Combining inter-observer variability, range and setup uncertainty in a variance-based sensitivity analysis for proton therapy.
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ABSTRACT:
SUBMITTER: Hofmaier J
PROVIDER: S-EPMC8645917 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The NIMO TEMPO (Lambda-X, Nivelles, Belgium) is a novel, user-friendly and compact device designed for in vitro optical analysis of refractive and diffractive intraocular lenses (IOLs). This device analyzes the IOL wavefront and generates a synthetic eye model for numerical computation. The objective of this study was to evaluate the precision of this innovative device. Intra- and inter-observer variability were calculated using a two-way analysis of variance (ANOVA) after conducting ten measurements of eight different IOL models, with each measurement being repeated by three distinct operators (resulting in a total of 30 measurements for each IOL). The device demonstrated satisfactory intra- and inter-observer variability in evaluating IOL power and modulation transfer function (MTF) profiles, with values of 0.066 and 0.078 diopters for IOL power and 0.018 and 0.019 for MTF measurements, respectively. Furthermore, this hybrid optical and numerical in vitro IOL wavefront analyzer appears to have several advantages over conventional optical bench devices. It reduces the need for operator manipulation, and allows for numerical modeling of various optical environments, including cornea models and apertures. In conclusion, this novel metrology device designed for refractive and diffractive IOLs appears to provide a satisfactory precision, making it a promising tool in the field of IOL metrology.
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Project description:Background and purposeProton therapy is sensitive to range uncertainties, which typically are accounted for by margins or robust optimization, based on tissue-independent uncertainties. However, range uncertainties have been shown to depend on the specific tissues traversed. The aim of this study was to investigate the differences between range margins based on stopping power ratio (SPR) uncertainties which were tissue-specific (applied voxel-wise) or fixed (tissue-independent or composite).Materials and methodsUncertainties originating from imaging, computed tomography (CT) number estimation, and SPR estimation were calculated for low-, medium-, and high-density tissues to quantify the tissue-specific SPR uncertainties. Four clinical treatment plans (four different tumor sites) were created and recomputed after applying either tissue-specific or fixed SPR uncertainties. Plans with tissue-specific and fixed uncertainties were compared, based on dose-volume-histogram parameters for both targets and organs-at-risk.ResultsThe total SPR uncertainties were 7.0% for low-, 1.0% for medium-, and 1.3% for high-density tissues. Differences between the proton plans with tissue-specific and fixed uncertainties were mainly found in the vicinity of the target. Composite uncertainties were found to capture the tissue-specific uncertainties more accurately than the tissue-independent uncertainties.ConclusionDifferent SPR uncertainties were found for low-, medium-, and high-density tissues indicating that range margins based on tissue-specific uncertainties may be more exact than the standard approach of using tissue-independent uncertainties. Differences between applying tissue-specific and fixed uncertainties were found, however, a fixed uncertainty might still be sufficient, but with a magnitude that depends on the body region.
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| S-EPMC10670653 | biostudies-literature
Project description:ObjectiveTo determine inter-observer and inter-examination variability of manual attenuation measurements of the vertebrae in low-dose unenhanced chest computed tomography (CT).MethodsThree hundred and sixty-seven lung cancer screening trial participants who underwent baseline and repeat unenhanced low-dose CT after 3 months because of an indeterminate lung nodule were included. The CT attenuation value of the first lumbar vertebrae (L1) was measured in all CTs by one observer to obtain inter-examination reliability. Six observers performed measurements in 100 randomly selected CTs to determine agreement with limits of agreement and Bland-Altman plots and reliability with intraclass correlation coefficients (ICCs). Reclassification analyses were performed using a threshold of 110 HU to define osteoporosis.ResultsInter-examination reliability was excellent with an ICC of 0.92 (p < 0.001). Inter-examination limits of agreement ranged from -26 to 28 HU with a mean difference of 1 ± 14 HU. Inter-observer reliability ICCs ranged from 0.70 to 0.91. Inter-examination variability led to 11.2 % reclassification of participants and inter-observer variability led to 22.1 % reclassification.ConclusionsVertebral attenuation values can be manually quantified with good to excellent inter-examination and inter-observer reliability on unenhanced low-dose chest CT. This information is valuable for early detection of osteoporosis on low-dose chest CT.Key points• Vertebral attenuation values can be manually quantified on low-dose unenhanced CT reliably. • Vertebral attenuation measurements may be helpful in detecting subclinical low bone density. • This could become of importance in the detection of osteoporosis.
| S-EPMC4972882 | biostudies-literature
Project description:ObjectivesEstablishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning.MethodsFour independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots.ResultsPre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement.ConclusionsManual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models.Key points• Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.
