scholarly journals Comparison of Methods to Estimate Splenic Volume in Myelofibrosis Trials

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4633-4633
Author(s):  
Alice Motovylyak ◽  
Merryl Lobo ◽  
Rohit Sood

Abstract Primary myelofibrosis (PM) is a chronic blood cancer which increases burden on the spleen to produce blood cells and results in palpable splenomegaly. In the clinic, splenomegaly is classified based on the distance between the spleen's lowest point and the left costal margin, however, this method is highly subjective and depends on the subject's position and respiration. Imaging techniques have the potential to provide accurate, reliable, and reproducible measurements of splenic volume (SV). In clinical trials assessing therapy response, an accepted imaging-based endpoint is ≥35% reduction in SV at week 24 from baseline as measured by Magnetic Resonance Imaging (MRI) or Computer Tomography (CT). A ≥25% increase in SV is typically considered progression. The most accurate method for volume assessment is manual segmentation, since the entire spleen boundary can be utilized for the volume calculation. This study compared two other volume estimation methods: ellipsoid method and a model proposed by Bezerra et al (AJR Am J Roentgenol. 2005). We compared the methods' performance in assessing treatment response or progression based on SV change from baseline to week 24. Imaging data from 30 participants were used in this study, predominantly acquired using MRI modality; CT was used as an alternative, when MRI was contraindicated. Scans from two timepoints per participant were used: baseline and 24 weeks after start of treatment. For the manual segmentation method, preliminary regions of interest were manually outlined on every imaging slice by an experienced imaging analyst and then reviewed by a trained radiologist. SV was derived by multiplying the number of voxels contained in the spleen outlined by the voxel size of the scan. For the ellipsoid method, maximum width (W) and orthogonal thickness (T) were measured on the axial images. Length (L) was measured by multiplying the number of slices containing spleen by the slice interval. Ellipsoid volume was calculated as follows: V = W * T * L * π / 6 For the length-estimated SV based on the Bezerra et al model, spleen length was utilized as shown: V = (L - 5.8006) / 0.0126 For each of the three methods, percent change in SV was calculated from baseline to week 24. Pearson's correlation coefficient and Bland Altman analysis were implemented for comparison of methods to manual segmentation. Sensitivity and specificity analysis was performed to determine the accuracy of each method to predict response or progression. The manual segmentation volume was significantly correlated with both the ellipsoid method (r(58) = 0.94, p < 0.0001) and the length-estimated method (r(58) = 0.89, p < 0.0001). When assessing percent changes from baseline to week 24 using manual segmentation, 4 of the participants achieved splenic response and 4 progressed with 25% increase in SV. However, analysis using ellipsoid method yielded 3 responding and 2 progressing participants. Finally, analysis with length-estimated volume yielded no responding or progressing participants. This data is also illustrated in Table 1, which shows the sensitivity and specificity results. Figure 1 illustrates Bland-Altman plots, suggesting that ellipsoid method provides a more accurate estimation of the change in SV compared to length-estimated volume. Furthermore, we found that the inaccuracy with length-estimated volume increases with larger spleens (not shown). Change in spleen volume contributes to the primary/ secondary endpoints in large multi-center clinical trials for myelofibrosis, so it is imperative that the methods used to measure SV are consistent across imaging sites. The current standard for assessing SV is the manual segmentation method because it provides the most comprehensive measurement of spleen size however, this process is burdensome, time consuming, and requires specific training. The ellipsoid and length-estimated methods were strongly correlated with the manual segmentation method; however, they were not as sensitive when determining treatment response or progression. The length-estimated method had the least level of agreement with manual segmentation. The ellipsoid method may be a better alternative; however, it is important to use one method consistently across all visits for a study participant. Additional work is required to test performance of methods on a larger cohort, as well as assess automated segmentation algorithms that may reduce the burden of manual tracing. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andreas M. Weng ◽  
Julius F. Heidenreich ◽  
Corona Metz ◽  
Simon Veldhoen ◽  
Thorsten A. Bley ◽  
...  

