Dose-volume Histogram Metrics Research Articles

Clinical impact of DVH uncertainties

Dose-volume histograms (DVH), along with dose and volume metrics, are central to radiotherapy planning. As such, errors have the potential to significantly impact the selection of appropriate treatment plans. Dose distributions that pass tests in one TPS may fail the same tests when transferred to another, even if using identical structures and dose grid information. This work shows the design and implementation of methods for assessing the accuracy of dose and volume computations performed by treatment planning systems (TPS), and other analytical tools. We demonstrate examples where differences in calculations between systems can change the assessment of a plan’s clinical acceptability. Our work also provides a more detailed DVH analysis of single targets than earlier published studies. This is relevant for SRS plans and small structure dose assessments. Very small structures are a particular problem because of their coarse digital representation, and the impact of this is thoroughly examined.Reference DVH curves were derived mathematically, based on Gaussian dose distributions centered on spherical structures. The structures and dose distributions were generated synthetically, and imported into RayStation, MasterPlan, and ProKnow. Corresponding DVHs were analytically derived and taken as ground truth references, for comparison with the commercial DVH calculations. Two commonly used dose metrics PCI and MGI were used to determine the limit of calculation accuracy for small structures. In addition, to measure the DVH differences between a larger range of commercial DVH calculators, the D95 metric from a set of real clinical plans was compared across both the 3 DVH calculators under test, and across a further six TPSs from other hospitals.We show that even slight deviations between the results of DVH calculators can lead to plan check failures, and we illustrate this with the commonly used D95 planning metric. We present clinical data across eight planning systems that highlight instances where plan checks would pass in one software and fail in another due to DVH calculation differences. For the smallest volumes tested, errors of up to 20% were observed in the DVHs. RayStation was tested down to a 3 mm radius sphere (≈0.1 cc) and this showed close to 10% error, reducing to 1% for 10 mm radius (≈4.0 cc) and 0.1% for 20 mm radius (≈33 cc). In clinical plans, the variation in D95 was up to 9% for the smallest volumes, and typically around 2% in the range 0.5 cc-20 cc, and 1% in 20 cc-70 cc, falling to <0.1% for large volumes. Paddick Conformity Index (PCI) and Modified Gradient Index (MGI) are commonly used plan quality indicators for very small volumes. For volumes ≈0.1 cc we observed errors of up to 40% in PCI, and up to 75% in MGI.Our study extends the range of tested DVH calculators in published work, and shows their performance over a wider range of volume sizes. We provide quantitative evidence of the critical need to test the accuracy of DVH calculators in the TPS before clinical use. This work is particularly relevant for both stereotactic plan evaluation and for assessment of small volume doses in published dose constraint recommendations. We demonstrate that significant errors can occur in DVHs for volumes less than 1 cc, even if the volumes themselves are calculated accurately. Even for large structures, deviations between the outputs of DVH calculators can lead to indicated or reported plan check failures if they do not include appropriate tolerances. We urge caution in the use of DVH metrics for these very small volumes and recommend that appropriate DVH uncertainty tolerances are set in organ dose constraints when using them to evaluate clinical plans.

Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization

Objective. Treatment plan optimization in high dose rate brachytherapy often requires manual fine-tuning of penalty weights for each objective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach. The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results. MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 s. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution.Significance. MOBO-qNEHVI combined with FMIO can automatically explore the trade-offs between treatment plan objectives in a patient specific manner within a minute. This approach can reduce the dependency of plan quality on planner’s experience and reduce dose to the organs at risk.

Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction

Background and purposeMRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC. Materials and methodsAn in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes. ResultsThe planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%. ConclusionThe straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.

Linear Boltzmann equation solver for voxel-level dosimetry in radiopharmaceutical therapy: Comparison with Monte Carlo and kernel convolution.

