Differences In Dose Distributions Research Articles

Comparison of deep inspiration breath hold and free breathing intensity modulated proton therapy of locally advanced lung cancer

Background and purposeFor locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT. Materials and methodsFortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness. ResultsIn IMPT, DIBH reduced median lungs Dmean from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % (p < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT (p < 0.001). These differences were much larger than between FB and DIBH within each modality. ConclusionDIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.

Optimization of stereotactic radiotherapy (SRT) planning for brain metastases from lung cancer by Cyberknife system: reducing dose distribution inner ear.

Objective: Through retrospective statistical analysis of radiation distribution in inner ear avoidance for brain metastases from lung cancer by the CyberKnife (CK) system, it can provide a reference for stereotactic radiotherapy (SRT) planning and treatment optimization. Methods: Computed tomography/magnetic resonance imaging data of 44 patients with one brain metastases lesion from lung cancer were used to re-plan and analyze, who had been treated by CK system from April 2021 to April 2022. The prescribed doses of 14-30 Gy in 1-3 fractions was simultaneously delivered to the metastatic lesions. The SRT plans for the same patients were replaned under with and without inner ear avoidance setting. The plan parameters and dose distribution differences were compared between plans. Results: All plans met the dose restrictions. There were no significant differences in the coverage (Coverage), conformity index (CI), mean dose (Dmean), the maximum dose (Dmax) and minimum dose (Dmin) of planning target volume (PTV). With inner ear avoidance setting, the Dmax and Dmean of inner ear area decreased by 13.76% and 12.15% (p<0.01), respectively. The total number of machine nodes and monitor units (MU) increased by 4.63% and 1.06%. Conclusions: During the SRT plan designing for brain metastases from lung cancer, the dose distribution in inner ear area could be reduced by avoidance setting, and the patient's hearing would be well protected.

INVESTIGATION OF CENTERING PROBLEM IN THE CADMIUM ZINC TELLURIDE (CZT) CARDIAC CAMERA

Introduction: Cadmium Zinc Telluride (CZT) Cardiac Cameras are a special SPECT cameras with solid state cadmium zinc telluride detectors. This diagnostic method applied in the evaluation of coronary artery disease is well designed for myocardial perfusion scintigraphy. Aim: CZT cardiac cameras have limited detector area. In patients who are morbidly obese or whose heart is not in its normal location in the body, the center of the heart and the isocenter of the device do not coincide. It is aim to investigate how these conditions affect the imaging performance of the CZT cardiac camera. Methods: A homogeneous and spherical ball with a diameter of 4 cm was used for the research. To be able to measure away from the center a wooden phantom with 8 cavities and a total length of 32 cm was designed. The center as reference point has been determined by matching the center of the device with the center of the sphere. For the next imaging, the image were taken by placing the spherical ball in the other cavities, respectively, without changing the center. Results: According to the results obtained, farther from the center, deviations from sphericity and differences in dose distribution were observed. Counts decreased by 1.5% in the 2nd position and 16% in the 3rd position relative to the center in the X-axis. A disaster area has been formed and the image area has been exited from the 4th imaging. Conclusion: It is very important to center the patient as much as possible, otherwise the doctor will misdiagnose.

Artificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

PurposeTo compare the performance of artificial intelligence (AI) in auto-contouring compared to a human practitioner in terms of precision, differences in dose distribution and time consumption. Methods and MaterialsDatasets of previously irradiated patients in three different segments (head and neck- (HNC), breast- (BC) and prostate cancer (PC)) were retrospectively collected. An experienced radiation oncologist (MD) performed organs-at-risk (OARs) and standard clinical target volume (CTV) delineations as baseline structures for comparison. AI-based auto-contours were generated in two additional CT copies; therefore, three groups were assessed: MD alone, AI alone, AI plus MD corrections (AI+C). Differences in Dice similarity coefficient (DSC) and person-hour burden were assessed. Furthermore, changes in clinically relevant dose-volume parameters were evaluated and compared. ResultsSeventy-five previously treated cases were collected (25 per segment) for the analysis. When compared to MD contours, the mean DSC scores were higher than 0.7 for 74% and 80% of AI and AI+C, respectively. After corrections, 17.1% structures presented DSC score deviations higher than 0.1 and 10.4% dose-volume parameters significantly changed in AI-contoured structures. The time consumption assessment yielded mean person-hour reductions of 68%, 51% and 71% for BC, PC and HNC, respectively. ConclusionIn great extent, AI yielded clinically acceptable OARs and certain CTVs in the explored anatomical segments. Sparse correction and assessment requirements place AI+C as a standard workflow. Minimal clinically relevant differences in OAR-exposure were identified. A substantial amount of person-hours could be repurposed with this technology.

Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors

This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose–volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.

Plan Quality Analysis of Automated Treatment Planning Workflow With Commercial Auto-Segmentation Tools and Clinical Knowledge-Based Planning Models for Prostate Cancer.

This study evaluated the feasibility of using artificial intelligence (AI) segmentation software for volume-modulated arc therapy (VMAT) prostate planning in conjunction with knowledge-based planning to facilitate a fully automated workflow. Two commercially available AI software programs, Radformation AutoContour (Radformation, New York, NY) and Siemens AI-Rad Companion (Siemens Healthineers, Malvern, PA) were used to auto-segment the rectum, bladder, femoral heads, and bowel bag on 30 retrospective clinical cases (10 intact prostate, 10 prostate bed, and 10 prostate and lymph node). Physician-segmented target volumes were transferred to AI structure sets. In-house RapidPlan models were used to generate plans using the original, physician-segmented structure sets as well as Radformation and Siemens AI-generated structure sets. Thus, there were three plans for each of the 30 cases, totaling 90 plans. Following RapidPlan optimization, planning target volume (PTV) coverage was set to 95%. Then, the plans optimized using AI structures were recalculated on the physician structure set with fixed monitor units. In this way, physician contours were used as the gold standard for identifying any clinically relevant differences in dose distributions. One-way analysis of variation (ANOVA) was used for statistical analysis. No statistically significant differences were observed across the three sets of plans for intact prostate, prostate bed, or prostate and lymph nodes. The results indicate that an automated volumetric modulated arc therapy (VMAT) prostate planning workflow can consistently achieve high plan quality. However, our results also show that small but consistent differences in contouring preferences may lead to subtle differences in planning results. Therefore, the clinical implementation of auto-contouring should be carefully validated.

Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.

Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation. Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland-Altman plots to assess level of agreement. Auto-contouring was significantly faster than manual (1.1/20min, p<0.01). Median DSC and MSD were 0.7/0.9mm for AI and 0.8/0.5mm for AIedit. DSC was significantly correlated with structure size (ρ=0.76, p<0.01), with higher DSC for large structures. Median gamma pass rate was 74% (71-81%) for Plan_AI and 82% (75-86%) for Plan_AIedit, with no correlation with DSC or MSD. Differences between Dmean_AI and Dmean_Ref were≤0.2Gy (p<0.05). The dose difference was moderately correlated with DSC. Bland Altman plot showed minimal discrepancy (0.1/0) between AI and reference Dmean/Dmax. The AI-model showed good accuracy for large structures, but developments are required for smaller ones. Auto-segmentation was significantly faster, with minor differences in dose distribution caused by geometric variations.

Most Appropriate Radiation Therapy Techniques for The Breast Cancer Treatment: Dosimetric Analysis of Three Different Radiation Therapy Methods

Background: Radiation-induced cardiac events and pneumonitis are the major late radiotherapy side effects. Methods: We explored the differences in dose distribution between 3D CRT, IMRT and VMAT in 125 women with unilateral breast cancer. The various dose distribution to the tumor and OARs were studied for left and right sided irradiation. Results: Upon using 3D CRT breasts with lymph nodes, we observed a significant dose increase in the ipsilateral lung. Having irradiated only the right breast, there was a negligible Dmean difference to the adjacent lung between three methods, likewise for the myocardium in left breast cases. For right-sided cases 3D CRT showed the lowest doses in myocardium. Conclusion: Radiation oncologists should consider VMAT if conditions do not allow devising three plans with different methods. This in turn will reduce cardio and pneumotoxicity of left breast cancer treatment. In the treatment of right breast cancer, physicians should focus on 3D CRT.

Investigation on the proton range uncertainty with spectral CT-based virtual monoenergetic images.

