Limits Of Agreement Research Articles

Continuous non-invasive vs. invasive arterial blood pressure monitoring during neuroradiological procedure: a comparative, prospective, monocentric, observational study

BackgroundIn the perioperative setting, the most accurate way to continuously measure arterial blood pressure (ABP) is using an arterial catheter. Surrogate methods such as finger cuff have been developed to allow non-invasive measurements and are increasingly used, but need further evaluation. The aim of this study is to evaluate the accuracy and clinical concordance between two devices for the measurement of ABP during neuroradiological procedure.MethodsThis is a prospective, monocentric, observational study. All consecutive patients undergoing a neuroradiological procedure were eligible. Patients who needed arterial catheter for blood pressure measurement were included. During neuroradiological procedure, ABP (systolic, mean and diatolic blood pressure) was measured with two different technologies: radial artery catheter and Nexfin. Bland-Altman and error grid analyses were performed to evaluate the accuracy and clinical concordance between devices.ResultsFrom March 2022 to November 2022, we included 50 patients, mostly ASA 3 (60%) and required a cerebral embolization (94%) under general anaesthesia (96%). Error grid analysis showed that 99% of non-invasive ABP measures obtained with the Nexfin were located in the risk zone A or B. However, 65.7% of hypertension events and 41% of hypotensive events were respectively not detected by Nexfin. Compared to the artery catheter, a significant relationship was found for SAP (r2 = 0.78) and MAP (r2 = 0.80) with the Nexfin (p < 0.001). Bias and limits of agreement (LOA) were respectively 9.6 mmHg (− 15.6 to 34.8 mmHg) and − 0.8 mmHg (− 17.2 to 15.6 mmHg), for SAP and MAP.ConclusionsNexfin is not strictly interchangeable with artery catheter for ABP measuring. Further studies are needed to define its clinical use during neuroradiological procedure.Trial registrationClinicaltrials.gov, registration number: NCT05283824.

Optimized target delineation procedure for the radiosurgery treatment of ventricular tachycardia: observer-independent accuracy.

Part of the current stereotactic arrythmia radioablation (STAR) workflow is transfer of findings from the electroanatomic mapping (EAM) to computed tomography (CT). Here, we analyzed inter- and intraobserver variation in a modified EAM-CT registration using automatic registration algorithms designed to yield higher robustness. This work is based on data of 10 patients who had previously undergone STAR. Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substatial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. EAM-CT merge consists of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, clinical target volume (CTV) delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. Intraobserver variability: The mean DSC and MHD for 3 CT LVs altogether was 0.92 ± 0.01 and 1.49 ± 0.23 mm. The mean DSC and MHD for 3 CTVs altogether was 0,82 ± 0,06 and 0,71 ± 0,22 mm. Interobserver variability: Segmented CT LVs showed great similarity (mean DSC of 0,91 ± 0,01, MHD of 1,86 ± 0,47 mm). The mean DSC comparing CTVs from both observers was 0,81 ± 0,11 and MHD was 0,87 ± 0,45 mm. The high interobserver similarity of segmented LVs and delineated CTVs confirmed the robustness of the proposed method. Even an inexperienced user can perform a precise EAM-CT merge following workflow instructions.

Diagnostic Efficacy of Dynamic Maneuver in Contrast-Enhanced Computed Tomography Compared With Conventional Contrast-Enhanced Computed Tomography in Imaging the Neck Region.