With recent interest in patient-specific dosimetry for radiopharmaceutical therapy (RPT) and selective internal radiation therapy (SIRT), an increasing number of voxel-based algorithms are being evaluated. Monte Carlo (MC) radiation transport, generally considered to be the most accurate among different methods for voxel-level absorbed dose estimation, can be computationally inefficient for routine clinical use. This work demonstrates a recently implemented grid-based linear Boltzmann transport equation (LBTE) solver for fast and accurate voxel-based dosimetry in RPT and SIRT and benchmarks it against MC. A deterministic LBTE solver (Acuros MRT) was implemented within a commercial RPT dosimetry package (Velocity 4.1). The LBTE is directly discretized using an adaptive mesh refined grid and then the coupled photon-electron radiation transport is iteratively solved inside specified volumes to estimate radiation doses from both photons and charged particles in heterogeneous media. To evaluate the performance of the LBTE solver for RPT and SIRT applications, 177 Lu SPECT/CT, 90 Y PET/CT, and 131 I SPECT/CT images of phantoms and patients were used. Multiple lesions (2-1052mL) and normal organs were delineated for each study. Voxel dosimetry was performed with the LBTE solver, dose voxel kernel (DVK) convolution with density correction, and a validated in-house MC code using the same time-integrated activity and density maps as input to the different dose engines. The resulting dose maps, difference maps, and dose-volume-histogram (DVH) metrics were compared, to assess the voxel-level agreement. Evaluation of mean absorbed dose included comparison with structure-level estimates from OLINDA. In the phantom inserts/compartments, the LBTE solver versus MC and DVK convolution demonstrated good agreement with mean absorbed dose and DVH metrics agreeing to within 5% except for the D90 and D70 metrics of a very low activity concentration insert of 90 Y where the agreement was within 15%. In the patient studies (five patients imaged after 177 Lu DOTATATE RPT, five after 90 Y SIRT, and two after 131 I radioimmunotherapy), in general, there was better agreement between the LBTE solver and MC than between LBTE solver and DVK convolution for mean absorbed dose and voxel-level evaluations. Across all patients for all three radionuclides, for soft tissue structures (kidney, liver, lesions), the mean absorbed dose estimates from the LBTE solver were in good agreement with those from MC (median difference<1%, maximum 9%) and those from DVK (median difference<5%, maximum 9%). The LBTE and OLINDA estimates for mean absorbed dose in kidneys and liver agreed to within 10%, but differences for lesions were larger with a maximum 14% for 177 Lu, 23% for 90 Y, and 26% for 131 I. For bone regions, the agreement in mean absorbed doses between LBTE and both MC and DVK were similar (median<11%, max 11%) while for lung the agreement between LBTE and MC (median<1%, max 8%) was substantially better than between LBTE and DVK (median<16%, max 33%). Voxel level estimates for soft tissue structures also showed good agreement between the LBTE solver and both MC and DVK with a median difference<5% (maximum<13%) for the DVH metrics with all three radionuclides. The largest difference in DVH metrics was for the D90 and D70 metric in lung and bone where the uptake was low. Here, the difference between LBTE and MC had a median value<14% (maximum 23%) for bone and<4% (maximum 37%) for lung, while the corresponding differences between LBTE and DVK were<23% (maximum 31%) and<67% (maximum 313%), respectively. For a typical patient with a matrix size of 166×166×129 (voxel size 3×3×3 mm3 ), voxel dosimetry using the LBTE solver was as fast as ∼2 min on a desktop computer. Having established good agreement between the LBTE solver and MC for RPT and SIRT applications, the LBTE solver is a viable option for voxel dosimetry that can be faster than MC. Further analysis is being performed to encompass the broad range of radionuclides and conditions encountered clinically.

Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy

AimsAccurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. Materials and methodsWe proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. ResultsThe prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. ConclusionAtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.

Impact of radiation dose distribution on nutritional supplementation needs in head and neck cancer radiotherapy: a voxel-based machine learning approach.

