) and 0°–2° (rotation) 28 times per patient. Differences between planned and blurred doses in the clinical target volume (CTV) D_98% and D_2% were evaluated. Daily delivered doses were calculated from cone‐beam computed tomography by the Hounsfield unit‐to‐density conversion method. Fractional and accumulated dose differences between original plans and actual delivery were evaluated by CTV D_98% and D_2%. The significance of accumulated doses was tested by the paired t test. Trajectory log file analysis showed that MLC positional errors were −0.01 ± 0.02 mm and MU delivery errors were 0.10 ± 0.10 MU. Differences in CTV D_98% and D_2% were <0.5% for simulated patient setup errors. Differences in CTV D_98% and D_2% were 2.4% or less between the fractional planned and delivered doses, but were 1.7% or less for the accumulated dose. Dosimetric errors were primarily caused by patient weight changes and internal organ deformation in gynecologic radiation therapy.

Prostate or bone? Comparing the efficacy of image guidance surrogates for pelvis and prostate radiotherapy using accumulated delivered dose

INTRODUCTION

This study assessed the impact of dosimetry to both the target and normal tissue when either bony anatomy (BA) or prostate (PRO) was used as surrogates for image guidance for pelvis and prostate radiotherapy using a dose accumulation process.

METHODS

Thirty patients who were prescribed 50-54Gy to the pelvic lymph nodes (PLN) and 78Gy to the prostate/seminal vesicles were included. Daily acquired CBCTs were rigidly registered to the CT using BA and PRO to simulate two different treatment positions. The accumulated delivered dose (D_Acc) of PLN, prostate, bladder and rectum for each surrogate were compared with the planned dose. Deviation from the planned dose (ΔD_Acc-Plan) of >5% was considered clinically significant.

RESULTS

Prostate was displaced from bony anatomy by > 5 mm in 96/755 fractions (12.7%). Deviation between the mean D_Acc and the planned dose for PLN and prostate was <2% when either BA or PRO was used. No significant deviation from planned dose was observed for bladder (p > 0.2). In contrary, D_Acc for rectum D₅₀ was significantly greater than the planned dose when BA was used (Mean ΔD_Acc-Plan = 6%). When examining individual patient, deviation from the planned dose for rectum D₅₀ was clinically significant for 18 patients for BA (Range: 5-21%) and only 8 patients for PRO (Range: 5-8%).

CONCLUSIONS

The use of either BA or PRO for image guidance could deliver dose to PLN and prostate with minimal deviation from the plan using existing PTV margins. However, deviation for rectum was greater when BA was used.

Evaluation of a commercial DIR platform for contour propagation in prostate cancer patients treated with IMRT/VMAT

Purpose

To assess the performance and limitations of contour propagation with three commercial deformable image registration (DIR) algorithms using fractional scans of CT‐on‐rails (CTOR) and Cone Beam CT (CBCT) in image guided prostate therapy patients treated with IMRT/VMAT.

Methods

Twenty prostate cancer patients treated with IMRT/VMAT were selected for analysis. A total of 453 fractions across those patients were analyzed. Image data were imported into MIM (MIM Software, Inc., Cleveland, OH) and three DIR algorithms (DIR Profile, normalized intensity‐based (NIB) and shadowed NIB DIR algorithms) were applied to deformably register each fraction with the planning CT. Manually drawn contours of bladder and rectum were utilized for comparison against the DIR propagated contours in each fraction. Four metrics were utilized in the evaluation of contour similarity, the Hausdorff Distance (HD), Mean Distance to Agreement (MDA), Dice Similarity Coefficient (DSC), and Jaccard indices. A subfactor analysis was performed per modality (CTOR vs. CBCT) and time (fraction). Point estimates and 95% confidence intervals were assessed via a Linear Mixed Effect model for the contour similarity metrics.