Abstract Background Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. Methods In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. Results The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. Conclusions Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.


2021 ◽  
pp. 1-9
Author(s):  
Esther Heyde Selke Costa ◽  
Jenifer Faria Krüger ◽  
Carolina Q. Camargo ◽  
Vinícius Basso Preti ◽  
Elaine Hillesheim ◽  
...  

Author(s):  
Philon Nguyen ◽  
Thanh An Nguyen ◽  
Yong Zeng

AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.


2021 ◽  
Author(s):  
Javier C. Urcuyo ◽  
Susan Christine Massey ◽  
Andrea Hawkins-Daarud ◽  
Bianca-Maria Marin ◽  
Danielle M. Burgenske ◽  
...  

AbstractGlioblastoma is the most malignant primary brain tumor with significant heterogeneity and a limited number of effective therapeutic options. Many investigational targeted therapies have failed in clinical trials, but it remains unclear if this results from insensitivity to therapy or poor drug delivery across the blood-brain barrier. Using well-established EGFR-amplified patient-derived xenograft (PDX) cell lines, we investigated this question using an EGFR-directed therapy. With only bioluminescence imaging, we used a mathematical model to quantify the heterogeneous treatment response across the three PDX lines (GBM6, GBM12, GBM39). Our model estimated the primary cause of intracranial treatment response for each of the lines, and these findings were validated with parallel experimental efforts. This mathematical modeling approach can be used as a useful complementary tool that can be widely applied to many more PDX lines. This has the potential to further inform experimental efforts and reduce the cost and time necessary to make experimental conclusions.Author summaryGlioblastoma is a deadly brain cancer that is difficult to treat. New therapies often fail to surpass the current standard of care during clinical trials. This can be attributed to both the vast heterogeneity of the disease and the blood-brain barrier, which may or may not be disrupted in various regions of tumors. Thus, while some cancer cells may develop insensitivity in the presence of a drug due to heterogeneity, other tumor areas are simply not exposed to the drug. Being able to understand to what extent each of these is driving clinical trial results in individuals may be key to advancing novel therapies. To address this challenge, we used mathematical modeling to study the differences between three patient-derived tumors in mice. With our unique approach, we identified the reason for treatment failure in each patient tumor. These results were validated through rigorous and time-consuming experiments, but our mathematical modeling approach allows for a cheaper, quicker, and widely applicable way to come to similar conclusions.


1999 ◽  
Vol 81 (02) ◽  
pp. 264-267 ◽  
Author(s):  
A. Keller ◽  
S. Argirion ◽  
D. L. Heene ◽  
C. E. Dempfle

SummaryIn clinical routine use, fibrinogen is measured by clotting-time methods, or by clot turbidity in photometric prothrombin time determination. For calibration of these assays measurement of total thrombinclottable protein has been recommended. We have now developed a microfiltration assay for total thrombin-clottable protein. Plasma samples were mixed with thrombin in a 96-well microfiltration device. After clot formation, the fluid was extracted by vacuum suction, and fibrin adherent to the filter membranes washed with buffer. Membrane segments with adherent fibrin were recovered from the 96-well manifold with a punch and transferred to tubes containing denaturing buffer solution. After dissolution of fibrin, protein concentration was determined by optical absorption at 280 nm. The microfiltration assay displayed a high correlation with the total clottable protein method (R = 0.95), and fibrinogen antigen (r = 0.96). Correlation with clotting time assays, and PT-derived fibrinogen in 150 clinical plasma samples was in the range of r = 0.84 to r = 0.97. Intraassay and day-to-day variability of the assay was comparable to the conventional total clottable fibrinogen assay. The novel microfiltration assay appears to be well suited for measurement of large series of samples for calibration, screening purposes, and clinical trials.