To investigate the relationship between nutritional supplementation and radiation dose to the pharyngeal constrictor muscles and larynx for head and neck (HN) cancer patients undergoing radiotherapy. We retrospectively analyzed radiotherapy (RT) dose for 231 HN cancer patients, focusing on the pharyngeal constrictors and larynx. We defined nutritional supplementation as feeding tube utilization or >10% weight loss from baseline within 90 days after radiotherapy completion. Using deformable image registration (DIR), we mapped each patient's anatomical structures to a reference coordinate system, and corresponding deformations were applied to dose matrices. Voxel doses were utilized as features for ridge logistic regression models, optimized through 5-fold cross-validation. Model performance was assessed with area under the curve of a receiver operating curve (AUC) and F1 score. We built and compared models using 1) pharyngeal constrictor voxels, 2) larynx voxels, 3) clinical factors and mean regional dose metrics, and 4) clinical factors and dose-volume histogram metrics. Test set AUCs were compared among the models, and feature importance was evaluated. DIR of the pharyngeal constrictors and larynx yielded mean Dice coefficients of 0.80 and 0.84, respectively. Pharyngeal constrictors voxels and larynx voxel models had AUC of 0.88 and 0.82, respectively. Voxel-based dose modeling identified the superior to middle regions of the pharyngeal constrictors and the superior region of larynx as most predictive of feeding tube use/weight loss. Univariate analysis found treatment setting, treatment laterality, chemotherapy, baseline dysphagia, weight, and socioeconomic status predictive of outcome. An aggregated model using mean doses of pharyngeal constrictors and larynx subregions had an AUC of 0.87 and the model using conventional DVH metrics had an AUC of 0.85 with p-value of 0.04. Feature importance calculations from the regional dose model indicated that mean doses to the superior-middle pharyngeal constrictor muscles followed by mean dose to the superior larynx were most predictive of nutritional supplementation. Machine learning modeling of voxel-level doses enables identification of subregions within organs that correlate with toxicity. For HN radiotherapy, doses to the superior-middle pharyngeal constrictors are most predictive of feeding tube use/weight loss followed by the doses to superior portion of the larynx.

Plan complexity metrics for head and neck VMAT competition plans

Previous plan competitions have largely focused on dose metric assessments. However, whether the submitted plans were realistic and reasonable from a quality assurance (QA) perspective remains unclear. This study aimed to investigate the relationship between aperture-based plan complexity metrics (PCM) in volumetric modulated arc therapy (VMAT) competition plans and clinical treatment plans verified through patient-specific QA (PSQA). In addition, the association of PCMs with plan quality was examined. A head and neck (HN) plan competition was held for Japanese institutions from June 2019 to July 2019, in which 210 competition plans were submitted. Dose distribution quality was quantified based on dose-volume histogram (DVH) metrics by calculating the dose distribution plan score (DDPS). Differences in PCMs between the two VMAT treatment plan groups (HN plan competitions held in Japan and clinically accepted HN VMAT plans through PSQA) were investigated. The mean (± standard deviation) DDPS for the 98 HN competition plans was 158.5 ± 20.6 (maximum DDPS: 200). DDPS showed a weak correlation with PCMs with a maximum r of 0.45 for monitor unit (MU); its correlation with some PCMs was "very weak." Significant differences were found in some PCMs between plans with the highest 20% DDPSs and the remaining plans. The clinical VMAT and competition plans revealed similar distributions for some PCMs. Deviations in PCMs for the two groups were comparable, indicating considerable variability among planners regarding planning skills. The plan complexity for HN VMAT competition plans increased for high-quality plans, as shown by the dose distribution. Direct comparison of PCMs between competition plans and clinically accepted plans showed that the submitted HN VMAT competition plans were realistic and reasonable from the QA perspective. This evaluation may provide a set of criteria for evaluating plan quality in plan competitions.

RapidBrachyTG43: A Geant4-based TG-43 parameter and dose calculation module for brachytherapy dosimetry.

