Reducing Interobserver Variability Research Articles

The Aberrant Expression of Biomarkers and Risk Prediction for Neoplastic Changes in Barrett's Esophagus-Dysplasia.

Background: Barrett's esophagus (BE) is a pre-neoplastic condition associated with an increased risk of esophageal adenocarcinoma (EAC). The accurate diagnosis of BE and grading of dysplasia can help to optimize the management of patients with BE. However, BE may be missed and the accurate grading of dysplasia based on a routine histology has a considerable intra- and interobserver variability. Thus, well-defined biomarker testing remains indispensable. The aim of our study was to identify routinely applicable and relatively specific biomarkers for an accurate diagnosis of BE, as well as determining biomarkers to predict the risk of progression in BE-dysplasia. Methods: Retrospectively, we performed immunohistochemistry to test mucin 2(MUC2), trefoil factor 3 (TFF3), p53, p16, cyclin D1, Ki-67, beta-catenin, and minichromosome maintenance (MCM2) in biopsies. Prospectively, to identify chromosomal alterations, we conducted fluorescent in situ hybridization testing on fresh brush samples collected at the time of endoscopy surveillance. Results: We discovered that MUC2 and TFF3 are specific markers for the diagnosis of BE. Aberrant expression, including the loss and strong overexpression of p53, Ki-67, p16, beta-catenin, cyclin D1, and MCM2, was significantly associated with low-grade dysplasia (LGD), high-grade dysplasia (HGD), and EAC histology, with a relatively high risk of neoplastic changes. Furthermore, the aberrant expressions of p53 and p16 in BE-indefinite dysplasia (IND) progressor cohorts predicted the risk of progression. Conclusions: Assessing the biomarkers would be a suitable adjunct to accurate BE histology diagnoses and improve the accuracy of BE-dysplasia grading, thus reducing interobserver variability, particularly of LGD and risk prediction.

A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ at risk (OAR) in high-dose-rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC). On two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt-nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three-fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU). The Prompt-nnUnet included two forms of parameters Predict-Prompt (PP) and Label-Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96±0.02, 0.91±0.02, and 0.83±0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73±0.95, 8.18±4.84, and 2.11±0.50, respectively. The mean HD95% values (mm) were 1.66±1.11, 3.07±0.94, and 1.35±0.55, respectively. The mean IoUs were 0.92±0.04, 0.84±0.03, and 0.71±0.09, respectively. A delineation time<2.35s per structure in the new model was observed, which was available to save clinician time. The Prompt-nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time.

The Relationship between the Contouring Time of the Metal Artifacts Area and Metal Artifacts in Head and Neck Radiotherapy.

(1) Background: The impacts of metal artifacts (MAs) on the contouring workload for head and neck radiotherapy have not yet been clarified. Therefore, this study evaluated the relationship between the contouring time of the MAs area and MAs on head and neck radiotherapy treatment planning. (2) Methods: We used treatment planning computed tomography (CT) images for head and neck radiotherapy. MAs were classified into three severities by the percentage of CT images containing MAs: mild (<25%), moderate (25−75%), and severe (>75%). We randomly selected nine patients to evaluate the relationship between MAs and the contouring time of the MAs area. (3) Results: The contouring time of MAs showed moderate positive correlations with the MAs volume and the number of CT images containing MAs. Interobserver reliability of the extracted MAs volume and contouring time were excellent and poor, respectively. (4) Conclusions: Our study suggests that the contouring time of MAs areas is related to individual commitment rather than clinical experience. Therefore, the development of software combining metal artifact reduction methods with automatic contouring methods is necessary to reducing interobserver variability and contouring workload.

Implementation of a Commercial Deep Learning-Based Auto Segmentation Software in Radiotherapy: Evaluation of Effectiveness and Impact on Workflow.