Introduction Dynamic contrast-enhanced computed tomography (DCE-CT) and conventional contrast-enhanced computed tomography (CE-CT) are widely used to evaluate neck lesions, including lymph node metastases, thyroid nodules, salivary gland tumors, and other soft tissue masses. DCE-CT, which captures multiple phases of contrast enhancement over time, is hypothesized to provide superior diagnostic accuracy compared to the single-phase images obtained by CE-CT due to its ability to offer dynamic information about tissue perfusion, blood volume, and vascular permeability. Methods This retrospective observational diagnostic study included 100 patients who underwent neck imaging, divided equally into DCE-CT and CE-CT groups. Patient demographics (age, gender, body mass index) and lesion characteristics (type, location, size, enhancement pattern, margins) were recorded. Diagnostic performance metrics (sensitivity, specificity, accuracy, positive predictive value, negative predictive value) were evaluated alongside inter-observer variability using the kappa statistic. Clinical impact was assessed based on changes in treatment plans and improvements in patient outcomes. The radiation dose for each modality was documented. Statistical analysis was performed using SPSS software (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY) with chi-square tests for categorical variables and t-tests for continuous variables. Results The study included 58 males and 42 females with a mean age of 55.5 years. A total of 145 lesions were detected: 75 by DCE-CT and 70 by CE-CT. DCE-CT demonstrated higher sensitivity (93.33%) and specificity (96.00%) compared to CE-CT (sensitivity 86.67%, specificity 92.00%). The accuracy of DCE-CT was 94.00% versus 88.00% for CE-CT. Inter-observer agreement was higher for DCE-CT (kappa = 0.85) compared to CE-CT (kappa = 0.80). DCE-CT led to treatment plan changes in 40% of cases and resulted in a 75% improvement in outcomes compared to 25% and 60%, respectively, for CE-CT. The mean radiation dose was slightly higher for DCE-CT (8.5 mSv) compared to CE-CT (7.0 mSv). Conclusion DCE-CT offers superior diagnostic efficacy compared to CE-CT for imaging neck lesions with enhanced sensitivity, specificity, and accuracy. Its ability to capture multiple phases of contrast enhancement allows for detailed lesion characterization and provides crucial quantitative data on tissue perfusion and blood volume. These benefits lead to more frequent improvements in patient outcomes and changes in treatment plans. Despite the slightly higher radiation dose, the diagnostic advantages of DCE-CT outweigh the disadvantages, particularly in complex cases requiring detailed lesion analysis. Further prospective studies are recommended to validate these findings and explore the broader clinical benefits of DCE-CT.

Multicenter Study on Tumor Budding in Lung Squamous Cell Carcinoma: Comparison Between Biopsy and Resection With Interobserver Variability Assessment

Grading lung squamous cell carcinoma (LUSC) is controversial and not universally accepted. The histomorphologic feature of tumor budding (TB) is an established independent prognostic factor in colorectal cancer, and its importance is growing in other solid cancers, making it a candidate for inclusion in tumor grading schemes. We aimed to compare TB between preoperative biopsies and resection specimens in pulmonary squamous cell carcinoma and assess interobserver variability. A retrospective cohort of 249 consecutive patients primarily resected with LUSC in Bern (2000-2013, n = 136) and Lausanne (2005-2020, n = 113) with available preoperative biopsies was analyzed for TB and additional histomorphologic parameters, such as spread through airspaces and desmoplasia, by 2 expert pathologists (M.M., C.N.). Results were correlated with clinicopathologic parameters and survival. In resection specimens, peritumoral budding (PTB) score was low (0-4 buds/0.785 mm2) in 47.6%, intermediate (5-9 buds/0.785 mm2) in 27.4%, and high (≥10 buds/0.785 mm2) in 25% of cases (median bud count, 5; IQR, 0-26). Both the absolute number of buds and TB score were similar when comparing tumor edge and intratumoral zone (P = .192) but significantly different from the score obtained in the biopsy (P < .001). Interobserver variability was moderate, regardless of score location (Cohen kappa, 0.59). The discrepant cases were reassessed, and consensus was reached in all cases with identification of causes of discordance. TB score was significantly associated with stage (P = .002), presence of lymph node (P = .033), and distant metastases (P = .020), without significant correlation with overall survival, tumor size, or pleural invasion. Desmoplasia was significantly associated with higher PTB (P < .001). Spread through airspaces was present in 34% and associated with lower PTB (P < .001). To conclude, despite confirming TB as a reproducible factor in LUSC, we disclose areas of scoring ambiguity. Preoperative biopsy evaluation was insufficient in establishing the final TB score of the resected tumor.

Evaluation of artificial intelligence-assisted morphological analysis for platelet count estimation.

This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer. Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared. There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT. The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images.

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study.

