When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling?:An Evaluation of the DETECT Multidimensionality Index

In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.<br/

Phase separation in mixtures of colloids and long ideal polymer coils

Colloidal suspensions with free polymer coils which are larger than the colloidal particles are considered. The polymer-colloid interaction is modeled by an extension of the Asakura-Oosawa model. Phase separation occurs into dilute and dense fluid phases of colloidal particles when polymer is added. The critical density of this transition tends to zero as the size of the polymer coils diverges.Comment: 5 pages, 3 figure

Interfacial tension and nucleation in mixtures of colloids and long ideal polymer coils

Mixtures of ideal polymers with hard spheres whose diameters are smaller than the radius of gyration of the polymer, exhibit extensive immiscibility. The interfacial tension between demixed phases of these mixtures is estimated, as is the barrier to nucleation. The barrier is found to scale linearly with the radius of the polymer, causing it to become large for large polymers. Thus for large polymers nucleation is suppressed and phase separation proceeds via spinodal decomposition, as it does in polymer blends.Comment: 4 pages (v2 includes discussion of the scaling of the interfacial tension along the coexistence curve and its relation to the Ginzburg criterion

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Flory-Huggins theory for athermal mixtures of hard spheres and larger flexible polymers

A simple analytic theory for mixtures of hard spheres and larger polymers with excluded volume interactions is developed. The mixture is shown to exhibit extensive immiscibility. For large polymers with strong excluded volume interactions, the density of monomers at the critical point for demixing decreases as one over the square root of the length of the polymer, while the density of spheres tends to a constant. This is very different to the behaviour of mixtures of hard spheres and ideal polymers, these mixtures although even less miscible than those with polymers with excluded volume interactions, have a much higher polymer density at the critical point of demixing. The theory applies to the complete range of mixtures of spheres with flexible polymers, from those with strong excluded volume interactions to ideal polymers.Comment: 9 pages, 4 figure

Low adherence to recommended use of neoadjuvant chemotherapy for muscle-invasive bladder cancer

Purpose: To evaluate guideline adherence and variation in the recommended use of neoadjuvant chemotherapy (NAC) and the effects of this variation on survival in patients with non-metastatic muscle-invasive bladder cancer (MIBC). Patients and methods: In this nationwide, Netherlands Cancer Registry-based study, we identified 1025 patients newly diagnosed with non-metastatic MIBC between November 2017 and November 2019 who underwent radical cystectomy. Patients with ECOG performance status 0–1 and creatinine clearance ≥ 50 mL/min/1.73 m2 were considered NAC-eligible. Interhospital variation was assessed using case-mix adjusted multilevel analysis. A Cox proportional hazards model was used to evaluate the association between hospital specific probability of using NAC and survival. All analyses were stratified by disease stage (cT2 versus cT3-4a). Results: In total, of 809 NAC-eligible patients, only 34% (n = 277) received NAC. Guideline adherence for NAC in cT2 was 26% versus 55% in cT3-4a disease. Interhospital variation was 7–57% and 31–62%, respectively. A higher hospital specific probability of NAC might be associated with a better survival, but results were not statistically significant (HRcT2 = 0.59, 95% CI 0.33–1.05 and HRcT3-4a = 0.71, 95% CI 0.25–2.04). Conclusion: Guideline adherence regarding NAC use is low and interhospital variation is large, especially for patients with cT2-disease. Although not significant, our data suggest that survival of patients diagnosed in hospitals more inclined to give NAC might be better. Further research is warranted to elucidate the underlying mechanism. As literature clearly shows the potential survival benefit of NAC in patients with cT3-4a disease, better guideline adherence might be pursued.</p

Low adherence to recommended use of neoadjuvant chemotherapy for muscle-invasive bladder cancer

Purpose: To evaluate guideline adherence and variation in the recommended use of neoadjuvant chemotherapy (NAC) and the effects of this variation on survival in patients with non-metastatic muscle-invasive bladder cancer (MIBC). Patients and methods: In this nationwide, Netherlands Cancer Registry-based study, we identified 1025 patients newly diagnosed with non-metastatic MIBC between November 2017 and November 2019 who underwent radical cystectomy. Patients with ECOG performance status 0–1 and creatinine clearance ≥ 50 mL/min/1.73 m2 were considered NAC-eligible. Interhospital variation was assessed using case-mix adjusted multilevel analysis. A Cox proportional hazards model was used to evaluate the association between hospital specific probability of using NAC and survival. All analyses were stratified by disease stage (cT2 versus cT3-4a). Results: In total, of 809 NAC-eligible patients, only 34% (n = 277) received NAC. Guideline adherence for NAC in cT2 was 26% versus 55% in cT3-4a disease. Interhospital variation was 7–57% and 31–62%, respectively. A higher hospital specific probability of NAC might be associated with a better survival, but results were not statistically significant (HRcT2 = 0.59, 95% CI 0.33–1.05 and HRcT3-4a = 0.71, 95% CI 0.25–2.04). Conclusion: Guideline adherence regarding NAC use is low and interhospital variation is large, especially for patients with cT2-disease. Although not significant, our data suggest that survival of patients diagnosed in hospitals more inclined to give NAC might be better. Further research is warranted to elucidate the underlying mechanism. As literature clearly shows the potential survival benefit of NAC in patients with cT3-4a disease, better guideline adherence might be pursued.</p

Value of multiparametric magnetic resonance imaging for local staging of invasive urinary bladder tumours.

Background: Initial tumour staging in bladder cancer mainly relies on the histo-pathological outcome of the transurethral bladder tumour resection (TURBT) and imaging by means of a CT-scan (CT-intravenous urography; CT-IVU). The reported risk of understaging varies from 24-50%. To further improve the the evaluation of depth of invasion of the bladder tumour the application of magnetic resonance imaging (MRI) may be useful. To substantiate the additional value of this imaging modality the present observational study was designed. Study design: This is a prospective observational study to analyse bladder tumour staging with multiparametric magnetic resonance imaging (mpMRI) in patients with a known bladder tumour, who are planned for radical cystectomy. Study population: Patients with an invasive bladder cancer who are planned for radical cystectomy. Intervention: Patients were accrued during their visit to the outpatient department of urology. They underwent routine cystoscopy, laboratory tests (including serum Creatinin) and CT-IVU investigations and subsequently a mpMRI. Main study parameters/endpoints: To demonstrate the value of mpMRI in the initial staging of bladder tumours using radiological bladder tumour stage (T-stage) based on mpMRI and pathological bladder tumour stage based on ‘whole-mount’ histo-pathology after radical cystectomy. Results: Thirty-seven participants with known bladder tumours underwent mpMRI and subsequent cystectomy. After mpMRI 10 participants were diagnosed with non-muscle-invasive bladder cancer (NMIBC) and 27 participants with muscle-invasive bladder cancer (MIBC). In the ‘whole-mount’ pathology results 12 participants had NMIBC and 25 participants had MIBC. We found a sensitivity and specificity of 0.88 en 0.58 respectively, for the evaluation of MIBC. The positive and negative predictive value were 81% and 70% respectively. The diagnostic accuracy of mpMRI to differentiate between NMIBC and MIBC was 78%. Conclusions: We found a sensitivity of 88% and a specificity of 58% for mpMRI to discriminate NMIBC from MIBC