Fully automated delineation of the optic radiation for surgical planning using clinically feasible sequences.
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ABSTRACT:
SUBMITTER: Reid LB
PROVIDER: S-EPMC8596983 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.
| S-EPMC10417353 | biostudies-literature
Project description:PurposeTo propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multileaf collimator.MethodsThe AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in-house. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry.ResultsAUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (-49%)), segments (59.5/98.9 (-40%)), and delivery times (17.1/22.3 min. (-23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (-21%)) and had on average shorter delivery times (18.6/22.3 min. (-17%)). Delivered Monitor Units (MU) were similar for all three planning approaches.ConclusionsA new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (-23%) due to large reductions in beam and segment numbers.
| S-EPMC8457110 | biostudies-literature
Project description:The accurate contour delineation of the target and/or organs at risk (OAR) is essential in treatment planning for image-guided radiation therapy (IGRT). Although many automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. Moreover, clinicians would like to analyze the characteristics of regions of interests (ROI) and adjust contours manually during IGRT. Interactive tool for contour delineation is necessary in such cases. In this work, a novel approach of curve fitting for interactive contour delineation is proposed. It allows users to quickly improve contours by a simple mouse click. Initially, a region which contains interesting object is selected in the image, then the program can automatically select important control points from the region boundary, and the method of Hermite cubic curves is used to fit the control points. Hence, the optimized curve can be revised by moving its control points interactively. Meanwhile, several curve fitting methods are presented for the comparison. Finally, in order to improve the accuracy of contour delineation, the process of the curve refinement based on the maximum gradient magnitude is proposed. All the points on the curve are revised automatically towards the positions with maximum gradient magnitude. Experimental results show that Hermite cubic curves and the curve refinement based on the maximum gradient magnitude possess superior performance on the proposed platform in terms of accuracy, robustness, and time calculation. Experimental results of real medical images demonstrate the efficiency, accuracy, and robustness of the proposed process in clinical applications.
| S-EPMC5711244 | biostudies-literature
Project description:Treatment planning for whole-brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and anual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3-4min. Automated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost-effective solution to planning through modifying previous validated plans to match a current patient's anatomy.
| S-EPMC5690177 | biostudies-literature
Project description:ObjectiveTo develop an automated, reproducible method for delineating hospital service areas (HSAs).Data sources/settingDischarge data from all Swiss acute care hospitals for the years 2013 to 2016.Study designWe derived HSAs and hospital referral regions for Switzerland using a newly developed flow-based, automated, objective, and reproducible method using all discharge data. We compared our method to the classical, partially subjective approach used to delineate the Swiss Health Care Atlas by delineating four sets of intervention-specific HSAs.Principal findingsBased on 4 105 885 discharges, the fully automated method delineated 63 HSAs. Comparison with existing HSAs reveals good overlap and comparable measures of health utilization between the methods and shows that in the Swiss setting, our method outperforms a cluster-based approach to defining HSAs. While the classical method potentially takes an entire day to delineate the regions, our method took approximately 10 minutes.ConclusionsHospital service areas are used to analyze differences in use of health care that may indicate underuse and overuse. Our new, fully automated, objective, and reproducible method provides a useful tool for hospital services researchers that will enable them to delineate and update patient-flow-based HSAs.
| S-EPMC7240760 | biostudies-literature
Project description:PurposeMeticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning.Methods and materialsManual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations.ResultsThe median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively.ConclusionsAutomated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.
| S-EPMC10943520 | biostudies-literature
Project description:Background and purposeCurrent automated radiotherapy planning solutions do not allow for the intuitive exploration of different treatment options during protocol calibration. This work introduces an automated planning solution, which aims to address this problem through incorporating Pareto navigation techniques into the calibration process.Materials and methodsFor each tumour site a set of planning goals is defined. Utilising Pareto navigation techniques an operator calibrates the solution through intuitively exploring different treatment options: selecting the optimum balancing of competing planning goals for the given site. Once calibrated, fully automated plan generation is possible, with specific algorithms implemented to ensure trade-off balancing of new patients is consistent with that during calibration. Using the proposed methodology the system was calibrated for prostate and seminal vesicle treatments. The resultant solution was validated through quantitatively comparing the dose distribution of automatically generated plans (VMATAuto) against the previous clinical plan, for ten randomly selected patients.ResultsVMATAuto yielded statistically significant improvements in: PTV conformity indices, high dose bladder metrics, mean bowel dose, and the majority of rectum dose metrics. Of particular note was the reduction in mean rectum dose (median 25.1 Gy vs. 27.5 Gy), rectum V24.3Gy (median 41.1% vs. 46.4%), and improvement in the conformity index for the primary PTV (median 0.86 vs. 0.79). Dosimetric improvements were not at the cost of other dose metrics.ConclusionsAn automated planning methodology with a Pareto navigation based calibration has been developed, which enables the complex balancing of competing trade-offs to be intuitively incorporated into automated protocols.