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

Deep learning-based measurement of split glomerular filtration rate with 99mTc-diethylenetriamine pentaacetic acid renal scan

PurposeTo develop a deep learning (DL) model for generating automated regions of interest (ROIs) on 99mTc-diethylenetriamine pentaacetic acid (DTPA) renal scans for glomerular filtration rate (GFR) measurement.MethodsManually-drawn ROIs retrieved from a Picture Archiving and Communications System were used as ground-truth (GT) labels. A two-dimensional U-Net convolutional neural network architecture with multichannel input was trained to generate DL ROIs. The agreement between GFR values from GT and DL ROIs was evaluated using Lin’s concordance correlation coefficient (CCC) and slope coefficients for linear regression analyses. Bias and 95% limits of agreement (LOA) were assessed using Bland-Altman plots.ResultsA total of 24,364 scans (12,822 patients) were included. Excellent concordance between GT and DL GFR was found for left (CCC 0.982, 95% confidence interval [CI] 0.981–0.982; slope 1.004, 95% CI 1.003–1.004), right (CCC 0.969, 95% CI 0.968–0.969; slope 0.954, 95% CI 0.953–0.955) and both kidneys (CCC 0.978, 95% CI 0.978–0.979; slope 0.979, 95% CI 0.978–0.979). Bland-Altman analysis revealed minimal bias between GT and DL GFR, with mean differences of − 0.2 (95% LOA − 4.4–4.0), 1.4 (95% LOA − 3.5–6.3) and 1.2 (95% LOA − 6.5–8.8) mL/min/1.73 m² for left, right and both kidneys, respectively. Notably, 19,960 scans (81.9%) showed an absolute difference in GFR of less than 5 mL/min/1.73 m².ConclusionOur DL model exhibited excellent performance in the generation of ROIs on 99mTc-DTPA renal scans. This automated approach could potentially reduce manual effort and enhance the precision of GFR measurement in clinical practice.

Real-time quantification of laser speckle contrast imaging during intestinal laparoscopic surgery: successful demonstration in a porcine intestinal ischemia model

BackgroundAnastomotic leakage (AL) is a dreaded complication following colorectal cancer surgery, impacting patient outcome and leads to increasing healthcare consumption as well as economic burden. Bowel perfusion is a significant modifiable factor for anastomotic healing and thus crucial for reducing AL.AimsThe study aimed to calculate a cut-off value for quantified laser speckle perfusion units (LSPUs) in order to differentiate between ischemic and well-perfused tissue and to assess inter-observer reliability.MethodsLSCI was performed using a porcine ischemic small bowel loop model with the PerfusiX-Imaging® system. An ischemic area, a well-perfused area, and watershed areas, were selected based on the LSCI colormap. Subsequently, local capillary lactate (LCL) levels were measured. A logarithmic curve estimation tested the correlation between LSPU and LCL levels. A cut-off value for LSPU and lactate was calculated, based on anatomically ischemic and well-perfused tissue. Inter-observer variability analysis was performed with 10 observers.ResultsDirectly after ligation of the mesenteric arteries, differences in LSPU values between ischemic and well-perfused tissue were significant (p < 0.001) and increased significantly throughout all following measurements. LCL levels were significantly different (p < 0.001) at both 60 and 120 min. Logarithmic curve estimation showed an R2 value of 0.56 between LSPU and LCL values. A LSPU cut-off value was determined at 69, with a sensitivity of 0.94 and specificity of 0.87. A LCL cut-off value of 3.8 mmol/L was found, with a sensitivity and specificity of 0.97 and 1.0, respectively. There was no difference in assessment between experienced and unexperienced observers. Cohen’s Kappa values were moderate to good (0.52–0.66).ConclusionReal-time quantification of LSPUs may be a feasible intraoperative method to assess tissue perfusion and a cut-off value could be determined with high sensitivity and specificity. Inter-observer variability was moderate to good, irrespective of prior experience with the technique.

Comparison of axial length and anterior segment parameters of patients with myopia measured using 2 fourier-domain optical coherent biometry devices

