Inter- and intraobserver agreement of antenatal cardiotocography assessments by maternity care professionals: A prospective study.

OBJECTIVE: In the Netherlands, antenatal cardiotocography (aCTG) to assess fetal well-being is performed in obstetrician-led care. An innovative initiative was started to evaluate whether aCTG for specific indications-reduced fetal movements, external cephalic version, or postdate pregnancy-is feasible in non-obstetrician-led care settings by independent primary care midwives. Quality assessment is essential when reorganizing and shifting tasks and responsibilities. Therefore, we aimed to assess the inter- and intraobserver agreement for aCTG assessments between and within four professional groups involved in Dutch maternity care regarding the overall classification and assessment of the various components of aCTG. METHOD: This was a prospective study among 47 Dutch primary care midwives, hospital-based midwives, residents, and obstetricians. Ten aCTG traces were assessed twice at a 1 month interval. To ensure a representative sample, we used two different sets of 10 aCTG traces each. We calculated the degree of agreement using the proportions of agreement. RESULTS: The proportions of agreement for interobserver agreement on the classification of aCTG between and within the four professional groups varied from 0.82 to 0.94. The proportions of agreement for each professional group were slightly higher for intraobserver (0.86-0.94) than for interobserver agreement. For the various aCTG components, the proportions of agreement for interobserver agreement varied from 0.64 (presence of contractions) to 0.98 (baseline heart frequency). CONCLUSION: The proportion of agreement levels between and within the maternity care professionals in the classification of aCTG traces among healthy women were comparable. This means that these professional groups are equally well able to classify aCTGs in healthy pregnant women.

Cultural competency of GP trainees and GP trainers: a cross-sectional survey study.

OBJECTIVE: To assess the cultural competence (CC) of GP trainees and GP trainers.Design and setting: A cross-sectional survey study was conducted at the GP Training Institute of Amsterdam UMC. SUBJECTS: We included 92 GP trainees and 186 GP trainers. MAIN OUTCOME MEASURES: We measured the three domains of cultural competency: 1) knowledge, 2) culturally competent attitudes and 3) culturally competent skills. Regression models were used to identify factors associated with levels of CC. Participants rated their self-perceived CC at the beginning and end of the survey, and the correlation between self-perceived and measured CC was assessed. RESULTS: Approximately 94% of the GP trainees and 81% of the GP trainers scored low on knowledge; 45% and 42%, respectively, scored low on culturally competent attitudes. The level of culturally competent skills was moderate (54.3%) or low (48.4%) for most GP trainees and GP trainers. The year of residency and the GP training institute were significantly associated with one or more (sub-)domains of CC in GP trainees. Having >10% migrant patients and experience as a GP trainer were positively associated with one or more (sub-) domains of cultural competence in GP trainers. The correlation between measured and self-perceived CC was positive overall but very weak (Spearman correlation coefficient ranging from -0.1-0.3). CONCLUSION: The level of cultural competence was low in both groups, especially in the knowledge scores. Cultural competence increased with experience and exposure to an ethnically diverse patient population. Our study highlights the need for cultural competence training in the GP training curricula.

General practitioner (GP) trainees find cross-cultural consultations stressful due to a self-perceived lack of cultural competence (CC). The level of CC in general practice is as yet unknown.On average, the level of CC was low for the majority of GP trainees and GP trainers, especially for the scores on knowledge.CC increased with experience and exposure to an ethnically diverse patient population.GP trainees and trainers perceived a lack of covered education on various topics related to the care of migrants.Our study highlights the need for cultural competence training in the GP training curricula.

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.

Interobserver variability studies in diagnostic imaging: a methodological systematic review.