The stopping power ratio (SPR) prediction error will contribute to the range uncertainty of proton therapy. Spectral CT is promising in reducing the uncertainty in SPR estimation. The purpose of this research is to determine the optimal energy pairs of SPR prediction for each tissue and to evaluate the dose distribution and range difference between the spectral CT with the optimal energy pairs method and the single energy CT (SECT) method. A new method was proposed based on image segmentation to calculate the proton dose with spectral CT images for the head and body phantom. CT number of each organ region were converted to SPR with the optimal energy pairs of each organ. The CT images were segmented into different organ parts with thresholding method. Virtual monoenergetic (VM) images from 70keV to 140keV were investigated to determine the optimal energy pairs for each organ based on Gammex 1467 phantom. The beam data of Shanghai Advanced Proton Therapy facility (SAPT) was employed in matRad (an open-source software for radiation treatment planning) for the dose calculation. The optimal energy pairs were obtained for each tissue. The dose distribution of two tumor sites (brain and lung) were calculated with the aforementioned optimal energy pairs. The maximum dose deviation between spectral CT and SECT at the target region was 2.57% and 0.84% for the lung tumor and brain tumor respectively. The range difference between spectral and SECT was significant with 1.8411mm for the lung tumor. γ passing rate was 85.95% and 95.49% for the lung tumor and brain tumor with the criterion 2%/2mm. This work presents a way to determine the optimal energy pairs for each organ and to calculate the dose distribution based on the more accurate SPR prediction.

Impact of log file source and data frequency on accuracy of log file-based patient specific quality assurance

Performing phantom measurements for patient-specific quality assurance (PSQA) adds a significant amount of time to the adaptive radiotherapy procedure. Log file based PSQA can be used to increase the efficiency of this process. This study compared the dosimetric accuracy of high-frequency linear accelerator (Linac) log files and low-frequency log data stored in the oncology information system (OIS). Thirty patients were included, that were recently treated in the head and neck (HN), brain, and prostate region with volumetric modulated arc therapy (VMAT) and an additional ten patients treated using stereotactic body radiation therapy (SBRT) with 3D-conformal radiotherapy (3D-CRT) technique. Log data containing a single fraction were used to calculate the dose distributions. The dosimetric differences between Linac log files and OIS logs were evaluated with a gamma analysis with 2%/2 mm criterion and dose threshold of 30%. The original treatment plan was used as a reference. Moreover, DVH parameters of D98%, D50%, and D2% of the planning-target volume (PTV) and dose to several organs at risk (OARs) were reported. Significant differences in dose distributions between the two log types and the original dose were observed for PTV D98% and D2% (r < 0.001) for HN cases, PTV D98% (r = 0.005) for brain cases, and PTV D50% (r = 0.015) for prostate cases. No significant differences were found between the two log types with respect to D50%. The root mean square (RMS) error of the leaf positions of the OIS log was approximately twice the RMS error of the Linac log file for VMAT plans, but identical for 3D-CRT plans. The relationship between the gamma pass rate and the RMS error showed a moderate correlation for the Linac log files (r = −0.58, p < 0.001) and strong correlation for OIS logs (r = −0.71, p < 0.001). Furthermore, all doses calculated using Linac log files and OIS log data had a GPR >90% for an RMS error < 3.3 mm. Based on these findings, a tolerance limit of RMS error of 3.3 mm for considering OIS log based PSQA was established. Nevertheless, the OIS log data quality should be improved to achieve adequate PSQA.

Methodology to compare alternative design options for radioactive waste disposal facilities under the principle of ALARA based on probabilistic approach with Bayesian inference.

In the process of optimisation of radiological protection in long-term radioactive waste management, design options for disposal facilities are assessed from the viewpoint of practical reduction in radiation dose to as low as reasonably achievable (ALARA). In this paper, a probabilistic approach is introduced to evaluate differences in dose distributions between alternative facility designs using the Bayesian inference technique. It is demonstrated by a series of parametric calculations for hypothetic disposal system designs that an additional dose from an disposal facility could be sufficiently reduced from the viewpoint of dose reduction under the ALARA principle if the design option for a disposal facility whose 95th percentile of dose distribution is lower than the target dose of 0.1mSv/y is provided. Reducing uncertainties of the distribution of dose from disposal options is also an important aspect for the practical reduction in radiation dose to which members of the public are exposed.