Proper delineation of both target volumes and organs at risk is a crucial step in the radiation therapy workflow. This process is normally carried out manually by medical doctors, hence demanding timewise. To improve efficiency, auto-contouring methods have been proposed. We assessed a specific commercial software to investigate its impact on the radiotherapy workflow on four specific disease sites: head and neck, prostate, breast, and rectum. For the present study, we used a commercial deep learning-based auto-segmentation software, namely Limbus Contour (LC), Version 1.5.0 (Limbus AI Inc., Regina, SK, Canada). The software uses deep convolutional neural network models based on a U-net architecture, specific for each structure. Manual and automatic segmentation were compared on disease-specific organs at risk. Contouring time, geometrical performance (volume variation, Dice Similarity Coefficient-DSC, and center of mass shift), and dosimetric impact (DVH differences) were evaluated. With respect to time savings, the maximum advantage was seen in the setting of head and neck cancer with a 65%-time reduction. The average DSC was 0.72. The best agreement was found for lungs. Good results were highlighted for bladder, heart, and femoral heads. The most relevant dosimetric difference was in the rectal cancer case, where the mean volume covered by the 45 Gy isodose was 10.4 cm3 for manual contouring and 289.4 cm3 for automatic segmentation. Automatic contouring was able to significantly reduce the time required in the procedure, simplifying the workflow, and reducing interobserver variability. Its implementation was able to improve the radiation therapy workflow in our department.

The Augmented Hematologist: Human-AI Feedback Loops to Assist Differential Cell Count during the Analysis of Bone Marrow Aspirates

Introduction Every day, thousands of hematologists worldwide analyze bone marrow aspirate (BMA) samples supported by an optical microscope and their own clinical expertise. A crucial part of the BMA analysis, the differential cell count (DCC), is still a time-consuming task that wastes professionals’ energy and time, and its results are subject to interobserver variability (Fuentes-Arderiu 2009). Significant advances have been made to date to assist professionals during the DCC process and to increase their efficiency by leveraging artificial intelligence. However, these tools have not been widely implemented in real clinical environments due to the complexity and high price of the devices that allow the digitization of BMA samples (Chandradevan 2020, Jin 2020). We aimed to develop and evaluate a simple and integrated digital system (see Figure) to cover the entire process, from sample digitization to DCC facilitated by human-artificial intelligence (AI) interaction, by using a 3D printed microscopy adapter arm, a smartphone, a mobile application, and a web-based telemedicine platform. Methods First, a hematologist or a laboratory technician digitizes 20 microscopy fields (x100 objective) using the 3D-printed device that allows coupling a mobile phone and aligning its camera with a conventional optical microscope's ocular lens, and a mobile app customized specifically for fast, standardized, and easy digitization of BMA microscopy images (Dacal 2021). Once the images have been uploaded to the cloud, hematologists can visualize the samples by scrolling and zooming the images and label the different types of cells. This annotations were used to train the AI algorithm. Full traceability of the system allows multiple users to label the same image and also review others’ analyses. Additionally, and once the AI algorithm has been developed, as the image samples arrive in the cloud system, an it automatically analyzes the images by detecting nucleated cells and classifying them into the 6 cell lineages (Myeloid, Erythroid, Monocytic, Lymphoid, Blasts, and Plasma cells). This process runs in a few seconds after image upload, so when hematologists review the image, the virtual AI annotations on the image sample are available and ready to be edited, confirmed, or deleted to obtain a satisfactory final DCC. To evaluate the system's impact on the DCC process, 16 BMA different from those used to train the algorithm were digitized using the 3D-printed adapter, and 4 hematologists remotely analyzed and classified at least 500 cells per BMA sample using the web platform. Each BMA sample was analyzed by 1 hematologist in a blind fashion (without AI support) and by 3 hematologists with the AI algorithm support. The selection of hematologists who were assisted by the AI and those who performed the blinded analysis was done on a rotational basis. Time to perform DCC (500 cells) and interobserver variability by cell lineage were also measured in both groups. This system was supported by a two-stage deep learning algorithm that was created to detect and classify hematopoietic stem cells in 6 lineages from BMA images. A total of 22446 cells from 100 BMA samples were used to train the algorithm. AI algorithm performance was calculated by comparing its lineage prediction and the consensus label (majority vote) among 4 experts for each cell (N=4401 cells). Results The average performance of the AI algorithm for classifying cells in BMA samples was 92.52% (95%CI 90.92-93.62) (98.25% for myeloid, 92.43% for erythroid, 82.60% for blasts, 58% for plasma cells, 86.82% for lymphoid and 67.63% for monocytic class). The interobserver agreement significantly increased when BMA analyses were performed with AI support, increasing from 92.27% (95%CI 90.92-93.62) when hematologists analyzed the samples without AI assistance to 94.11% (95%CI 93.28-94.93) when they were supported by AI. The time to perform a DCC (on 500 cells) decreased when AI was used, from 26.4 minutes in the blinded group to 21.8 minutes for the AI-supported experts. Conclusions Thanks to mobile technologies, any laboratory in the world can easily digitize and analyze BMA samples. AI can support hematologists when analyzing BMA samples increasing efficiency and reducing interobserver variability. Systems designed to augment hematologists’ quantitative and analytics capabilities require interacting with AI in an intuitive and user-friendly way. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation.