| S-EPMC10017115 | biostudies-literature
Project description:PurposeSeveral efforts are being undertaken toward MRI-based treatment planning for ocular proton therapy for uveal melanoma (UM). The interobserver variability of the gross target volume (GTV) on magnetic resonance imaging (MRI) is one of the important parameters to design safety margins for a reliable treatment. Therefore, this study assessed the interobserver variation in GTV delineation of UM on MRI.Methods and materialsSix observers delineated the GTV in 10 different patients using the Big Brother contouring software. Patients were scanned at 3T MRI with a surface coil, and tumors were delineated separately on contrast enhanced 3DT1 (T1gd) and 3DT2-weighted scans with an isotropic acquisition resolution of 0.8 mm. Volume difference and overall local variation (median standard deviation of the distance between the delineated contours and the median contour) were analyzed for each GTV. Additionally, the local variation was analyzed for 4 interfaces: sclera, vitreous, retinal detachment, and tumor-choroid interface.ResultsThe average GTV was significantly larger on T1gd (0.57cm3) compared with T2 (0.51cm3, P = .01). A not significant higher interobserver variation was found on T1gd (0.41 mm) compared with T2 (0.35 mm). The largest variations were found at the tumor-choroid interface due to peritumoral enhancement (T1gd, 0.62 mm; T2, 0.52 mm). As a result, a larger part of this tumor-choroid interface appeared to be included on T1gd-based GTVs compared with T2, explaining the smaller volumes on T2.ConclusionsThe interobserver variation of 0.4 mm on MRI are low with respect to the voxel size of 0.8 mm, enabling small treatment margins. We recommend delineation based on the T1gd-weighted scans, as choroidal tumor extensions might be missed.
| S-EPMC9860418 | biostudies-literature
Project description:PurposeThe objective of this study was to investigate the dosimetric impact of range uncertainty in a large cohort of patients receiving passive scatter proton therapy.MethodsA cohort of 120 patients were reviewed in this study retrospectively, of which 61 were brain, 39 lung, and 20 prostate patients. Range uncertainties of ±3.5% (overshooting and undershooting by 3.5%, respectively) were added and recalculated on the original plans, which had been planned according to our clinical planning protocol while keeping beamlines, apertures, compensators, and dose grids intact. Changes in the coverage on CTV and DVH for critical organs were compared and analyzed. Correlation between dose change and minimal distance between CTV and critical organs were also investigated.ResultsAlthough CTV coverages and maximum dose to critical organs were largely maintained for most brain patients, large variations over 5% were still observed sporadically. Critical organs, such as brainstem and chiasm, could still be affected by range uncertainty at 4 cm away from CTV. Coverage and OARs in lung and prostate patients were less likely to be affected by range uncertainty with very few exceptions.ConclusionThe margin recipe in modern TPS leads to clinically acceptable OAR doses in the presence of range uncertainties. However, range uncertainties still pose a noticeable challenge for small but critical serial organs near tumors, and occasionally for large parallel organs that are located distal to incident proton beams.
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Project description:IntroductionUniform case definitions are required to ensure harmonised reporting of neurological syndromes associated with SARS-CoV-2. Moreover, it is unclear how clinicians perceive the relative importance of SARS-CoV-2 in neurological syndromes, which risks under- or over-reporting.MethodsWe invited clinicians through global networks, including the World Federation of Neurology, to assess ten anonymised vignettes of SARS-CoV-2 neurological syndromes. Using standardised case definitions, clinicians assigned a diagnosis and ranked association with SARS-CoV-2. We compared diagnostic accuracy and assigned association ranks between different settings and specialties and calculated inter-rater agreement for case definitions as "poor" (κ ≤ 0.4), "moderate" or "good" (κ > 0.6).Results1265 diagnoses were assigned by 146 participants from 45 countries on six continents. The highest correct proportion were cerebral venous sinus thrombosis (CVST, 95.8%), Guillain-Barré syndrome (GBS, 92.4%) and headache (91.6%) and the lowest encephalitis (72.8%), psychosis (53.8%) and encephalopathy (43.2%). Diagnostic accuracy was similar between neurologists and non-neurologists (median score 8 vs. 7/10, p = 0.1). Good inter-rater agreement was observed for five diagnoses: cranial neuropathy, headache, myelitis, CVST, and GBS and poor agreement for encephalopathy. In 13% of vignettes, clinicians incorrectly assigned lowest association ranks, regardless of setting and specialty.ConclusionThe case definitions can help with reporting of neurological complications of SARS-CoV-2, also in settings with few neurologists. However, encephalopathy, encephalitis, and psychosis were often misdiagnosed, and clinicians underestimated the association with SARS-CoV-2. Future work should refine the case definitions and provide training if global reporting of neurological syndromes associated with SARS-CoV-2 is to be robust.
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Project description:Background and purposeThe delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. Delineation is a time-consuming process that is subject to inter- and intra-observer variability. While methods have been proposed to predict GTV contours, typical approaches ignore variability and therefore fail to utilize the valuable confidence information offered by multiple contours.Materials and methodsIn this work we propose an automatic GTV contouring method for soft-tissue sarcomas from X-ray computed tomography (CT) images, using deep learning by integrating inter- and intra-observer variability in the learned model. Sixty-eight patients with soft tissue and bone sarcomas were considered in this evaluation, all underwent pre-operative CT imaging used to perform GTV delineation. Four radiation oncologists and radiologists performed three contouring trials each for all patients. We quantify variability by defining confidence levels based on the frequency of inclusion of a given voxel into the GTV and use a deep convolutional neural network to learn GTV confidence maps.ResultsResults were compared to confidence maps from the four readers as well as ground-truth consensus contours established jointly by all readers. The resulting continuous Dice score between predicted and true confidence maps was 87% and the Hausdorff distance was 14 mm.ConclusionResults demonstrate the ability of the proposed method to predict accurate contours while utilizing variability and as such it can be used to improve clinical workflow.
| S-EPMC8934266 | biostudies-literature
Project description:BackgroundPET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction.MethodsTwenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences.ResultsThe results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation.ConclusionsFDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.
| S-EPMC7105134 | biostudies-literature