A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy.

Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC). However, it can cause implicit RT-induced injury by irradiating normal brain tissue. To date, there have been no detailed reports on the radiated exact location in the brain, the corresponding radiation dose, and their relationship. We analyzed 803 Chinese NPC patients treated with RT and used a CT brain template in a Montreal Neurological Institute (MNI) space to compare the group differences in RT dose distribution for different RT technologies (IMRT or VMAT). Brain regions that received high doses (>50 Gy) of radiation were mainly located in parts of the temporal and limbic lobes, where radioactive damage often occurs. Brain regions that accepted higher doses with IMRT were mainly located near the anterior region of the nasopharyngeal tumor, while brain regions that accepted higher doses with VMAT were mainly located near the posterior region of the tumor. No significant difference was detected between IMRT and VMAT for T1 stage patients. For T2 stage patients, differences were widely distributed, with VMAT showing a significant dose advantage in protecting the normal brain tissue. For T3 stage patients, VMAT showed an advantage in the superior temporal gyrus and limbic lobe, while IMRT showed an advantage in the posterior cerebellum. For T4 stage patients, VMAT showed a disadvantage in protecting the normal brain tissue. These results indicate that IMRT and VMAT have their own advantages in sparing different organs at risk (OARs) in the brain for different T stages of NPC patients treated with RT. Our approach for analyzing dosimetric characteristics in a standard MNI space for Chinese NPC patients provides greater convenience in toxicity and dosimetry analysis with superior localization accuracy. Using this method, we found interesting differences from previous reports: VMAT showed a disadvantage in protecting the normal brain tissue for T4 stage NPC patients.

Inpainting truncated areas of CT images based on generative adversarial networks with gated convolution for radiotherapy.

This study aimed to inpaint the truncated areas of CT images by using generative adversarial networks with gated convolution (GatedConv) and apply these images to dose calculations in radiotherapy. CT images were collected from 100 patients with esophageal cancer under thermoplastic membrane placement, and 85 cases were used for training based on randomly generated circle masks. In the prediction stage, 15 cases of data were used to evaluate the accuracy of the inpainted CT in anatomy and dosimetry based on the mask with a truncated volume covering 40% of the arm volume, and they were compared with the inpainted CT synthesized by U-Net, pix2pix, and PConv with partial convolution. The results showed that GatedConv could directly and effectively inpaint incomplete CT images in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the mean absolute errors for the truncated tissue were 195.54, 196.20, 190.40, and 158.45 HU, respectively. The mean dose of the planning target volume, heart, and lung in the truncated CT was statistically different (p < 0.05) from those of the ground truth CT ([Formula: see text]). The differences in dose distribution between the inpainted CT obtained by the four models and [Formula: see text] were minimal. The inpainting effect of clinical truncated CT images based on GatedConv showed better stability compared with the other models. GatedConv can effectively inpaint the truncated areas with high image quality, and it is closer to [Formula: see text] in terms of image visualization and dosimetry than other inpainting models.

New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer

ObjectiveA generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer. MethodsThe low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN). ResultsThe three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet. ConclusionsHigh-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.

Difference in target dose distributions between Acuros XB and collapsed cone convolution/superposition and the impact of the tumor locations in clinical cases of stereotactic ablative body radiotherapy for lung cancer.

The objective of the study is to analyze the difference in target dose distributions between Acuros XB (AXB) and collapsed cone convolution (CCC)/superposition and the impact of the tumor locations in clinical cases of stereotactic ablative body radiotherapy (SABR) for lung cancer. Ninety-six patients underwent SABR for lung cancers Kyushu University Hospital from 2014 to 2017. We recalculated clinical plans originally calculated by AXB using CCC with the identical monitor units (MUs) and beam arrangements. We calculated the following dosimetric parameters: maximum dose (Dmax), minimum dose (Dmin), homogeneity index (HI), conformity index (CI), and D95 of the planning target volume (PTV). We investigated the difference between the results of two calculations and examined the impact of tumor location. Moreover, we determined the target central dose using a thorax phantom and assessed the calculation accuracy of the two algorithms for each fraction. CCC significantly overestimated the dose to PTV, compared to AXB (P < 0.05). The mean differences of Dmax, Dmin, and D95 were 1.17, 1.95, and 1.85 Gy, respectively. The mean differences of HI and CI were 0.02 and - 0.06. Dmin, HI, and D95 had significant correlations with the tumor location, and the difference was greater when the PTV was included the chest wall (P < 0.05). The discrepancy between the calculated and irradiated dose was 2.48% for CCC, whereas it was 0.14% for AXB. We demonstrated that CCC significantly overestimated the dose to PTV relative to AXB in clinical cases of lung SABR.