The AAPM TG-43U1 formalism remains the clinical standard for dosimetry of low- and high-energy -emitting brachytherapy sources. TG-43U1 and related reports provide consensus datasets of TG-43 parameters derived from various published measured data and Monte Carlo simulations. These data are used to perform standardized and fast dose calculations for brachytherapy treatmentplanning. Monte Carlo TG-43 dosimetry parameters are commonly derived to characterize novel brachytherapy sources. RapidBrachyTG43 is a module of RapidBrachyMCTPS, a Monte Carlo-based treatment planning system, designed to automate this process, requiring minimal user input to prepare Geant4-based Monte Carlo simulations for a source. RapidBrachyTG43 may also perform a TG-43 dose to water-in-water calculation for a plan, substantially accelerating the same calculation performed using RapidBrachyMCTPS's Monte Carlo dose calculationengine. TG-43 parameters , , , and were calculated using three commercial source models, one each of I, Ir, and Co, and were benchmarked to published data. TG-43 dose to water was calculated for a clinical breast brachytherapy plan and was compared to a Monte Carlo dose calculation with all patient tissues, air, and catheters set towater. TG-43 parameters for the three simulated sources agreed with benchmark datasets within tolerances specified by the High Energy Brachytherapy Dosimetry working group. A gamma index comparison between the TG-43 and Monte Carlo dose-to-water calculations with a dose difference and difference to agreement criterion of 1%/1 mm yielded a 98.9% pass rate, with all relevant dose volume histogram metrics for the plan agreeing within 1%. Performing a TG-43-based dose calculation provided an acceleration of dose-to-water calculation by a factor of165. Determination of TG-43 parameter data for novel brachytherapy sources may now be facilitated by RapidBrachyMCTPS. These parameter datasets and existing consensus or published datasets may also be used to determine the TG-43 dose for a plan inRapidBrachyMCTPS.

Pharyngeal Constrictor Dose–Volume Histogram Metrics and Patient-Reported Dysphagia in Head and Neck Radiotherapy

AimsHead and neck radiotherapy long-term survival continues to improve and the management of long-term side-effects is moving to the forefront of patient care. Dysphagia is associated with dose to the pharyngeal constrictors and can be measured using patient-reported outcomes to evaluate its effect on quality of life. The aim of the present study was to relate pharyngeal constrictor dose–volume parameters with patient-reported outcomes to identify prognostic dose constraints. Materials and methodsA 64-patient training cohort and a 24-patient testing cohort of oropharynx and nasopharynx cancer patients treated with curative-intent chemoradiotherapy were retrospectively examined. These patients completed the MD Anderson Dysphagia Inventory outcome survey at 12 months post-radiotherapy to evaluate late dysphagia: a composite score lower than 60 indicated dysphagia. The pharyngeal constrictor muscles were subdivided into four substructures: superior, middle, inferior and cricopharyngeal. Dose–volume histogram (DVH) metrics for each of the structure combinations were extracted. A decision tree classifier was run for each DVH metric to identify dose constraints optimising the accuracy and sensitivity of the cohort. A 60% accuracy threshold and feature selection method were used to ensure statistically significant DVH metrics were identified. These dose constraints were then validated on the 24-patient testing cohort. ResultsExisting literature dose constraints only had two dose constraints performing above 60% accuracy and sensitivity when evaluated on our training cohort. We identified two well-performing dose constraints: the pharyngeal constrictor muscle D63% < 55 Gy and the superior–middle pharyngeal constrictor combination structure V31Gy < 100%. Both dose constraints resulted in ≥73% mean accuracy and ≥80% mean sensitivity on the training and testing patient cohorts. In addition, a pharyngeal constrictor muscle mean dose <57 Gy resulted in a mean accuracy ≥74% and mean sensitivity ≥60%. ConclusionMid-dose pharyngeal constrictor muscle and substructure combination dose constraints should be used in the treatment planning process to reduce late patient-reported dysphagia.

CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study

BackgroundPseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective.Methods34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation.ResultsThere was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5–99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation.ConclusionsThis is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis.