2020 ◽  
Vol 79 (7) ◽  
pp. 914-919 ◽  
Author(s):  
Gareth T Jones ◽  
Linda E Dean ◽  
Ejaz Pathan ◽  
Rosemary J Hollick ◽  
Gary J Macfarlane

Management guidelines assume that results from clinical trials can be generalised, although seldom is data available to test this assumption. We aimed to determine the proportion of patients commencing tumour necrosis factor inhibition (TNFi) who would have been eligible for relevant clinical trials, and whether treatment response differs between these groups and the trials themselves. The British Society for Rheumatology Biologics Register for Ankylosing Spondylitis (BSRBR-AS) recruited a real-world cohort of TNFi-naïve spondyloarthritis patients with data collection from clinical records and patient questionnaires. Participant characteristics were extracted from trials identified from a recent Health Technology Assessment of TNFi for ankylosing spondylitis/non-radiographic axial spondyloarthritis. Descriptive statistics were used to determine the differences, including treatment response, between BSRBR-AS participants who would/would not have been eligible for the clinical trials and with trial participants. Among 2420 BSRBR-AS participants, those commencing TNFi (34%) had shorter symptom duration (15 vs 22 years) but more active disease (Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) 6.4 vs 4.0; Bath Ankylosing Spondylitis Disease Functional Index (BASFI) 6.2 vs 3.8). Of those commencing TNFi, 41% met eligibility criteria for ≥1 of fourteen relevant trials; they reported higher disease activity (BASDAI 6.9 vs 6.1) and poorer function (BASFI 6.6 vs 6.0). 61.7% of trial participants reported a positive treatment response, vs 51.3% of BSRBR-AS patients (difference: 10.4%; 95% CI 4.4% to 16.5%). Potential eligibility for trials did not influence treatment response (difference 2.0%; -9.4% to 13.4%). Fewer patients in the real world respond to TNFi than is reported in the trial literature. This has important implications for the generalisability of trial results, and the cost-effectiveness of TNFi agents.


2019 ◽  
pp. 1-7 ◽  
Author(s):  
Lorena Fernández de la Cruz ◽  
Jesper Enander ◽  
Christian Rück ◽  
Sabine Wilhelm ◽  
Katharine A. Phillips ◽  
...  

Abstract Background The number of clinical trials in body dysmorphic disorder (BDD) has steadily increased in recent years. As the number of studies grows, it is important to define the most empirically useful definitions for response and remission in order to enhance field-wide consistency and comparisons of treatment outcomes across studies. In this study, we aim to operationally define treatment response and remission in BDD. Method We pooled data from three randomized controlled trials of cognitive-behavior therapy (CBT) for BDD (combined n = 153) conducted at four academic sites in Sweden, the USA, and England. Using signal detection methods, we examined the Yale-Brown Obsessive Compulsive Scale modified for BDD (BDD–YBOCS) score that most reliably identified patients who responded to CBT and those who achieved remission from BDD symptoms at the end of treatment. Results A BDD–YBOCS reduction ⩾30% was most predictive of treatment response as defined by the Clinical Global Impression (CGI) – Improvement scale (sensitivity 0.89, specificity 0.91, 91% correctly classified). At post-treatment, a BDD–YBOCS score ⩽16 was the best predictor of full or partial symptom remission (sensitivity 0.85, specificity 0.99, 97% correctly classified), defined by the CGI – Severity scale. Conclusion Based on these results, we propose conceptual and operational definitions of response and full or partial remission in BDD. A consensus regarding these constructs will improve the interpretation and comparison of future clinical trials, as well as improve communication among researchers, clinicians, and patients. Further research is needed, especially regarding definitions of full remission, recovery, and relapse.


2020 ◽  
Vol 477 ◽  
pp. 112711
Author(s):  
Edouard Lhomme ◽  
Boris P. Hejblum ◽  
Christine Lacabaratz ◽  
Aurélie Wiedemann ◽  
Jean-Daniel Lelièvre ◽  
...  

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