OBJECTIVES: To review the methodology of interobserver variability studies; including current practice and quality of conducting and reporting studies. METHODS: Interobserver variability studies between January 2019 and January 2020 were included; extracted data comprised of study characteristics, populations, variability measures, key results, and conclusions. Risk of bias was assessed using the COSMIN tool for assessing reliability and measurement error. RESULTS: Seventy-nine full-text studies were included covering various imaging tests and clinical areas. The median number of patients was 47 (IQR:23-88), and observers were 4 (IQR:2-7), with sample size justified in 12 (15%) studies. Most studies used static images (n = 75, 95%), where all observers interpreted images for all patients (n = 67, 85%). Intraclass correlation coefficients (ICC) (n = 41, 52%), Kappa (κ) statistics (n = 31, 39%) and percentage agreement (n = 15, 19%) were most commonly used. Interpretation of variability estimates often did not correspond with study conclusions. The COSMIN risk of bias tool gave a very good/adequate rating for 52 studies (66%) including any studies that used variability measures listed in the tool. For studies using static images, some study design standards were not applicable and did not contribute to the overall rating. CONCLUSIONS: Interobserver variability studies have diverse study designs and methods, the impact of which requires further evaluation. Sample size for patients and observers was often small without justification. Most studies report ICC and κ values, which did not always coincide with the study conclusion. High ratings were assigned to many studies using the COSMIN risk of bias tool, with certain standards scored 'not applicable' when static images were used. ADVANCES IN KNOWLEDGE: The sample size for both patients and observers was often small without justification. For most studies, observers interpreted static images and did not evaluate the process of acquiring the imaging test, meaning it was not possible to assess many COSMIN risk of bias standards for studies with this design. Most studies reported intraclass correlation coefficient and κ statistics; study conclusions often did not correspond with results.

Studies on Reliability and Measurement Error of Measurements in Medicine - From Design to Statistics Explained for Medical Researchers.

Reliability and measurement error are measurement properties that quantify the influence of specific sources of variation, such as raters, type of machine, or time, on the score of the individual measurement. Several designs can be chosen to assess reliability and measurement error of a measurement. Differences in design are due to specific choices about which sources of variation are varied over the repeated measurements in stable patients, which potential sources of variation are kept stable (ie, restricted), and about whether or not the entire measurement instrument (or measurement protocol) was repeated or only part of it. We explain how these choices determine how intraclass correlation coefficients and standard errors of measurement formulas are built for different designs by using Venn diagrams. Strategies for improving the measurement are explained, and recommendations for reporting the essentials of these studies are described. We hope that this paper will facilitate the understanding and improve the design, analysis, and reporting of future studies on reliability and measurement error of measurements.

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma.

We investigated whether the outcome prediction of patients with aggressive B-cell lymphoma can be improved by combining clinical, molecular genotype, and radiomics features. MYC, BCL2, and BCL6 rearrangements were assessed using fluorescence in situ hybridization. Seventeen radiomics features were extracted from the baseline positron emission tomography-computed tomography of 323 patients, which included maximum standardized uptake value (SUVmax), SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and 12 dissemination features pertaining to distance, differences in uptake and volume between lesions, respectively. Logistic regression with backward feature selection was used to predict progression after 2 years. The predictive value of (1) International Prognostic Index (IPI); (2) IPI plus MYC; (3) IPI, MYC, and MTV; (4) radiomics; and (5) MYC plus radiomics models were tested using the cross-validated area under the curve (CV-AUC) and positive predictive values (PPVs). IPI yielded a CV-AUC of 0.65 ± 0.07 with a PPV of 29.6%. The IPI plus MYC model yielded a CV-AUC of 0.68 ± 0.08. IPI, MYC, and MTV yielded a CV-AUC of 0.74 ± 0.08. The highest model performance of the radiomics model was observed for MTV combined with the maximum distance between the largest lesion and another lesion, the maximum difference in SUVpeak between 2 lesions, and the sum of distances between all lesions, yielding an improved CV-AUC of 0.77 ± 0.07. The same radiomics features were retained when adding MYC (CV-AUC, 0.77 ± 0.07). PPV was highest for the MYC plus radiomics model (50.0%) and increased by 20% compared with the IPI (29.6%). Adding radiomics features improved model performance and PPV and can, therefore, aid in identifying poor prognosis patients.

Responsiveness and Minimal Important Change of the PROMIS Pain Interference Item Bank in Patients Presented in Musculoskeletal Practice.