BackgroundThis study assessed the agreement of ocular parameters of patients with myopia measured using Colombo intraocular lens (IOL) 2 and IOLMaster 700.MethodsEighty patients (male, 22; average age, 29.14 ± 7.36 years) with myopia (159 eyes) were included in this study in May 2023. The participants’ axial length (AXL), central corneal thickness (CCT), lens thickness (LT), white-to-white distance (WTW), front flat (K1), steep (K2), mean (Km) corneal keratometry, astigmatism (Astig), J0 vector, and J45 vector were measured using the IOLMaster 700 and Colombo IOL 2. The measurements from both devices were compared using the generalized estimating equation, correlation analysis, and Bland-Altman plots.ResultsWith the Colombo IOL 2, lower values for K2 and J0 (odds ratio [OR] = 0.587, p = 0.033; OR = 0.779, p < 0.0001, respectively), and larger values for WTW, Astig, and J45 (OR = 1.277, OR = 1.482, OR = 1.1, all p < 0.0001) were obtained. All ocular measurements by both instruments showed positive correlations, with AXL demonstrating the strongest correlation (r = 0.9996, p < 0.0001). The intraclass correlation coefficients for AXL and CCT measured by both instruments was 0.999 and 0.988 (both p < 0.0001), and Bland-Altman plot showed 95% limits of agreement (LoA) of -0.078 to 0.11 mm and − 9.989 to 13.486 μm, respectively. The maximum absolute 95% LoA for LT, WTW, K1, K2, and J0 were relatively high, achieving 0.829 mm, 0.717 mm, 0.983 D, 0.948 D, and 0.632 D, respectively.ConclusionsIn young patients with myopia, CCT and AXL measurements obtained with the Colombo IOL 2 and IOLMaster 700 were comparable. However, WTW, LT, corneal refractive power, and astigmatism values could not be used interchangeably in clinical practice.

Subglottic dilatation in extremely preterm infants on prolonged mechanical ventilation

BackgroundAirway injuries are reported among preterm infants with bronchopulmonary dysplasia. We hypothesized that prolonged ventilation in preterm infants is associated with subglottic dilatation that can be reliably evaluated by point of care ultrasonography (POCUS). MethodsAll preterm infants (<29-weeks) admitted to the neonatal ICU at the Advent-Health from January-2020 to June-2022 were eligible if they required invasive ventilation for ≤7 days in the first 28 days of life (control) or remained intubated for ≥28 days (prolonged ventilation). Sonography was performed by one technician and all images were reviewed by the pediatric radiologist. The trachea size was measured 3 times by randomly selecting three images. The first 20 scans were also independently reported by a different pediatric radiologist. Intra and inter-observer variability was estimated. Mean trachea size and weight at the time of imaging were compared. ResultsOut of 417 eligible infants; 11 died before 28 days and 163 required ventilation for 8–27 days. Consent missed for 80 infants during COVID-19 pandemic. We enrolled 23 and 28 infants in the control & prolonged ventilation groups, respectively. Inter and intra-observer correlations were 0.83 and 0.97 respectively. Infants in the control group had higher gestation and birth weight. Infants on prolonged ventilation were at higher risk for infections, BPD, longer hospital stay and significant subglottic dilation (4.51 ± 0.04 vs 4.17 ± 0.02 mm, p < 0.01) despite smaller body weight at the time of imaging (884 ± 102 vs 1059 ± 123g, p < 0.01). ConclusionExtremely preterm infants on prolonged ventilation are at risk for sub-glottic dilatation that can be reliably measured by POCUS.

Impact of the adjunction of a short video to an original article for the recognition of newly described tumor entities in pathology: An interventional prospective study.

Merkel cell carcinoma diagnosis is often based on microscopic examination by pathologists. While histopathologic diagnosis primarily hinges on conscious and analytical cognition, the pathologist's decision-making process is also influenced by a rapid "gist" or "gestalt" approach. In this study, using cases of Merkel cell carcinoma as a model, we aim to assess how pathologists' viewing short videos containing conceptual clues and visual aids, in conjunction with reading an original article as a reference, may enhance their diagnostic performance. Sixteen pathologists were included in the present work. After participants had read the original article, their ability to distinguish Merkel cell polyomavirus (MCPyV)+ and MCPyV- Merkel cell carcinoma cases was evaluated on a first preliminary series of 20 cases. Following this test, the participants watched the video and then evaluated a second "experimental" series of 20 independent cases. After reading the original article, for each case, a median number of 12 participants (75%, Q1-Q3: 10-13) classified the specimen in the correct category (92 incorrect answers in the whole series). An important interobserver variability was observed in this setting (Kappa coefficient = 0.465). By contrast, following the video, all cases were correctly classified by most of the participants, with only 12 incorrect answers on the whole series and excellent interobserver reproducibility (Kappa coefficient = 0.846). Our study demonstrated that providing a short video together with an original article may enhance pathologists' performance in diagnosing Merkel cell carcinoma.

Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty

PurposeThe aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery.MethodsTwenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques.ResultsMean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97.ConclusionsThese devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

Inter- and Intra-Observer Variability of the AMADEUS Tool for Osteochondral Lesions of the Talus.

Managing osteochondral cartilage defects (OCDs) of the talus is a common daily challenge in orthopaedics as they predispose patients to further cartilage damage and progression to osteoarthritis. Therefore, the implementation of a reliable tool to quantify the amount of cartilage damage that is present is of the essence. We retrospectively identified 15 adult patients diagnosed with uncontained OCDs of the talus measuring <150 mm2, which were treated arthroscopically with bone marrow stimulation. Five independent assessors evaluated the pre-operative MRI scans with the AMADEUS scoring system (i.e., MR-based pre-operative assessment system) and the intra-/inter-observer variability was then calculated by means of the intraclass correlation coefficients (ICC) and Kappa (κ) statistics, respectively. In addition, the correlation between the mean AMADEUS scores and pre-operative self-reported outcomes as measured by the Manchester-Oxford foot questionnaire (MOxFQ) was assessed. The mean ICC and the κ statistic were 0.82 (95% CI [0.71, 0.94]) and 0.42 (95% CI [0.25, 0.59]). The Pearson correlation coefficient was found to be r = -0.618 (p = 0.014). The AMADEUS tool, which was originally designed to quantify knee osteochondral defect severity prior to cartilage repair surgery, demonstrated good reliability and moderate inter-observer variability for small OCDs of the talar shoulder. Given the strong negative correlation between the AMADEUS tool and pre-operative clinical scores, this tool could be implemented in clinical practise to reliably quantify the extent of the osteochondral defects of the talus.

Proteomic analysis of serum in a population-based cohort did not reveal a biomarker for Modic changes.

Modic changes (MC) are bone marrow lesions of vertebral bones, which can be detected with magnetic resonance imaging (MRI) adjacent to degenerated intervertebral discs. Defined by their appearance on T1 and T2 weighted images, there are three interconvertible types: MC1, MC2, and MC3. The inter-observer variability of the MRI diagnosis is high, therefore a diagnostic serum biomarker complementing the MRI to facilitate diagnosis and follow-up would be of great value. We used a highly sensitive and reproducible proteomics approach: DIA/SWATH-MS to find serum biomarkers in a subset of the Northern Finland Birth Cohort 1966. Separately, we measured a panel of factors involved in inflammation and angiogenesis to confirm some potential biomarkers published before with an ELISA-based method called V-Plex. We found neither an association between the serum concentrations of the proteins detected with DIA/SWATH-MS with the presence of MC, nor a correlation with the size of the MC lesions. We did not find any association between the factors measured with the V-Plex and the presence of MC or their size. Altogether, our study suggests that a robust and generally usable biomarker to facilitate the diagnosis of MC cannot readily be found in serum.

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study

BackgroundThe planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast.MethodsPANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases.ResultsThe median JCI for the arterial and venous phases were 0.50 (range, 0.17–0.64) and 0.41 (range, 0.23–0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38–0.57) on the arterial phase, and 0.52 (range 0.36–0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators.ConclusionsOur results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

Two-tier architecture-based grading for pancreatic cancer outperforms traditional grading.

A novel architecture-based grading system for pancreatic ductal adenocarcinoma (PDAC) is tested against traditional grading. A total of 103 PDAC resections were graded by College of American Pathologists/American Joint Committee on Cancer (CAP/AJCC) guidelines and by a system using an architectural pattern (dispersed larger duct = low grade vs dense smaller duct = high grade). Survival analyses and interobserver variability were assessed. In total, 114 cases from a public data set were used for validation. Median overall survivals were 15 and 36 months for architectural high-grade and low-grade cases, respectively (P<.001). Conversely, CAP/AJCC grading showed no survival difference between well-differentiated and moderately differentiated tumors (P=.545). Architecture-based grading remained prognostically significant for recurrence-free survival (P=.004), but CAP/AJCC grading was not (P=.226). Adjusted for stage and margin status, architectural high-grade PDACs showed a hazard ratio of 2.69 relative to low grade (P<.001) for survival. The validation cohort confirmed prognostic differences in overall (P<.001) and recurrence-free survival (P=.027) for the architecture-based system, outperforming CAP/AJCC grading. Architecture-based grading exhibited a Cohen's ĸ value of 0.710 (substantial agreement), superior to traditional grading (0.488, moderate agreement). Grading PDAC based on architectural pattern results in superior prognostication and reproducibility vs CAP/AJCC grading.