Results

No statistically significant differences were observed between the DIR Profile and NIB algorithms. However, statistically significant differences were observed between the shadowed NIB and NIB algorithms for some of the DIR evaluation metrics. The Hausdorff Distance calculation showed the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 14.82 mm vs. 8.34 mm for bladder and 15.87 mm vs. 11 mm for rectum, respectively. Similarly, the Mean Distance to Agreement calculation comparing the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 2.43 mm vs. 0.98 mm for bladder and 2.57 mm vs. 1.00 mm for rectum, respectively. The Dice Similarity Coefficients comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.844 against 0.936 for bladder and 0.772 against 0.907 for rectum, respectively. The Jaccard indices comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.749 against 0.884 for bladder and 0.637 against 0.831 for rectum, respectively. The shadowed NIB DIR, which showed the closest agreement with the manual contours performed significantly better than the DIR Profile in all the comparisons. The OAR with the greatest agreement varied substantially across patients and image guided radiation therapy (IGRT) modality. Intra‐patient variability of contour metric evaluation was insignificant across all the DIR algorithms. Statistical significance at α = 0.05 was observed for manual vs. deformably propagated contours for bladder for all the metrics except Hausdorff Distance (P = 0.01 for MDA, P = 0.02 for DSC, P = 0.01 for Jaccard), whereas the corresponding values for rectum were: P = 0.03 for HD, P = 0.01 for MDA, P < 0.01 for DSC, P < 0.01 for Jaccard. The performance of the different metrics varied slightly across the fractions of each patient, which indicates that weekly contour propagation models provide a reasonable approximation of the daily contour propagation models.

Conclusion

The high variance of Hausdorff Distance across all automated methods for bladder indicates widely variable agreement across fractions for all patients. Lower variance across all modalities, methods, and metrics were observed for rectum. The shadowed NIB propagated contours were substantially more similar to the manual contours than the DIR Profile or NIB contours for both the CTOR and CBCT imaging modalities. The relationship of each algorithm to similarity with manual contours is consistent across all observed metrics and organs. Screening of image guidance for substantial differences in bladder and rectal filling compared with the planning CT reference could aid in identifying fractions for which automated DIR would prove insufficient.

Cross-Domain Data Augmentation for Deep-Learning-Based Male Pelvic Organ Segmentation in Cone Beam CT

For prostate cancer patients, large organ deformations occurring between radiotherapy treatment sessions create uncertainty about the doses delivered to the tumor and surrounding healthy organs. Segmenting those regions on cone beam CT (CBCT) scans acquired on treatment day would reduce such uncertainties. In this work, a 3D U-net deep-learning architecture was trained to segment bladder, rectum, and prostate on CBCT scans. Due to the scarcity of contoured CBCT scans, the training set was augmented with CT scans already contoured in the current clinical workflow. Our network was then tested on 63 CBCT scans. The Dice similarity coefficient (DSC) increased significantly with the number of CBCT and CT scans in the training set, reaching 0.874 ± 0.096 , 0.814 ± 0.055 , and 0.758 ± 0.101 for bladder, rectum, and prostate, respectively. This was about 10% better than conventional approaches based on deformable image registration between planning CT and treatment CBCT scans, except for prostate. Interestingly, adding 74 CT scans to the CBCT training set allowed maintaining high DSCs, while halving the number of CBCT scans. Hence, our work showed that although CBCT scans included artifacts, cross-domain augmentation of the training set was effective and could rely on large datasets available for planning CT scans.

Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users

User-guided deformable image registration (DIR) has allowed users to actively participate in the DIR process and is expected to improve DIR accuracy. The purpose of this study was to evaluate the time required for and effect of user-guided DIR on registration accuracy for thoracic images among users. In this study, 4-dimensional computed tomographic images of 10 thoracic cancer patients were used. The dataset for these patients was provided by DIR-Lab (www.dir-lab.com) and included a coordinate list of anatomical landmarks (300 bronchial bifurcations). Four medical physicists from different institutions performed DIR between peak-inhale and peak-exhale images with/without the user-guided DIR tool, Reg Refine, implemented in MIM Maestro (MIM software, Cleveland, OH). DIR accuracy was quantified by using target registration errors (TREs) for 300 anatomical landmarks in each patient. The average TREs with user-guided DIR in the 10 images by the 4 medical physicists were 1.48, 1.80, 3.46, and 3.55 mm, respectively, whereas the TREs without user-guided DIR were 3.28, 3.45, 3.56, and 3.28 mm, respectively. The average times taken by the 4 physicists to use the user-guided DIR were 10.0, 6.7, 7.1, and 8.0 min, respectively. This study demonstrated that user-guided DIR can improve DIR accuracy and requires only a moderate amount of time (<10 min). However, 2 of the 4 users did not show much improvement in DIR accuracy, which indicated the necessity of training prior to use of user-guided DIR.