| S-EPMC7807535 | biostudies-literature
Project description:Precise procedural planning is crucial to achieve excellent results in patients undergoing Transcatheter aortic valve implantation (TAVI). The aim of this study was to compare the semi-automated 3mensio (3 m) software to the fully-automated HeartNavigator3 (HN) software. We randomly selected 100 patients from our in-house TAVI-registry and compared aortic annulus and perimeter as well as coronary distances between 3m-measurements and post-hoc HN-measurements. Finally, we retrospectively simulated prosthesis choice based on HN-measurements and analyzed the differences compared to routinely used 3 m based strategy. We observed significant differences between the two software packages regarding area (3 m 464 ± 88 mm², HN 482 ± 96 mm², p < 0.001), perimeter (3 m 77 ± 7 mm, HN 79 ± 8 mm, p < 0.001) and coronary distances (LCA: 3 m 13 ± 3 mm, HN 12 ± 3 mm, p < 0.001; RCA: 3 m 16 ± 3 mm, HN 15 ± 3 mm, p < 0.001). Prosthesis choice simulation based on newly obtained HN-measurements would have led to a decision change in 18% of patients, with a further reduction to 4% following manual adjustment of HN-measurements. The fully-automatic HN-software provides higher values for annular metrics and lower annulus-to-coronary-ostia distances compared to 3m-software. Measurement differences did not influence clinical outcome. Both, the HN-software and the 3m-software are sophisticated, reliable and easy to use for the clinician. Manual adjustment of HN-measurements may increase precision in complex aortic annulus anatomy.
| S-EPMC7329903 | biostudies-literature
Project description:ObjectiveTo test the hypothesis that wide area detector face transplant surgical planning CT angiograms with simulated lower radiation dose and iterative reconstruction (AIDR3D) are comparable in image quality to those with standard tube current and filtered back projection (FBP) reconstruction.Materials and methodsThe sinograms from 320-detector row CT angiography of four clinical candidates for face transplantation were processed utilizing standard FBP, FBP with simulated 75, 62, and 50% tube current, and AIDR3D with corresponding dose reduction. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured at muscle, fat, artery, and vein. Image quality for each reconstruction strategy was assessed by two independent readers using a 4-point scale.ResultsCompared to FBP, the median SNR and CNR for AIDR3D images were higher at all sites for all 4 different tube currents. The AIDR3D with simulated 50% tube current achieved comparable SNR and CNR to FBP with standard dose (median muscle SNR: 5.77 vs. 6.23; fat SNR: 6.40 vs. 5.75; artery SNR: 43.8 vs. 45.0; vein SNR: 54.9 vs. 55.7; artery CNR: 38.1 vs. 38.6; vein CNR: 49.0 vs. 48.7; all p-values >0.19). The interobserver agreement in the image quality score was good (weighted κ = 0.7). The overall score and the scores for smaller arteries were significantly lower when FBP with 50% dose reduction was used. The AIDR3D reconstruction images with 4 different simulated doses achieved a mean score ranging from 3.68 to 3.82 that were comparable to the scores from images reconstructed using FBP with original dose (3.68-3.77).ConclusionsSimulated radiation dose reduction applied to clinical CT angiography for face transplant planning suggests that AIDR3D allows for a 50% reduction in radiation dose, as compared to FBP, while preserving image quality.
| S-EPMC3637203 | biostudies-literature
Project description:PurposeMultimodality imaging can enhance the precision of tumor delineation for intensity modulated radiation therapy planning. This study aimed to analyze intermodality variation for gross tumor volume (GTV) delineation in locally advanced oropharyngeal carcinomas (LAOCs).Methods and materialsWe examined the pretreatment contrast-enhanced computed tomography (CECT), magnetic resonance imaging (MRI), and fluoro-deoxy-glucose-based positron emission tomography (FDG-PET) image data sets of 33 adult patients with primary LAOC. Automatic segmentation method was used to derive PET-based metabolic tumor volumes (MTVs) at 30%, 40%, 50%, 60%, and 70% of the primary tumor's maximum standardized uptake value (SUVmax). The geometric conformality or spatial overlap was assessed using the Dice similarity coefficient (DSC), which ranges from 0 to 1, indicating no overlap to complete overlap.ResultsThe size of the tumor in the anteroposterior dimension of the GTV was found to be more on CT than MRI, with a mean difference of 0.29 cm (P value .015). Overall, PET-based MTV volumes were smaller than GTVs on CT and MR. Among various intensities on PET, MTV30 was the closest match with GTV-CT/MR. The mean difference for absolute tumor volumes (GTV-CT, GTV-MR, and MTV30) was not statistically significant; however, spatial overlap by DSC score was average, that is, <0.7. DSC was 0.65 ± 0.15 between GTV-CT and GTV-MR, 0.62 ± 0.15 between GTV-CT and MTV30, and 0.576 ± 0.16 between GTV-MR and MTV30 pairs, respectively. On qualitative analysis, overall tumor extension into adjacent muscles, parotid gland, retromolar trigone, and marrow infiltration of mandible was better appreciated on MRI.ConclusionsGiven the significant spatial variation, multimodality imaging can serve as an excellent complement for target volume delineation on CT scans during intensity modulated radiation therapy planning for LAOC by harnessing the improved soft tissue definition of MRI and the ability of PET to provide metabolic activity information.
| S-EPMC10965431 | biostudies-literature