Esophagogastroscopy for predicting endoscopic ultrasonography T-stage by utilizing deep learning methods in esophageal cancer

Endoscopic ultrasonography (EUS) is commonly utilized in preoperative staging of esophageal cancer, however with additional pain and cost as well as adverse events. Meanwhile, the accuracy of EUS is highly depend on the training and practice of operators and not universally available. Different operators would lead to high inter-observer variability. Therefore, it is desirable to explore an alternative way to determine preoperative T stage in esophageal cancer. Whether conventional endoscopy possess the ability to predict EUS T stage has never been investigated yet. In current study, with the assistance of Artificial intelligence, we have developed a deep learning model to predict EUS T stage based on 9,714 images collected from 3,333 patients. ResNet-152 pre-trained on the ImageNet dataset was trained with the appropriate transfer learning and fine-tuning strategies on the conventional endoscopic images and their corresponding labels (e.g., T1, T2, T3, T4 and Normal). Meanwhile, augmentation strategies including rotation and flipping were performed to increase the number of images to improve the prediction accuracy. Finally, 4,382 T1, 243 T2, 3,985 T3, 1,102 T4, 14,302 controls images were obtained and split into training dataset, validation dataset and independent testing dataset with the ratio of 4:1:1. Our model could achieve a satisfied performance with an area under the receiver-operating curve (AUC) were 0.9767, 0.9637, 0.9597 and 0.9442 for T1, T2, T3 and T4, respectively in independent testing dataset. In conclusion, conventional gastroscopy combined with artificial intelligence have the great potential to predict EUS T stage.

Agreement Between the National Cancer Institute's diet history questionnaire II and III in a preconception cohort.

Food frequency questionnaires require updating over time, due to population changes in diet, posing analytical challenges in consistently measuring diet in prospective studies. We compared reliability and agreement between nutrients in two versions of the National Cancer Institute's web-based Diet History Questionnaire (DHQ, III vs. II) in an ongoing North American preconception study. We invited 51 consecutively-enrolled U.S. female participants aged 21-45 years to complete both DHQ versions within a 2-week period, in a randomized order. We compared 30 nutrients from both DHQ versions and calculated within-person reliability using intraclass correlation coefficients (ICCs). Bland-Altman plots and 95% limits of agreement (LOA) were generated to assess nutrient agreement between DHQ versions. We observed highest reliability in percent energy from carbohydrates and cholesterol (ICCs: 0.88; 95% CI: 0.80-0.93) and lowest for percent energy from protein and vitamin D (ICCs: 0.56; 95% CI: 0.34-0.72). At the group level, all nutrients had most observations within the LOA. Bland-Altman plots showed assessment differences between DHQs for protein, fat, monounsaturated fat, and vitamin D. The remaining nutrients showed good agreement and good-to-moderate reliability. Some nutrients may require adjustment and calibration analysis before using them interchangeably across DHQ versions.

Evaluation of the performance of digital x-ray systems in pelvis radiography

The aim of this study was to investigate the performance of eight digital radiography systems and to optimise the dose-image quality relationship for digital pelvis radiography. The study involved eight digital radiography systems used for general examinations at Vilnius University Hospital Santaros Klinikos. An anthropomorphic pelvic phantom (CIRS, US) was used to simulate a patient undergoing clinical pelvis radiography. Dose quantities entrance surface dose, dose area product (DAP) and exposure parameters (kVp, mA, mAs) were measured and the effects on the images were evaluated, considering physical contrast to noise ratio (CNR) and observer-based evaluations as image quality metrics. Increasing the tube voltage by 5 kVp from standard protocol led to a reduction in radiation dose (DAP) by 12%–20% with a slight impact on image quality (CNR decreases by 2%–10%). There was an inter-observer variability in image rating across different equipment (kappa value between 0 and 0.3); however, both observers agreed that increasing kVp up to 85–90 kV had no effect on perceived image quality. The results indicate that optimisation strategies should be tailored specifically for each x-ray system since significant performance differences and wide variations in radiation dose exist across various digital radiography systems used in clinical settings. The use of high kVp can be used for dose optimisation in digital pelvis radiography without compromising image diagnostic accuracy.