We evaluated the responsiveness of the Patient Reported Outcome Information System Pain Interference item bank in patients with musculoskeletal pain by testing predefined hypotheses about the relationship between the change scores on the item bank, change scores on legacy instruments and Global Ratings of Change (GRoC), and we estimated Minimal Important Change (MIC). Patients answered the full Dutch-Flemish V1.1 item bank. From the responses we derived scores for the standard 8-item short form (SF8a) and a CAT-score was simulated. Correlations between the change scores on the item bank, GRoC and legacy instruments were calculated, together with Effect Sizes, Standardized Response Means, and Area Under the Curve. GRoC were used as an anchor for estimating the MIC with (adjusted) predictive modeling. Of 1,677 patients answering baseline questionnaires 960 completed follow-up questionnaires at 3 months. The item bank correlated moderately high with the GRoC (Spearman's rho 0.63) and with the legacy instruments (Pearson's R ranging from .45 to .68). It showed a high ES (.97) and Standardized Response Means (.71), and could distinguish well between improved and not improved patients based on the GRoC (Area Under the Curve .77). Comparable results were found for the derived SF8a and CAT-scores. The MIC was estimated to be 3.2 (CI 2.6-3.7) T-score points. PERSPECTIVE: Our study supports the responsiveness of the PROMIS-PI item bank in patients with musculoskeletal complaints. Almost all predefined hypotheses were met (94%). The PROMIS-PI item bank correlated well with several legacy instruments which supports generic use of the item bank. MIC for PROMIS-PI was estimated to be 3.2 T-score points.

Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor.

INTRODUCTION: Although visual and quantitative assessments of [18F]FDG PET/CT studies typically rely on liver uptake value as a reference or normalisation factor, consensus or consistency in measuring [18F]FDG uptake is lacking. Therefore, we evaluate the variation of several liver standardised uptake value (SUV) measurements in lymphoma [18F]FDG PET/CT studies using different uptake metrics. METHODS: PET/CT scans from 34 lymphoma patients were used to calculate SUVmaxliver, SUVpeakliver and SUVmeanliver as a function of (1) volume-of-interest (VOI) size, (2) location, (3) imaging time point and (4) as a function of total metabolic tumour volume (MTV). The impact of reconstruction protocol on liver uptake is studied on 15 baseline lymphoma patient scans. The effect of noise on liver SUV was assessed using full and 25% count images of 15 lymphoma scans. RESULTS: Generally, SUVmaxliver and SUVpeakliver were 38% and 16% higher compared to SUVmeanliver. SUVmaxliver and SUVpeakliver increased up to 31% and 15% with VOI size while SUVmeanliver remained unchanged with the lowest variability for the largest VOI size. Liver uptake metrics were not affected by VOI location. Compared to baseline, liver uptake metrics were 15-18% and 9-18% higher at interim and EoT PET, respectively. SUVliver decreased with larger total MTVs. SUVmaxliver and SUVpeakliver were affected by reconstruction protocol up to 62%. SUVmax and SUVpeak moved 22% and 11% upward between full and 25% count images. CONCLUSION: SUVmeanliver was most robust against VOI size, location, reconstruction protocol and image noise level, and is thus the most reproducible metric for liver uptake. The commonly recommended 3 cm diameter spherical VOI-based SUVmeanliver values were only slightly more variable than those seen with larger VOI sizes and are sufficient for SUVmeanliver measurements in future studies. TRIAL REGISTRATION: EudraCT: 2006-005,174-42, 01-08-2008.

Prognostic value of TARC and quantitative PET parameters in relapsed or refractory Hodgkin lymphoma patients treated with brentuximab vedotin and DHAP.

Risk-stratified treatment strategies have the potential to increase survival and lower toxicity in relapsed/refractory classical Hodgkin lymphoma (R/R cHL) patients. This study investigated the prognostic value of serum (s)TARC, vitamin D and lactate dehydrogenase (LDH), TARC immunohistochemistry and quantitative PET parameters in 65 R/R cHL patients who were treated with brentuximab vedotin (BV) and DHAP followed by autologous stem-cell transplantation (ASCT) within the Transplant BRaVE study (NCT02280993). At a median follow-up of 40 months, the 3-year progression free survival (PFS) was 77% (95% CI: 67-88%) and the overall survival was 95% (90-100%). Significant adverse prognostic markers for progression were weak/negative TARC staining of Hodgkin Reed-Sternberg cells in the baseline biopsy, and a high standard uptake value (SUV)mean or SUVpeak on the baseline PET scan. After one cycle of BV-DHAP, sTARC levels were strongly associated with the risk of progression using a cutoff of 500 pg/ml. On the pre-ASCT PET scan, SUVpeak was highly prognostic for progression post-ASCT. Vitamin D, LDH and metabolic tumor volume had low prognostic value. In conclusion, we established the prognostic impact of sTARC, TARC staining, and quantitative PET parameters for R/R cHL, allowing the use of these parameters in prospective risk-stratified clinical trials. Trial registration: NCT02280993.