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation. The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans. Fourteen H&N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (<250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone(>500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference>80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric. The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7±0.3, 1.1±0.1, 1.0±0.1, 0.9±0.1 and 0.8±0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t=0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences>80 HU of note were the left and right (L/R) cochlea and mandible (>79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring>29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5%±2% (3mm, 3%) and 92.7%±2.1% (2mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3%±3.0% and 0.7%±1.9% respectively. In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage.

Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy.

The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes. Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter. Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel. Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis.

Online dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation-free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images. In this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT-based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT-based dose calculations prior to radiation treatment delivery. An in-house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid-based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT-based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac-mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity-modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter-free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high-performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration. When compared to the gold-standard pCT, dosimetric errors in quantitative CBCT-based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high-performance ASG CBCT-based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets. The proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold-standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.

Improved Dosimetry and Plan Quality for Accelerated Partial Breast Irradiation Using Online Adaptive Radiation Therapy: A Single Institutional Study

PurposeAccelerated partial breast irradiation (APBI) is an attractive treatment modality for eligible patients as it has been shown to result in similar local control and improved cosmetic outcomes compared to whole breast radiotherapy. The use of online adaptive radiotherapy (OART) for APBI is promising as it allows for a reduction of PTV margins since breast motion and lumpectomy cavity volume changes are accounted for in daily imaging. Here we present a retrospective, single-institution evaluation on the adequacy of kV-cone beam computed tomography (CBCT) OART for APBI treatments. Methods and Materials19 patients (21 treatment sites) were treated to 30 Gy in 5 fractions between January of 2022 and May of 2023. Time between simulation and treatment, change in gross tumor (i.e. lumpectomy cavity) volume (GTV), and differences in dose volume histogram (DVH) metrics with adaption were analyzed. The Wilcoxon paired, non-parametric test was utilized to test for DVH metric differences between the scheduled plans (initial plans recalculated on daily CBCT anatomy) and delivered plans, either the scheduled or adapted plan, which was re-optimized using daily anatomy. ResultsMedian (inter-quartile range) time from simulation to first treatment was 26 days (21-32 days). During this same time, median GTV volume reduction was 16.0% (7.3-23.9%) relative to simulation volume. Adaptive treatments took 31.3 min (27.4-36.6 min) from start of CBCT to treatment session end. At treatment, the adaptive plan was selected for 86% (89/103) of evaluable fractions. In evaluating plan quality, 78% of delivered plans met all target, OAR, and conformity metrics evaluated, compared to 34% of scheduled plans. ConclusionsUse of OART for stereotactic linac-based APBI allowed for safe, high-quality treatments in this cohort of 21 treatment courses. Although treatment delivery times were longer than traditional stereotactic body treatments, there were notable improvements in plan quality for APBI using OART.

Dosimetric impact of CT metal artifact reduction for spinal implants in stereotactic body radiotherapy planning.

Metal artifacts due to spinal implants may affect the accuracy of dose calculation for radiotherapy. However, the dosimetric impact of metal artifact reduction (MAR) for spinal implants in stereotactic body radiotherapy (SBRT) plans has not been well studied. The objective of this study was to evaluate the dosimetric impact of MAR in spinal SBRT planning with three clinically common dose calculation algorithms. Gammex phantom and 10 patients' computed tomography (CT) images were studied to investigate the effects of titanium implants. A commercial orthopedic MAR algorithm was employed to reduce artifacts. Dose calculations for SBRT were conducted on both artifact-corrected and uncorrected images using three commercial algorithms [analytical anisotropic algorithm (AAA), Acuros XB (AXB), and Monte Carlo (MC)]. Dose discrepancies between artifact-corrected and uncorrected cases were appraised using a dose-volume histogram (DVH) and 3-dimensional (3D) gamma analysis with different distance to agreement (DTA) and dose difference criteria. The gamma agreement index (GAI) was denoted as G(∆D, DTA). Statistical analysis of t-test was utilized to evaluate the dose differences of different algorithms. The phantom study demonstrated that titanium metal artifacts can be effectively reduced. The patient cases study showed that dose differences between the artifact-corrected and uncorrected datasets were small evaluated by gamma index and DVH. Gamma analysis found that even the strict criterion local G(1,1) had average values ≥93.9% for the three algorithms. For all DVH metrics, average differences did not exceed 0.7% in planning target volume (PTV) and 2.1% in planning risk volume of spinal cord (PRV-SC). Statistical analysis showed that the observed dose differences of MC method were significantly larger than those of AAA (P<0.01 for D98% of PTV and P<0.001 for D0.1cc of spinal cord) and AXB methods (P<0.001 for D98% and P<0.0001 for D0.1cc). Dosimetric impact of artifacts caused by titanium implants is not significant in spinal SBRT planning, which indicates that dose calculation algorithms might not be very sensitive to CT number variation caused by titanium inserts.