e12543 Background: Human epidermal growth factor receptor 2 (HER2) expression is a predictive marker for HER2-targeted therapy in breast cancer patients. Interobserver variation in the interpretation of HER2 levels exists among pathologists, thus a method to increase the consistency of evaluation is needed. This study aimed to evaluate the performance of the artificial intelligence (AI)-based Lunit SCOPE HER2 in assisting pathologists to evaluate HER2 expression levels in breast cancer. Methods: Lunit SCOPE HER2 was developed with a 1.04 x 1010 μm2 area and 7.31 x 105 tumor cells from 1,133 HER2 immunohistochemistry stained whole-slide images (WSI) of breast cancer, annotated by 113 board-certified pathologists. The AI model was developed based on a semantic segmentation algorithm, which consists of two atrous spatial pyramid pooling blocks for tissue level classification and for tumor cell level classification. To validate the model, a total of 209 HER2 WSIs diagnosed with breast cancer were obtained from Kyung Hee University Hospital in Korea and were assigned as an external validation set. Three board-certified pathologists evaluated slide level HER2 expression (3+, 2+, 1+, and 0) twice, first without AI assistance and second, with it. The second reading was performed for WSIs where the pathologist's reading showed discrepancy with the AI model. Results: In the external validation set, all pathologists scored the same HER2 grade in 103 WSIs (49.3%), and the Fleiss kappa value was 0.512. The HER2 grade from the AI model and pathologists was the same in 151 WSIs (72.2%), and the weighted kappa value was 0.844. The pathologists re-evaluate 43, 63, and 83 WSIs, respectively. After AI assistance, all pathologists scored the same HER2 grade in 156 WSIs (74.6%), and the Fleiss kappa value increased to 0.762 (Table). Conclusions: This study demonstrates that an AI-powered HER2 analyzer can help achieve consistent HER2 expression level evaluation in breast cancer by reducing interobserver variability. Thus, the AI model can be applied as an assistance tool for pathologists in HER2 grade evaluation.[Table: see text]

Automatic SCOring of Atopic Dermatitis Using Deep Learning: A Pilot Study

Atopic dermatitis (AD) is a chronic, itchy skin condition that affects 15–20% of children but may occur at any age. It is estimated that 16.5 million US adults (7.3%) have AD that initially began at age >2 years, with nearly 40% affected by moderate or severe disease. Therefore, a quantitative measurement that tracks the evolution of AD severity could be extremely useful in assessing patient evolution and therapeutic efficacy. Currently, SCOring Atopic Dermatitis (SCORAD) is the most frequently used measurement tool in clinical practice. However, SCORAD has the following disadvantages: (i) time consuming—calculating SCORAD usually takes about 7–10 minutes per patient, which poses a heavy burden on dermatologists and (ii) inconsistency—owing to the complexity of SCORAD calculation, even well-trained dermatologists could give different scores for the same case. In this study, we introduce the Automatic SCORAD, an automatic version of the SCORAD that deploys state-of-the-art convolutional neural networks that measure AD severity by analyzing skin lesion images. Overall, we have shown that Automatic SCORAD may prove to be a rapid and objective alternative method for the automatic assessment of AD, achieving results comparable with those of human expert assessment while reducing interobserver variability.

Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification.

We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE). Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's κ statistics were calculated. In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the US-mode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001). AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability.

Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm.

Increasing incidence of skin cancer combined with a shortage of dermatopathologists has increased the workload of pathology departments worldwide. In addition, the high intraobserver and interobserver variability in the assessment of melanocytic skin lesions can result in underestimated or overestimated diagnosis of melanoma. Thus, the development of new techniques for skin tumor diagnosis is essential to assist pathologists to standardize diagnoses and plan accurate patient treatment. Here, we describe the development of an artificial intelligence (AI) system that recognizes cutaneous melanoma from histopathological digitalized slides with clinically acceptable accuracy. Whole-slide digital images from 100 formalin-fixed paraffin-embedded primary cutaneous melanoma were used to train a convolutional neural network (CNN) based on a pretrained Inception-ResNet-v2 to accurately and automatically differentiate tumoral areas from healthy tissue. The CNN was trained by using 60 digital slides in which regions of interest (ROIs) of tumoral and healthy tissue were extracted by experienced dermatopathologists, while the other 40 slides were used as test datasets. A total of 1377 patches of healthy tissue and 2141 patches of melanoma were assessed in the training/validation set, while 791 patches of healthy tissue and 1122 patches of pathological tissue were evaluated in the test dataset. Considering the classification by expert dermatopathologists as reference, the trained deep net showed high accuracy (96.5%), sensitivity (95.7%), specificity (97.7%), F1 score (96.5%), and a Cohen’s kappa of 0.929. Our data show that a deep learning system can be trained to recognize melanoma samples, achieving accuracies comparable to experienced dermatopathologists. Such an approach can offer a valuable aid in improving diagnostic efficiency when expert consultation is not available, as well as reducing interobserver variability. Further studies in larger data sets are necessary to verify whether the deep learning algorithm allows subclassification of different melanoma subtypes.

Evaluation of interobserver variability in application of the new neonatal seizure classification proposed by the ILAE Task Force

ObjectiveClinical identification of neonatal seizures (NS) remains challenging. The International League Against Epilepsy (ILAE) Task Force on Neonatal Seizures has proposed a new classification of NS, based on the 2017 ILAE seizure classification. One of the key points of this proposed NS classification is that seizure types should be determined by the “predominant” clinical feature. However, when the definition of “predominant” is uncertain, interobserver variability may arise. MethodsWe asked 49 health professionals to classify 21 NS video-electroencephalogram (EEG) recordings using the proposed 9 seizure types. ResultsThe degree of agreement among participants was low, and agreement was weak among experts in neonatal neurology. Among experts, the rate of agreement was <50% for 2 NS. This disagreement was related to differences in the interpretation of “predominant features.” Although interobserver variability was present among users of the new NS classification, the reproducibility of the NS classification was satisfactory. ConclusionEducation designed to foster consistent application of the standards for NS will be important for reducing interobserver variability and expanding the use of the new NS classification.

Deciding Thrombolysis in AIS Based on Automated versus on WhatsApp Interpreted ASPECTS, a Reliability and Cost-Effectiveness Analysis in Developing System of Care.

Background: Automated ASPECTS has the potential of reducing interobserver variability in the determination of early ischemic changes. We aimed to assess the performance of an automated ASPECTS vs. ASPECTS interpreted for sent CT images on WhatsApp and to correlate these results with the outcome.Materials and Methods: Patients with anterior circulation stroke who had baseline NCCT and underwent successful IV-thrombolysis were included. NCCT-ASPECTS was assessed by two neuroradiologists, and discrepancies were resolved by agreement. Two groups of patients were included; group 1, where treatment was decided after an automated ASPECTS interpretation that was provided by RAPID software, and group 2, where patients received IV-tPA after an assessment of CT images sent on WhatsApp.Results: A total of 122 patients were included: 36 in group 1 and 86 in group 2. In group 2, the interobserver agreement for NCCT ASPECTS was moderate (κ = 0.36), as was the dichotomized data (κ = 0.44). IOA, however, improved (to κ = 0.57 and κ = 0.64) when the same CT images were interpreted on a workstation. In group 1, Automated ASPECTS showed excellent agreement (κ = 0.80) with agreement reads for workstation images. There were significantly (P < 0.001) increased odds of functional independence and fewer hemorrhagic complications with thrombolyzed patients in group 1.Conclusions: Automated ASPECTS provided by the RAPID@IschemaView ASPECTS performs at a level equal to the agreement read of expert neuroradiologists, and this performance was severely degraded when WhatsApp captured CT images used for ASPECTS assessment. In our study, we found that automated ASPECTS might predict outcomes after IV thrombolysis.