Blood-circulating EV-miRNAs, serum TARC, and quantitative FDG-PET features in classical Hodgkin lymphoma.

Blood-based biomarkers are gaining interest for response evaluation in classical Hodgkin lymphoma (cHL). However, it is unknown how blood-based biomarkers relate to quantitative 18F-FDG-PET features. We correlated extracellular vesicle-associated miRNAs (EV-miRNA), serum TARC, and complete blood count (CBC) with PET features (e.g., metabolic tumor volume [MTV], dissemination and intensity features) in 30 cHL patients at baseline. EV-miR127-3p, EV-miR24-3p, sTARC, and several CBC parameters showed weak to strong correlations with MTV and dissemination features, but not with intensity features. Two other EV-miRNAs only showed weak correlations with PET features. Therefore, blood-based biomarkers may be complementary to PET features, which warrants further exploration of combining these biomarkers in prognostic models.

External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients.

AIM: Clinical prediction models need to be validated. In this study, we used simulation data to compare various internal and external validation approaches to validate models. METHODS: Data of 500 patients were simulated using distributions of metabolic tumor volume, standardized uptake value, the maximal distance between the largest lesion and another lesion, WHO performance status and age of 296 diffuse large B cell lymphoma patients. These data were used to predict progression after 2 years based on an existing logistic regression model. Using the simulated data, we applied cross-validation, bootstrapping and holdout (n = 100). We simulated new external datasets (n = 100, n = 200, n = 500) and simulated stage-specific external datasets (1), varied the cut-off for high-risk patients (2) and the false positive and false negative rates (3) and simulated a dataset with EARL2 characteristics (4). All internal and external simulations were repeated 100 times. Model performance was expressed as the cross-validated area under the curve (CV-AUC ± SD) and calibration slope. RESULTS: The cross-validation (0.71 ± 0.06) and holdout (0.70 ± 0.07) resulted in comparable model performances, but the model had a higher uncertainty using a holdout set. Bootstrapping resulted in a CV-AUC of 0.67 ± 0.02. The calibration slope was comparable for these internal validation approaches. Increasing the size of the test set resulted in more precise CV-AUC estimates and smaller SD for the calibration slope. For test datasets with different stages, the CV-AUC increased as Ann Arbor stages increased. As expected, changing the cut-off for high risk and false positive- and negative rates influenced the model performance, which is clearly shown by the low calibration slope. The EARL2 dataset resulted in similar model performance and precision, but calibration slope indicated overfitting. CONCLUSION: In case of small datasets, it is not advisable to use a holdout or a very small external dataset with similar characteristics. A single small testing dataset suffers from a large uncertainty. Therefore, repeated CV using the full training dataset is preferred instead. Our simulations also demonstrated that it is important to consider the impact of differences in patient population between training and test data, which may ask for adjustment or stratification of relevant variables.

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.

PURPOSE: Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [18F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years. METHODS: A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. RESULTS: Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. CONCLUSION: Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.

Measurement properties of the Dutch PROMIS-29 v2.1 profile in people with and without chronic conditions.

PURPOSE: To investigate the structural validity, internal consistency, measurement invariance, and construct validity of the Dutch PROMIS-29 v2.1 profile, including seven physical (e.g., pain, physical function), mental (e.g., depression, anxiety), and social (e.g., role functioning) domains of health, in a Dutch general population sample including subsamples with and without chronic diseases. METHODS: The PROMIS-29 was completed by 63,602 participants from the Lifelines cohort study. Structural validity of the PROMIS-29, including unidimensionality of each domain and the physical and mental health summary scores, was evaluated using factor analyses (criteria: CFI ≥ 0.95, TLI ≥ 0.95, RMSEA ≤ 0.06, SRMR ≤ 0.08). Internal consistency, measurement invariance (no differential item functioning (DIF) for age, gender, administration mode, educational level, ethnicity, chronic diseases), and construct validity (hypotheses on known-groups validity and correlations between domains) were assessed per domain. RESULTS: The factor structure of the seven domains was supported (CFI = 0.994, TLI = 0.993, RMSEA = 0.046, SRMR = 0.031) as was unidimensionality of each domain, both in the entire sample and the subsamples. Model fit of the physical and mental health summary scores reached the criteria, and scoring coefficients were obtained. Cronbach's alpha for the seven PROMIS-29 domains ranged from 0.75 to 0.96 in the complete sample. No DIF was detected. Of the predefined hypotheses, 78% could be confirmed. CONCLUSION: Sufficient structural validity, internal consistency and measurement invariance were found, both in the entire sample and in subsamples with and without chronic diseases. Requirements for sufficient evidence for construct validity were (almost) met for most subscales. Future studies should investigate test-retest reliability, measurement error, and responsiveness of the PROMIS-29.

Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index.

PURPOSE: Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model. METHODS: PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times. RESULTS: The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm3. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities. CONCLUSION: A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.

The Forensic High and Intensive Care Monitor: Measurement Properties of a Model Fidelity Scale for Contact-Based Care in Forensic Psychiatry.

Forensic High and Intensive Care (FHIC) has recently been developed as a new care model in Dutch forensic psychiatry. FHIC aims to provide contact-based care. To support Dutch forensic care institutions in the implementation of the model, a model fidelity scale was developed called the FHIC monitor. The aim of this study was to assess the inter-rater reliability, content validity, and construct validity of the FHIC monitor. A multi-methods design was used, combining qualitative and quantitative research. To collect data, audits and focus group meetings were organized to score care at individual wards with the monitor and get feedback from auditors and audit receiving teams about the quality of the monitor. In total, fifteen forensic mental healthcare institutions participated. The instrument showed acceptable inter-rater reliability and content validity, and a significant difference between expected high and low scoring institutions, supporting construct validity. The instrument can be used as a valid instrument to measure the level of implementation of the FHIC model on forensic psychiatric wards in the Netherlands.

Quantitative Radiomics Features in Diffuse Large B-Cell Lymphoma: Does Segmentation Method Matter?

Radiomics features may predict outcome in diffuse large B-cell lymphoma (DLBCL). Currently, multiple segmentation methods are used to calculate metabolic tumor volume (MTV). We assessed the influence of segmentation method on the discriminative power of radiomics features in DLBCL at the patient level and for the largest lesion. Methods: Fifty baseline 18F-FDG PET/CT scans of DLBCL patients with progression or relapse within 2 years after diagnosis were matched on uptake time and reconstruction method with 50 baseline PET/CT scans of DLBCL patients without progression. Scans were analyzed using 6 semiautomatic segmentation methods (SUV threshold of 4.0 [SUV4.0], SUV threshold of 2.5, 41% of SUVmax, 50% of SUVpeak, a majority vote segmenting voxels detected by ≥2 methods, and a majority vote segmenting voxels detected by ≥3 methods). On the basis of these segmentations, 490 radiomics features were extracted at the patient level, and 486 features were extracted for the largest lesion. To quantify the agreement between features extracted from different segmentation methods, the intraclass correlation (ICC) agreement was calculated for each method compared with SUV4.0. The feature space was reduced by deleting features that had high Pearson correlations (≥0.7) with the previously established predictors MTV or SUVpeak Model performance was assessed using stratified repeated cross validation with 5 folds and 2,000 repeats, yielding the mean receiver-operating-characteristics curve integral for all segmentation methods using logistic regression with backward feature selection. Results: The percentage of features yielding an ICC of at least 0.75, compared with the SUV4.0 segmentation, was lowest for 50% of SUVpeak both at the patient level and for the largest lesion, with 77.3% and 66.7% of the features yielding an ICC of at least 0.75, respectively. Features did not correlate strongly with MTV, with at least 435 features at the patient level and 409 features for the largest lesion for all segmentation methods having a correlation coefficient of less than 0.7. Features correlated strongly with SUVpeak (at least 190 at patient level and 134 for the largest lesion were uncorrelated to SUVpeak, respectively). Receiver-operating-characteristics curve integrals ranged between 0.69 ± 0.11 and 0.84 ± 0.09 at the patient level and between 0.69 ± 0.11 and 0.73 ± 0.10 at the lesion level. Conclusion: Even though there are differences in the actual radiomics feature values derived and selected features among segmentation methods, there is no substantial difference in the discriminative power of radiomics features among segmentation methods.

18F-FDG PET Improves Baseline Clinical Predictors of Response in Diffuse Large B-Cell Lymphoma: The HOVON-84 Study.