Artificial intelligence based auto-contouring solutions for use in radiotherapy treatment planning of head and neck cancer

BackgroundManual contouring is time-consuming and subjective. Thus, auto-segmentation methods, which can be deployed in the existing workflow, are needed. The objective of this study was to assess the feasibility of Limbus AI and AI Rad Companion auto-contours for head and neck treatment planning. MethodsHead and neck patients treated with RapidArc were selected retrospectively. The manual contours on the planning CT were used as reference. Geometric analysis of the auto-contours was performed using several evaluation metrics such as the Dice Similarity Coefficient (DSC) and the Mean Distance to Conformity (MDC). Dosimetric analysis was performed by recalculating the original plan on the auto-contours and comparing Dose Volume Histogram (DVH) metrics to the original plan. Results and discussionBoth AI tools tend to underestimate the volumes of brainstem and cord. For brainstem and parotids, median DSC values were ≥ 0.8. For all auto-contours, median MDC values were ∼ 3–6 mm. Median differences were found of up to ±7 % in DVH points on the auto-contours relative to the planning CT contours, but these were not statistically-significant. ConclusionThe auto-contours could be used as a starting point to assist the clinician with the manual contouring of structures on the planning and re-scanning planning CT.

Feasibility study of adaptive radiotherapy with Ethos for breast cancer.

The aim of this study was to assess the feasibility of online adaptive radiotherapy with Ethos for breast cancer. This retrospective study included 20 breast cancer patients previously treated with TrueBeam. All had undergone breast surgery for different indications (right/left, lumpectomy/mastectomy) and were evenly divided between these four cases, with five extended cone beam computed tomography (CBCT) scans per patient. The dataset was used in an Ethos emulator to test the full adaptive workflow. The contours generated by artificial intelligence (AI) for the influencers (left and right breasts and lungs, heart) and elastic or rigid propagation for the target volumes (internal mammary chain (IMC) and clavicular lymph nodes (CLNs)) were compared to the initial contours delineated by the physician using two metrics: Dice similarity coefficient (DICE) and Hausdorff 95% distance (HD95). The repeatability of influencer generation was investigated. The times taken by the emulator to generate contours, optimize plans, and calculate doses were recorded. The quality of the scheduled and adapted plans generated by Ethos was assessed using planning target volume (PTV) coverage, homogeneity indices (HIs), and doses to organs at risk (OARs) via dose-volume histogram (DVH) metrics. Quality assurance (QA) of the treatment plans was performed using an independent portal dosimetry tool (EpiQA) and gamma index. On average, the DICE for the influencers was greater than 0.9. Contours resulting from rigid propagation had a higher DICE and a lower HD95 than those resulting from elastic deformation but remained below the values obtained for the influencers: DICE values were 0.79 ± 0.11 and 0.46 ± 0.17 for the CLN and IMC, respectively. Regarding the repeatability of the influencer segmentation, the DICE was close to 1, and the mean HD95 was strictly less than 0.15mm. The mean time was 73 ± 4 s for contour generation per AI and 80 ± 9 s for propagations. The average time was 53 ± 3 s for dose calculation and 125 ± 9 s for plan optimization. A dosimetric comparison of scheduled and adapted plans showed a significant difference in PTV coverage: dose received by 95% of the volume (D95%) values were higher and closer to the prescribed doses for adapted plans. Doses to organs at risk were similar. The average gamma index for quality assurance of adapted plans was 99.93 ± 0.38 for a 3%/3mm criterion. This study comprehensively evaluated the Ethos® adaptive workflow for breast cancer and its potential technical limitations. Although the results demonstrated the high accuracy of AI segmentation and the superiority of adapted plans in terms of target volume coverage, a medical assessment is still required.

Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy.

Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning. This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer. CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network(cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics. The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2mm) has reached 99.02%, and the WET difference is only 1.28mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model. The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.

TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance

Objective. Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range dependency for volumetric dose prediction due to the loss of spatial dose features and the inherent locality of CPs. The purpose of this work is to construct a deep hybrid network by combining the self-attention mechanism-based Transformer with modified U-Net for predicting measurement-guided volumetric dose (MDose) of prePSQA. Approach. The enrolled 307 cancer patients underwent VMAT were randomly divided into 246 and 61 cases for training and testing the model. The input data included computed tomography images, radiotherapy dose images exported from the treatment planning system, as well as the MDose distribution from the verification system. The output was the predicted high-quality voxel-wise prePSQA dose distribution. Main results: qualitative and quantitative experimental results show that the proposed prediction method could achieve comparable or better performance on MDose prediction over other approaches in terms of spatial dose distribution, dose–volume histogram metrics, gamma passing rates, mean absolute error, root mean square error, and structural similarity. Significance. The preliminary results on multiple cancer sites show that our approach can be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Dose Constraints and Planning Considerations for Thoracic Radiation Therapy: Delphi Consensus from a National Survey of Experts

Many physicians refer to trial protocols or published guidelines (NCCN, QUANTEC, HyTEC) for dose-volume histogram (DVH) metrics. However, there may be variation in implementing these metrics during plan optimization. Some studies have suggested better outcomes for patients treated at high-volume, high-expertise centers. These differences may in part be due to greater standardization or center-specific treatment planning processes. We surveyed radiation oncologists with stated thoracic-specific expertise using the Delphi method to formulate consensus DVH metrics that would be considered ideal for high-quality radiation treatment plans. Thoracic radiation oncology experts were identified using departmental websites of ACGME-accredited radiation oncology programs. After confirming their expertise, panelists were invited to submit their institutional templates and complete three rounds of questions related to normal organ dose constraints, target coverage metrics, prescribing practices, and other planning considerations. Queried radiation schemes included conventional fractionation, twice-daily fractionation, and stereotactic body radiation therapy (3 and 5 fractions). Preliminary consensus statements were generated using median values for DVH metrics and were iteratively refined in subsequent surveys. Consensus was pre-defined as ≥75% agreement among panelists. A total of 194 experts were invited, and 100 agreed to participate. The panel was 28% female and included experts from 29 states with a median of 11 years of clinical experience (IQR 6-19). 89% specialized in 1-2 disease sites. Response rates for the Demographics, round 1, 2, and 3 surveys were 83%, 78%, 57%, and 55%, respectively. 93% of panelists believed that DVH metrics should provide thresholds for both optimal and acceptable criteria for treatment planning. 49 of the 96 proposed normal tissue dose constraint statements were iterated to consensus (Table 1), and 5 of 7 proposed target coverage metric statements achieved consensus. This study highlights the heterogeneity in metrics used by thoracic radiation oncologists and provides levels of consensus on ideal and acceptable dose constraints as guidance for treatment planning. Future directions include using these statements to develop prescription templates and acceptance criteria for treatment planning systems for widespread use as well as extending this Delphi approach to additional disease sites.