P2.11-16 Variability in Reading Low-Dose Chest CT: Individual Readers vs. Central Review in a Nationwide Lung Cancer Screening Project

Variability in interpretation among readers or institutions is an important issue in the nationwide lung cancer screening. The potential of computer-aided detection and volumetry in reducing interobserver variability has rarely been investigated in the actual screening situation. This study aimed to evaluate the variability among experts in a nationwide lung cancer screening project. We evaluated 1647 consecutive baseline screening CT scans obtained during one month period of December 2017 from a nationwide lung cancer screening project in Korea (K-LUCAS) in which 14 institutions participated. One of 20 chest radiologists in each institution assessed CT scans using a thin-client system equipped with semi-automated nodule segmentation and computer-aided detection software based on Lung-RADS (institutional reading). One chest radiologist retrospectively reviewed all these CT scans while minimizing modification of segmentation results and rejection of tiny nodules (central review). Reading results between institutional reading and central review were compared. Per-case and per-nodule positive rates of central review using Lung-RADS and NELSON criteria were also compared. Per-case positive rate was significantly higher in central review (24.9% [410/1647; 11.1-32.7% across institutions] vs. 19.3% [319/1647; 5.6-30.0% across institutions]; P<.001), while per-nodule positive rate was significantly higher in institutional reading (10.2% [511/4497; 5.2-21.9%] vs. 19.9% [382/1920; 9.5-60.6%]; P<.001). The number of detected nodules was significantly larger in central review (3.04 vs. 1.17 nodule/case; P<.001), while the size of detected nodules was significantly smaller in central review (4.0 vs. 5.1 mm; P<.001). Variability in positive rates among institutions was significantly lower in central review (coefficient of variability, 21.9% vs. 40.2%; P=.044). Manual measurements after rejecting result of semi-automated segmentation occurred in 1.6% (80/5008) of nodules at central review and in 17.8% (342/1920) nodules at institutional reading. Positive rate with Lung-RADS was significantly higher compared with that of NELSON criteria (24.9% vs. 3.9%; P<.001), however, including indeterminate scan by NELSON criteria which requires additional scanning, positive rate was higher in NELSON criteria (24.9% vs. 33.4%; P<.001). Even in a situation where computerized tools are adopted, considerable variability was observed in interpretation of lung cancer screening CT even among expert chest radiologists. The variability was mainly caused by discarding tiny nodules and modifying or rejecting segmentations results, and reduced by central review. The NELSON criteria did not reduce the number of additional scanning in nodule management compared with Lung-RADS. The value of reducing variability by applying stricter rules should be further investigated.

Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis

Evaluation of endoscopic disease activity for patients with ulcerative colitis (UC) is important when determining the treatment of choice. However, endoscopists require a certain period of training to evaluate the activity of inflammation properly, and interobserver variability exists. Therefore, we constructed acomputer-assisted diagnosis (CAD) system using a convolutional neural network (CNN) and evaluated its performance using a large dataset of endoscopic images from patients with UC. A CNN-based CAD system was constructed based on GoogLeNet architecture. The CNN was trained using 26,304 colonoscopy images from a cumulative total of 841 patients with UC, which were tagged with anatomic locations andMayo endoscopic scores. The performance of the CNN in identifying normal mucosa (Mayo 0) and mucosal healing state (Mayo 0-1) was evaluated in an independent test set of 3981 images from 114 patients with UC, by calculating the areas under the receiver operating characteristic curves (AUROCs). In addition, AUROCs in the right side of the colon, left side of the colon, and rectum were evaluated. The CNN-based CAD system showed a high level of performance with AUROCs of 0.86 and 0.98 to identify Mayo 0 and 0-1, respectively. The performance of the CNN was better for the rectum than for the right side and left side of the colon when identifying Mayo 0 (AUROC= 0.92, 0.83, and 0.83, respectively). The performance of the CNN-based CAD system was robust when used to identify endoscopic inflammation severity in patients with UC, highlighting its promising role in supporting less-experienced endoscopists and reducing interobserver variability.

Phosphorylated Histone H3 (PHH3) as a Novel Cell Proliferation Marker and Prognosticator for Meningeal Tumors: A Short Review.

Cell proliferation markers are used in grading of brain tumors and in patient stratification for different treatments. Therefore, defining simple and accurate proliferation markers are of essential importance. Ki67 labeling index is widely studied, yet it is subject to interlaboratory variability. Ki67 protein is expressed on all cell cycle phases except G0 and does not precisely reflect proliferating cells. Counting mitotic figures (MFs) is used in World Health Organization grading of brain tumors (meningiomas), but the detection of MFs on conventional hematoxylin and eosin stained slides is very time consuming. Further, karyorrhexis and pyknosis in apoptotic or crushed cells may mimic mitotic morphology and falsify counting results. Histone H3 phosphorylation on serine-10 is specific to mitosis and phosphorylated histone H3 (PHH3) proliferation markers (as counts defined per area or as indices defined per cell numbers) are increasingly being used to evaluate proliferation in various tumors. Herein, we review current data on PHH3 proliferation markers in meningeal tumors. PHH3-staining highlights mitotic cells and makes easier of rapid grading by driving pathologist's attention on the most mitotically active areas. Thereby, it would function more sensitive in detecting MFs that might be otherwise overloked and more precise by reducing interobserver variability through allowing the pathologist to analyze if the stained nuclei exhibit morphologic features of mitosis.

Fractional Flow Reserve Derived From Computed Tomographic Angiography in Patients With Multivessel CAD

BackgroundThe functional SYNTAX score (FSS) has been shown to improve the discrimination for major adverse cardiac events compared with the anatomic SYNTAX score (SS) while reducing interobserver variability. However, evidence supporting the noninvasive FSS in patients with multivessel coronary artery disease (CAD) is scarce. ObjectivesThe purpose of this study was to assess the feasibility of and validate the noninvasive FSS derived from coronary computed tomography angiography (CTA) with fractional flow reserve (FFRCT) in patients with 3-vessel CAD. MethodsThe CTA-SS was calculated in patients with 3-vessel CAD included in the SYNTAX II (SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery II) study. The noninvasive FSS was determined by including only ischemia-producing lesions (FFRCT ≤0.80). SS derived from different imaging modalities were compared using the Bland-Altman and Passing-Bablok method, and the agreement on the SS tertiles was investigated with Cohen’s Kappa. The risk reclassification was compared between the noninvasive and invasive physiological assessment, and the diagnostic accuracy of FFRCT was assessed by the area under the receiver-operating characteristic curve using instantaneous wave-free ratio as a reference. ResultsThe CTA-SS was feasible in 86% of patients (66 of 77), whereas the noninvasive FSS was feasible in 80% (53 of 66). The anatomic SS was overestimated by CTA compared with conventional angiography (27.6 ± 6.4 vs. 25.3 ± 6.9; p < 0.0001) whereas the calculation of the FSS yielded similar results between the noninvasive and invasive imaging modalities (21.6 ± 7.8 vs. 21.2 ± 8.8; p = 0.589). The noninvasive FSS reclassified 30% of patients from the high- and intermediate-SS tertiles to the low-risk tertile, whereas invasive FSS reclassified 23% of patients from the high- and intermediate-SS tertiles to the low-risk tertile. The agreement on the classic SS tertiles based on Kappa statistics was slight for the anatomic SS (Kappa = 0.19) and fair for the FSS (Kappa = 0.32). The diagnostic accuracy of FFRCT to detect functional significant stenosis based on an instantaneous wave-free ratio ≤0.89 revealed an area under the receiver-operating characteristics curve of 0.85 (95% CI: 0.79 to 0.90) with a sensitivity of 95% (95% CI: 89% to 98%), specificity of 61% (95% CI: 48% to 73%), positive predictive value of 81% (95% CI: 76% to 86%), and negative predictive value of 87% (95% CI: 74% to 94%). ConclusionsCalculation of the noninvasive FSS is feasible and yielded similar results to those obtained with invasive pressure-wire assessment. The agreement on the SYNTAX score tertile classification improved with the inclusion of the functional component from slight to fair agreement. FFRCT has good accuracy in detecting functionally significant lesions in patients with 3-vessel CAD. (A Trial to Evaluate a New Strategy in the Functional Assessment of 3-Vessel Disease Using SYNTAX II Score in Patients Treated With PCI; NCT02015832)