Variation in accumulated dose of volumetric-modulated arc therapy for pancreatic cancer due to different beam starting phases

PURPOSE

To assess the effects of different beam starting phases on dosimetric variations in the clinical target volume (CTV) and organs at risk (OARs), and to identify the relationship between plan complexity and the dosimetric impact of interplay effects in volumetric-modulated arc therapy (VMAT) plans for pancreatic cancer.

METHODS

Single and double full-arc VMAT plans were generated for 11 patients. A dose of 50.4 Gy in 28 fractions was prescribed to cover 50% of the planning target volume. Patient-specific Digital Imaging and Communications in Medicine-Radiation Therapy plan files were divided into 10 files based on the respiratory phases in four-dimensional computed tomography (4DCT) simulations. The phase-divided VMAT plans were calculated in consideration of the beam starting phase for each arc and were then combined in the mid-ventilation phase of 4DCT (4D plans). The dose-volumetric parameters were compared with the calculated dose distributions without consideration of the interplay effects (3D plans). Additionally, relationships among plan parameters such as modulation complexity scores, monitor units (MUs), and dose-volumetric parameters were evaluated.

RESULTS

Dosimetric differences in the median values associated with different beam starting phases were within ± 1.0% and ± 0.2% for the CTV and ± 0.5% and ± 0.9% for the OARs during single and double full-arc VMAT, respectively. Significant differences caused by variations in the beam starting phases were observed only for the dose-volumetric parameters of the CTV during single full-arc VMAT (P < 0.05), associated with moderate or strong correlations between the MUs and the dosimetric differences between the 4D and 3D plans.

CONCLUSIONS

The beam starting phase affected CTV dosimetric variations of single full-arc VMAT. The use of double full-arc VMAT mitigated this problem. However, variation in the dose delivered to OARs was not dependent on the beam starting phase, even for single full-arc VMAT.

Deformable image registration for dose mapping between external beam radiotherapy and brachytherapy images of cervical cancer

For locally advanced cervical cancer (LACC), anatomy correspondence with and without BT applicator needs to be quantified to merge the delivered doses of external beam radiation therapy (EBRT) and brachytherapy (BT). This study proposed and evaluated different deformable image registration (DIR) methods for this application. Twenty patients who underwent EBRT and BT for LACC were retrospectively analyzed. Each patient had a pre-BT CT at EBRT boost (without applicator) and a CT and MRI at BT (with applicator). The evaluated DIR methods were the diffeomorphic Demons, commercial intensity and hybrid methods, and three different biomechanical models. The biomechanical models considered different boundary conditions (BCs). The impact of the BT devices insertion on the anatomy was quantified. DIR method performances were quantified using geometric criteria between the original and deformed contours. The BT dose was deformed toward the pre-CT BT by each DIR method. The impact of boundary conditions to drive the biomechanical model was evaluated based on the deformation vector field and dose differences. The GEC-ESTRO guideline dose indices were reported. Large organ displacements, deformations, and volume variations were observed between the pre-BT and BT anatomies. Rigid registration and intensity-based DIR resulted in poor geometric accuracy with mean Dice similarity coefficient (DSC) inferior to 0.57, 0.63, 0.42, 0.32, and 0.43 for the rectum, bladder, vagina, cervix and uterus, respectively. Biomechanical models provided a mean DSC of 0.96 for all the organs. By considering the cervix-uterus as one single structure, biomechanical models provided a mean DSC of 0.88 and 0.94 for the cervix and uterus, respectively. The deformed doses were represented for each DIR method. Caution should be used when performing DIR for this application as standard techniques may have unacceptable results. The biomechanical model with the cervix-uterus as one structure provided the most realistic deformations to propagate the BT dose toward the EBRT boost anatomy.

Autosegmentation of the rectum on megavoltage image guidance scans

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Introduction

Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality.

Methods

CBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCT_r). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCT_r images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCT_r using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).

Results

The image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method.

Conclusion

We have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.