We aimed to determine the added value of baseline metabolic tumor volume (MTV) and interim PET (I-PET) to the age-adjusted international prognostic index (aaIPI) to predict 2-y progression-free survival (PFS) in diffuse large B-cell lymphoma. Secondary objectives were to investigate optimal I-PET response criteria (using Deauville score [DS] or quantitative change in SUVmax [ΔSUVmax] between baseline and I-PET4 [observational I-PET scans after 4 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone administered in 2-wk intervals with intensified rituximab in the first 4 cycles [R(R)-CHOP14]). Methods: I-PET4 scans in the HOVON-84 (Hemato-Oncologie voor Volwassenen Nederland [Haemato Oncology Foundation for Adults in the Netherlands]) randomized clinical trial (EudraCT 2006-005174-42) were centrally reviewed using DS (cutoff, 4-5). Additionally, ΔSUVmax (prespecified cutoff, 70%) and baseline MTV were measured. Multivariable hazard ratio (HR), positive predictive value (PPV), and negative predictive value (NPV) were obtained for 2-y PFS. Results: In total, 513 I-PET4 scans were reviewed according to DS, and ΔSUVmax and baseline MTV were available for 367 and 296 patients. The NPV of I-PET ranged between 82% and 86% for all PET response criteria. Univariate HR and PPV were better for ΔSUVmax (4.8% and 53%, respectively) than for DS (3.1% and 38%, respectively). aaIPI and ΔSUVmax independently predicted 2-y PFS (HR, 3.2 and 5.0, respectively); adding MTV brought about a slight improvement. Low or low-intermediate aaIPI combined with a ΔSUVmax of more than 70% (37% of patients) yielded an NPV of 93%, and the combination of high-intermediate or high aaIPI and a ΔSUVmax of 70% or less yielded a PPV of 65%. Conclusion: In this study on diffuse large B-cell lymphoma, I-PET after 4 cycles of R(R)-CHOP14 added predictive value to aaIPI for 2-y PFS, and both were independent response biomarkers in a multivariable Cox model. We externally validated that ΔSUVmax outperformed DS in 2-y PFS prediction.

18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma.

PURPOSE: Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. METHODS: Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. RESULTS: The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). CONCLUSION: Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. TRIAL REGISTRATION NUMBER AND DATE: EudraCT: 2006-005,174-42, 01-08-2008.

QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

Pain trajectories and possible predictors of a favourable course of low back pain in patients consulting musculoskeletal physicians in The Netherlands.

BACKGROUND: In The Netherlands, low back pain patients can consult physicians specialized in musculoskeletal (MSK) medicine. Previous studies have reported on the characteristics of patients consulting MSK physicians, and the treatment options used. There are no studies yet reporting on the course of Low Back Pain (LBP) after treatment by musculoskeletal (MSK) physicians in The Netherlands. METHODS: In an observational cohort study MSK physicians recorded data about all low back pain patients presenting for a first consultation. At baseline they recorded age, gender, type and duration of the main complaint, and concomitant complaints. At the end of treatment they recorded the type of treatment and the number of treatment sessions. Patients were recruited to answer questionnaires at baseline, and at 6-weekly intervals during a follow-up period of six months. Patient questionnaires included information about previous medical consumption, together with PROMs measuring the level of pain and functional status. Latent Class Growth Analysis (LCGA) was used to classify patients into different groups according to their pain trajectories. Baseline variables were evaluated as predictors of a favourable trajectory using logistic regression analyses, and treatment variables were evaluated as possible confounders. RESULTS: A total of 1377 patients were recruited, of whom 1117 patients (81%) answered at least one follow-up measurement. LCGA identified three groups of patients with distinct pain trajectories. A first group (N = 226) with high pain levels showed no improvement, a second group (N = 578) with high pain levels showed strong improvement, and a third group (N = 313) with mild pain levels showed moderate improvement. The two groups of patients presenting with high baseline pain scores were compared, and a multivariable model was constructed with possible predictors of a favourable course. Male gender, previous specialist visit, previous pain clinic visit, having work, a shorter duration of the current episode, and a longer time since the complaints first started were predictors of a favourable course. The multivariable model showed a moderate area under the curve (0.68) and a low explained variance (0.09). CONCLUSIONS: In low back pain patients treated by musculoskeletal physicians in The Netherlands three different pain trajectories were identified. Baseline variables were of limited value in predicting a favourable course.