FRACTIONAL FLOW RESERVE DERIVED FROM COMPUTED TOMOGRAPHY ANGIOGRAPHY IN PATIENTS WITH COMPLEX CORONARY ARTERY DISEASE: FEASIBILITY AND VALIDATION OF THE NON-INVASIVE FUNCTIONAL SYNTAX SCORE

The functional SYNTAX score (FSS) has been shown to improve the discrimination for major adverse cardiac events compared to the anatomic SS while reducing interobserver variability. However, evidence supporting the non-invasive FSS in patients with complex coronary artery disease is scarce. The

Penile vascular diagnostic categorization using penile duplex Doppler ultrasound: Differences in vascular hemodynamics parameters by differences in anatomic sampling location.

In 2013 the International Society for Sexual Medicine(ISSM) published the guidelines regarding the standard operating procedure (SOP) for penile duplex Doppler ultrasound (PDDU). Although ISSM-SOP have given important strides in reducing interobserver variability in PDDU by procedural protocol and parameters these guidelines do not address the anatomic location along the penis at which hemodynamic measurements have to be done. In our opinion a "double sampling" may be interesting to detect the arteriogenic or venogenic nature of the erectile dysfunction (ED). In particular sampling measurements at the "crus" (at the level of the peno-scrotal junction) may be significative for detection of veno-occlusive dysfunction (VOD),whereas an evaluation at "mid penis" (1/2 distance between peno-scrotal junction and coronal sulcus), may be useful to diagnose an arterial insufficiency (AI). We evalued 90 men, mean age 56.3, affected with ED of medium degree, responder to PDE5-I that urdergone to PDDU and also responder after pharmacologic intracavernosal injection (PII)of prostaglandin E1 20 mcg, with rigid erection and normal maintenance. We moreover evalued 90 men in youthful age (mean 35.2), in absence of vascular risk factors, no responder to PDE5-I that undergone to PDDU by PII at high dosage (bimix: prostaglandin E1 20 mcg, papaverine 20 mg). In the first pool the sampling at "mid penis" resulted significative for arterial insuffciency (AI) in 81% (73), in presence of normal or borderline end diastolic velocity (EDV). Sampling at the "crus" resulted negative for VOD in 90% (81). In the second pool, 66.6% (60) resulted responder with rigid erection and normal maintenance in presence of normal hemodynamic parameters: peak systolic velocity (PSV) and end diastolic velocity (EDV) both at the "crus" and at "mid penis" sampling. 33.4% (30) responded with a semirigid erection and manifested a constant deficit of maintenance; at the "crus"and at "mid penis" the hemodynamic arterial parameters resulted normal. At the "crus" the EDV resulted significantly augmented (VOD index) in 96.6% (29); at "mid penis" augmented EDV was founded in 50% (15). These observational data would be able to confirm the utility of a routinary "double sampling" procedure, at the "crus" and at "md penis", during PDDU in order to better distinguish between VOD or AI or in any case to be useful to stimulate a future more precise standardization in execution of PDDU examination.

Volumetric evaluation of therapy response in patients with lung metastases. Preliminary results with a computer system (CAD) and comparison with unidimensional measurements

The aim of this study was to assess the efficacy of a Computer-Aided Detection (CAD) system in the identification of lung metastases and to compare the volumetric CAD measurements with unidimensional observer measurements in the evaluation of treatment response in oncology patients. Two observers (A and B) evaluated nine patients undergoing lung computed tomography (CT) just before and immediately after treatment with chemotherapy. Multislice CT scans were performed before and after the injection of contrast material with a high-resolution protocol (collimation 4x1 mm, 100 mAs, 120 kV). Response Evaluation Criteria in Solid Tumours (RECIST) criteria were used to consider the disease as stable, increased or decreased. Subsequently, target lesions (most significant lesions identified before and after chemotherapy) were evaluated with a computerised system (CAD) to establish volumetric measurements. Observers' unidimensional measurements and CAD volumetric measurements were analysed for comparison. Twenty-four nodules (diameter: 5-18 mm in the first study and 4-20 mm in the follow-up study) were included. Observers agreed in the assessment of therapy response in 21 nodules: eight were considered to have increased in size, and 13 were judged stable. Observer and CAD measurements disagreed in three nodules: two were considered stable by radiologists and increased by CAD; one was considered increased by radiologists and stable by CAD.As regards patient response, radiologists disagreed in two cases. CAD and observers did not agree in one case. Our preliminary data suggest that volumetric measurements can modify the diagnostic and therapeutic evaluation of oncology patients under chemotherapy.CAD volumetric measurements allow an easy and objective evaluation, reducing interobserver variability in the evaluation of chemotherapy response.

Osteoarthritis antirheumatic drug trials: Effects of a standardized instructional videotape on the reliability of observer-dependent dependent outcome measures

A study was designed to assess the effects of a standardized instructional videotape on reducing interobserver variability for several commonly used observer dependent outcome measures. During a single day, six rheumatologists independently examined six patients with osteoarthritis (OA) in a predetermined order using a Latin square design, before and after viewing a standardized videotape demonstrating 13 examination techniques. Reliability coefficients were calculated based on variance components of the analysis of variance (ANOVA) table. Prestandardization reliability coefficients were >0.80 for all measures and remained above 0.80 following the intervention. It is usually assumed that serial measurement in clinical trials should be performed by the same assessor because of concern regarding interobserver variability. However, the high levels of prestandardization interobserver reliability observed in this study indicate that, for these variables, serial measurements in a clinical trial could be made by different assessors, assuming they were equally skilled. This observation has important implications for outcome measurement in OA clinical trials. Although high levels of prestandardization reliability precluded the demonstration of any significant improvement, we speculate that the videotape might be effective in training less-experienced assessors. Reductions in observer variability have the potential to diminish sample size requirements for OA antirheumatic drug studies. The use of a videotape to achieve this goal offers cost and convenience advantages over one-on-one training procedures, and this method should be further assessed in less-experienced assessors.

Fibromyalgia antirheumatic drug trials: Effects of a standardized instructional videotape on the reliability of observer-dependent outcome measures

A study was designed to assess the effects of a standardized instructional videotape on reducing interobserver variability for several commonly used observer-dependent outcome measures. During a single day, six rheumatologists independently examined six patients with fibromyalgia (FM) in a predetermined order using a Latin square design, before and after viewing a standardized videotape which demonstrated methods for performing dolorimetry and for detecting skinfold tenderness and reactive hyperaemia. Reliability coefficients were calculated based on the variance components of the analysis of variance (ANOVA) table. Prestandardization reliability coefficients were <0.80 for 8 measures. Following standardization all reliability coefficients approximated to or exceeded 0.80. An important and beneficial effect of the standardization procedure was noted for several outcome variables. Such reductions in observer variability have the potential to diminish sample size requirements for FM antirheumatic drug studies. The use of a videotape to achieve this goal has obvious cost and convenience advantages over one-on-one training procedures.