Interobserver Variations in Target Delineation in Intensity-Modulated Radiation Therapy for Nasopharyngeal Carcinoma and its Impact on Target Dose Coverage.

To investigate the differences between physicians in target delineation in intensity-modulated radiation therapy for nasopharyngeal carcinoma as well as their impact on target dose coverage. Ninety-nine in-hospital patients were randomly selected for retrospective analysis, and the target volumes were delineated by 2 physicians. The target volumes were integrated with the original plans, and the differential parameters, including the Dice similarity coefficient (DSC), Hausdorff distance (HD), and Jaccard similarity coefficient (JSC) were recorded. The dose-volume parameters to evaluate target dose coverage were analyzed by superimposing the same original plan to the 2 sets of images on which the target volumes were contoured by the 2 physicians. The significance of differences in target volumes and dose coverage were evaluated using statistical analysis. The target dose coverage for different sets of target volumes showed statistically significant differences, while the similarity metrics to evaluate geometric target volume differences did not. More specifically, for PGTVnx, the median DSC, JSC, and HD were 0.85, 0.74, and 11.73, respectively; for PCTV1, the median values were 0.87, 0.77, and 11.78, respectively; for PCTV2, the median values were 0.90, 0.82, and 16.12, respectively. For patients in stages T3-4, DSC, and JSC were reduced but HD was increased compared to those in stages T1-2. Dosimetric analysis indicated that, for the target volumes, significant differences between the 2 physicians were found in D95, D99, and V100 for all the target volumes (ie, PGTVnx, PCTV1, and PCTV2) across the whole group of patients, as well as in patients with disease stages T3-4 and T1-2. The target volumes delineated by the 2 physicians had a high similarity, but the maximal distances between the outer contours of the 2 sets were significantly different. In patients with advanced T stages, significant differences in dose distributions were found, stemming from the deviations of target delineation.

Bi-criteria Pareto optimization to balance irradiation time and dosimetric objectives in proton arc therapy

Objective. Proton arc therapy (PAT) is a new delivery technique that exploits the continuous rotation of the gantry to distribute the therapeutic dose over many angular windows instead of using a few static fields, as in conventional (intensity-modulated) proton therapy. Although coming along with many potential clinical and dosimetric benefits, PAT has also raised a new optimization challenge. In addition to the dosimetric goals, the beam delivery time (BDT) needs to be considered in the objective function. Considering this bi-objective formulation, the task of finding a good compromise with appropriate weighting factors can turn out to be cumbersome. Approach. We have computed Pareto-optimal plans for three disease sites: a brain, a lung, and a liver, following a method of iteratively choosing weight vectors to approximate the Pareto front with few points. Mixed-integer programming (MIP) was selected to state the bi-criteria PAT problem and to find Pareto optimal points with a suited solver. Main results. The trade-offs between plan quality and beam irradiation time (static BDT) are investigated by inspecting three plans from the Pareto front. The latter are carefully picked to demonstrate significant differences in dose distribution and delivery time depending on their location on the frontier. The results were benchmarked against IMPT and SPArc plans showing the strength of degrees of freedom coming along with MIP optimization. Significance. This paper presents for the first time the application of bi-criteria optimization to the PAT problem, which eventually permits the planners to select the best treatment strategy according to the patient conditions and clinical resources available.

Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system

Background and purposeTo investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions. Material and MethodsAll patients underwent iodine contrast-enhanced planning CT. Fourteen OAR were manually delineated. DL.1 and DL.2 solutions were trained with 63 mono-centric patients and > 1000 multi-centric patients, respectively. Ten and 15 patients with varied anatomies were selected for the atlas library and for testing, respectively. The evaluation was based on geometric indices (DICE coefficient and 95th percentile-Hausdorff Distance (HD95%)), time needed for manual corrections and clinical dosimetric endpoints obtained using automated treatment planning. ResultsBoth DICE and HD95% results indicated that DL algorithms generally performed better compared with ABAS algorithms for automatic segmentation of HN OAR. However, the hybrid-ABAS (ABAS.3) algorithm sometimes provided the highest agreement to the reference contours compared with the 2 DL. Compared with DL.2 and ABAS.3, DL.1 contours were the fastest to correct. For the 3 solutions, the differences in dose distributions obtained using AS contours and AS + manually corrected contours were not statistically significant. High dose differences could be observed when OAR contours were at short distances to the targets. However, this was not always interrelated. ConclusionDL methods generally showed higher delineation accuracy compared with ABAS methods for AS segmentation of HN OAR. Most ABAS contours had high conformity to the reference but were more time consuming than DL algorithms, especially when considering the computing time and the time spent on manual corrections.

Density Overrides that Best Predict Bowel Changes during Pencil Beam Scanning Proton Radiotherapy for Prostate Cancer

<h3>Purpose/Objective(s)</h3> Changes in patient anatomy during pencil beam scanning proton radiotherapy (PBSPT) can result in substantial differences in dose distribution. In patients with prostate cancer receiving elective nodal irradiation (ENI), interfraction variability in bowel position or bowel filling can highlight this uncertainty. Such changes are often managed by assessing the plan on CT dataset copies with the bowel contour overridden to other densities. The ideal Hounsfield Unit (HU) overrides remain unclear, but extremes (HU=0 water and HU=-1000 air) are often utilized. The aim of the present study is to determine the bowel density overrides that are most predictive of bowel changes during a course of PBSPT. <h3>Materials/Methods</h3> Consecutive patients, from a single institution, receiving PBSPT with ENI for intact prostate cancer were retrospectively reviewed for the period between 10/2020-12/2021. Proton radiotherapy consisted of 50.4 Gy in 28 fractions to the pelvic lymph node stations with a simultaneous integrated boost to 70 Gy in 28 fractions to the prostate. Individually contoured loops of small bowel, large bowel, and rectum were overridden to HU of 0 to -1000 in 100 HU intervals. A bowel evaluation structure, consisting of a bowel bag cropped 3 cm away from the high-risk volume, was created; its dose volume histogram was reviewed; and values for the maximum point dose (Dmax), V105%, V45Gy, V20Gy were recorded for each plan. The dosimetry of bowel override plans were compared to the patient's Quality Assurance CT (QACT) scans. The bowel override plan which most closely matched the dosimetry of each QACT for each dosimetric parameter was determined, and the prevalence of closest prediction for each override value was tabulated. <h3>Results</h3> A total of 32 QACTs from 12 patients were included. For Dmax, HU=0, HU=-100, and HU=-700 predicted most accurately with 10 (31.3%), 5 (15.6%), and 4 (12.5%) closest predictions, respectively. For V105%, HU=-700 and HU=-1000 performed best with 6 (18.8%) and 7 (21.9%), respectively. For V45, HU=0 and HU=-500 predicted best with 13 (40.6%) and 5 (15.6%), respectively. For V20, HU=0, HU=-200, and HU=-600 predicted best with 18 (56.3%), 4 (12.5%), and 3 (9.4%), respectively. Overall, HU=0, -100, -600, -700, and -1000 predicted best with 42 (32.8%), 11 (8.6%), 10 (7.8%), 12 (9.4%), and 12 (9.4%) out of the 128 total dose metrics, respectively. This created a relative bimodal distribution with peaks in the 0 to -100 and -600 to -700 ranges. The nominal plan predicted best in 13 scenarios. <h3>Conclusion</h3> Bowel density override plans remain an important tool for assessment of PBSPT robustness to bowel filling changes throughout the course of therapy as the nominal CT relatively poorly predicts QACT dosimetry. The relatively bimodal distribution of accurate predictions in this study would suggest that the most predictive overrides would be HU∼0 and HU∼-650. Future investigation into multidataset robust optimization with these overrides to improve plan robustness is planned.

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world’s first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors.