Disruption of HSD17B12 in mouse hepatocytes leads to reduced body weight and defect in the lipid droplet expansion associated with microvesicular steatosis.

The function of hydroxysteroid dehydrogenase 12 (HSD17B12) in lipid metabolism is poorly understood. To study this further, we created mice with hepatocyte-specific knockout of HSD17B12 (LiB12cKO). From 2 months on, these mice showed significant fat accumulation in their liver. As they aged, they also had a reduced whole-body fat percentage. Interestingly, the liver fat accumulation did not result in the typical formation of large lipid droplets (LD); instead, small droplets were more prevalent. Thus, LiB12KO liver did not show increased macrovesicular steatosis with the increasing fat content, while microvesicular steatosis was the predominant feature in the liver. This indicates a failure in the LD expansion. This was associated with liver damage, presumably due to lipotoxicity. Notably, the lipidomics data did not support an essential role of HSD17B12 in fatty acid (FA) elongation. However, we did observe a decrease in the quantity of specific lipid species that contain FAs with carbon chain lengths of 18 and 20 atoms, including oleic acid. Of these, phosphatidylcholine and phosphatidylethanolamine have been shown to play a key role in LD formation, and a limited amount of these lipids could be part of the mechanism leading to the dysfunction in LD expansion. The increase in the Cidec expression further supported the deficiency in LD expansion in the LiB12cKO liver. This protein is crucial for the fusion and growth of LDs, along with the downregulation of several members of the major urinary protein family of proteins, which have recently been shown to be altered during endoplasmic reticulum stress.

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary toxicologic pathologists using a multinomial logistic model. In this study, we assessed both the inter-rater and intra-rater agreement of the pathologists and compared pathologists' ratings to the artificial intelligence (AI)-predicted scores. Pathologists and the AI algorithm were presented with 500 slides of rodent heart. They quantified the amount of cardiomyopathy in each slide. A total of 200 of these slides were novel to this study, whereas 100 slides were intentionally selected for repetition from the previous study. After a washout period of more than six months, the repeated slides were examined to assess intra-rater agreement among pathologists. We found the intra-rater agreement to be substantial, with weighted Cohen's kappa values ranging from k = 0.64 to 0.80. Intra-rater variability is not a concern for the deterministic AI. The inter-rater agreement across pathologists was moderate (Cohen's kappa k = 0.56). These results demonstrate the utility of AI algorithms as a tool for pathologists to increase sensitivity and specificity for the histopathologic assessment of the heart in toxicology studies.

Development of an explainable artificial intelligence model for Asian vascular wound images.

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.

Automated contour extraction for light-sheet microscopy images of zebrafish embryos based on object edge detection algorithm.

Embryo contour extraction is the initial step in the quantitative analysis of embryo morphology, and it is essential for understanding the developmental process. Recent developments in light-sheet microscopy have enabled the in toto time-lapse imaging of embryos, including zebrafish. However, embryo contour extraction from images generated via light-sheet microscopy is challenging owing to the large amount of data and the variable sizes, shapes, and textures of objects. In this report, we provide a workflow for extracting the contours of zebrafish blastula and gastrula without contour labeling of an embryo. This workflow is based on the edge detection method using a change point detection approach. We assessed the performance of the edge detection method and compared it with widely used edge detection and segmentation methods. The results showed that the edge detection accuracy of the proposed method was superior to those of the Sobel, Laplacian of Gaussian, adaptive threshold, Multi Otsu, and k-means clustering-based methods, and the noise robustness of the proposed method was superior to those of the Multi Otsu and k-means clustering-based methods. The proposed workflow was shown to be useful for automating small-scale contour extractions of zebrafish embryos that cannot be specifically labeled owing to constraints, such as the availability of microscopic channels. This workflow may offer an option for contour extraction when deep learning-based approaches or existing non-deep learning-based methods cannot be applied.

Bone Mineral Loss at the Distal Femur and Proximal Tibia Following Spinal Cord Injury in Men and Women.

PURPOSE: Spinal cord injury (SCI) causes rapid bone loss and increases risk of fragility fractures in the lower extremities. The majority of individuals with SCI are men, and few studies have investigated sex as a biological variable in SCI-induced osteoporosis. This cross-sectional study aimed to quantify sex-specific differences in bone mineral following SCI. METHODS: Quantitative computed tomography (QCT) scans of the distal femur and proximal tibia were obtained at baseline of one of four clinical trials enrolling people who sustained SCI 1 month to 50 years prior to recruitment. Bone volume (BV), bone mineral content (BMC), bone mineral density (BMD), and bending strength index (BSI) were quantified in the integral, trabecular, and cortical bone in the epiphysis, metaphysis and diaphysis. Scans from 106 men and 31 women were analyzed to measure sex-specific effects on bone loss over time post-SCI. RESULTS: BMC and BSI declined exponentially as a function of time post-SCI and were best described by separate decay curves for men and women. Women had BV, BMC, and BSI at 58-77% that of men in the acute and plateau phases, with both sexes showing similar rates of loss as a function of time post-SCI. Trabecular BMD was best described as an exponential decay versus time post-SCI, with no sex-specific differences. CONCLUSIONS: Due to consistently lower BV, BMC, and BSI, women may be more susceptible to fractures after SCI than men.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study.

PURPOSE: Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population. MATERIAL AND METHODS: We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses. RESULTS: In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth. CONCLUSION: According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.

The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [18F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [18F]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolute and relative thresholds, k-means and Bayesian clustering, and a self-adaptive configuration (SAC) of k-means and Bayesian was applied. Three state-of-the-art deep learning-based segmentations methods using a 3D U-Net architecture were also applied. One was semiautomatic and two were fully automatic, of which one is publicly available. Dice coefficient (DC) measured segmentation overlap, considering manual segmentation the ground truth. Lymphoma lesions were characterized by 31 features. Intraclass correlation coefficient (ICC) assessed features agreement between different segmentation methods. Nine hundred twenty [18F]FDG-avid lesions were identified. The SAC Bayesian method achieved the highest median intra-observer DC (0.87). Inter-observers' DC was higher for SAC Bayesian than manual segmentation (0.94 vs 0.84, p < 0.001). Semiautomatic deep learning-based median DC was promising (0.83 (Obs1), 0.79 (Obs2)). Threshold-based methods and publicly available 3D U-Net gave poorer results (0.56 ≤ DC ≤ 0.68). Maximum, mean, and peak standardized uptake values, metabolic tumor volume, and total lesion glycolysis showed excellent agreement (ICC ≥ 0.92) between manual and SAC Bayesian segmentation methods. The SAC Bayesian classifier is more reproducible and produces similar lesion features compared to manual segmentation, giving the best concordant results of all other methods. Deep learning-based segmentation can achieve overall good segmentation results but failed in few patients impacting patients' clinical evaluation.

3-dimensional analysis of hard- and soft-tissue symmetry in a Chinese population.

BACKGROUND: Facial symmetry severely affects appearance and function. Large numbers of patients seek orthodontic treatment to improve facial symmetry. However, the correlation between hard- and soft-tissue symmetry is still unclear. Our aim was to investigate the hard- and soft-tissue symmetry in subjects with different levels of menton deviation and sagittal skeletal classes with 3D digital analysis and to investigate the relationship between the entire and individual hard- and soft-tissues. METHODS: A total of 270 adults (135 males and 135 females) consisting of 45 subjects of each sex in each sagittal skeletal classification group. All subjects were further classified into relative symmetry (RS), moderate asymmetry (MA) and severe asymmetry (SA) groups based on the degree of menton deviation from the mid-sagittal plane (MSP). The 3D images were segmented into anatomical structures and mirrored across the MSP after establishing a coordinate system. Original and mirrored images were registered by a best-fit algorithm, and the corresponding root mean square (RMS) values and colormap were obtained. The Mann‒Whitney U test and Spearman correlation were conducted for statistical analysis. RESULTS: The RMS increased with greater deviations with regard to the deviation of the menton in most of anatomical structures. Asymmetry was represented in the same way regardless of sagittal skeletal pattern. The soft-tissue asymmetry had a significant correlation with dentition in the RS group (0.409), while in the SA group, it was related to the ramus (0.526) and corpus (0.417) in males and was related to the ramus in the MA (0.332) and SA (0.359) groups in females. CONCLUSIONS: The mirroring method combining CBCT and 3dMD provides a new approach for symmetry analysis. Asymmetry might not be influenced by sagittal skeletal patterns. Soft-tissue asymmetry might be reduced by improving the dentition in individuals with RS group, while among those with MA or SA, whose menton deviation was larger than 2 mm, orthognathic treatment should be considered.

Automated immunofluorescence analysis for sensitive and precise dystrophin quantification in muscle biopsies.

AIMS: Dystrophin, the protein product of the DMD gene, plays a critical role in muscle integrity by stabilising the sarcolemma during contraction and relaxation. The DMD gene is vulnerable to a variety of mutations that may cause complete loss, depletion or truncation of the protein, leading to Duchenne and Becker muscular dystrophies. Precise and reproducible dystrophin quantification is essential in characterising DMD mutations and evaluating the outcome of efforts to induce dystrophin through gene therapies. Immunofluorescence microscopy offers high sensitivity to low levels of protein expression along with confirmation of localisation, making it a critical component of quantitative dystrophin expression assays. METHODS: We have developed an automated and unbiased approach for precise quantification of dystrophin immunofluorescence in muscle sections. This methodology uses microscope images of whole-tissue sections stained for dystrophin and spectrin to measure dystrophin intensity and the proportion of dystrophin-positive coverage at the sarcolemma of each muscle fibre. To ensure objectivity, the thresholds for dystrophin and spectrin are derived empirically from non-sarcolemmal signal intensity within each tissue section. Furthermore, this approach is readily adaptable for measuring fibre morphology and other tissue markers. RESULTS: Our method demonstrates the sensitivity and reproducibility of this quantification approach across a wide range of dystrophin expression in both dystrophinopathy patient and healthy control samples, with high inter-operator concordance. CONCLUSION: As efforts to restore dystrophin expression in dystrophic muscle bring new potential therapies into clinical trials, this methodology represents a valuable tool for efficient and precise analysis of dystrophin and other muscle markers that reflect treatment efficacy.

Assessment of parathyroid gland cellularity by digital slide analysis.

BACKGROUND: Computer-aided examination of digital tissue images has attracted attention in recent years. Application in the field of parathyroid pathology has not been studied previously. It holds a potential to assist in the examination of parathyroid gland adenoma or hyperplasia. OBJECTIVES: To explore parathyroid cell detection of slide images by digital tissue analysis and compare the results to standard human processing. METHODS: 47 incisional biopsies of healthy appearing parathyroid glands were evaluated for their cellularity level. First, by the standard examination using microscopy by three independent pathologists. We compared the mean cellularity grading of the pathologists to the output of a computerized cell detection software. RESULTS: A disagreement was found between the standard human cellularity grading and the digital analysis output. However, the digital analysis reaches a 94% specificity and 48% sensitivity to predict high cellularity (>60% parenchymal cells). CONCLUSIONS: Digital analysis of parathyroid tissue can be used as a tool for hypercellularity elimination, therefore assisting in the diagnosis of parathyroid cell hyperplasia. Additional studies using more advanced algorithms are necessary for further precision enhancement.

Gingival shape analysis using surface curvature estimation of the intraoral scans.

BACKGROUND: Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape. METHODS: The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8). RESULTS: Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method's reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively. CONCLUSIONS: Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy.

OBJECTIVES: To evaluate the performance of CT-based texture analysis (TA) for predicting clinical outcomes of mechanical thrombectomy (MT) in acute ischemic stroke (AIS). METHODS: This single-center, retrospective study contained 64 consecutive patients with AIS who underwent MT for large anterior circulation occlusion between December 2016 and January 2020. Patients were divided into 2 groups according to the modified Rankin scale (mRS) scores at 3 months as good outcome (mRS ≤ 2) and bad outcome (mRS > 2). Two observers examined the early ischemic changes for TA on baseline non-contrast CT images independently. Demographic, clinical, periprocedural, and texture variables were compared between the groups and ROC curves were made. Logistic regression analysis was used and a model was created to determine the independent predictors of a bad outcome. RESULTS: Sixty-four patients (32 female, 32 male; mean age 63.03 ± 14.42) were included in the study. Fourteen texture parameters were significantly different between patients with good and bad outcomes. The long-run high gray-level emphasis (LRHGE), which is a gray-level run-length matrix (GLRLM) feature, showed the highest sensitivity (80%) and specificity (70%) rates to predict disability. The GLRLM_LRHGE value of > 4885.0 and the time from onset to puncture of > 237.5 mi were found as independent predictors of the bad outcome. The diagnostic rate was 80.0% when using the combination of the GLRLM_LRHGE and the time from onset to puncture cutoff values. CONCLUSION: CT-based TA might be a promising modality to predict clinical outcome after MT in patients with AIS. KEY POINTS: • The gray-level run-length matrix parameters displayed higher diagnostic performance among the texture features. • The long-run high gray-level emphasis showed the highest sensitivity and specificity rates for predicting a bad outcome in stroke patients undergoing mechanical thrombectomy. • The gray-level run-length matrix_long-run high gray-level emphasis value of > 4885.0 (OR = 11.06; 95% CI = 2.51 - 48.77; p = 0.001) and the time from onset to puncture of > 237.5 min (OR = 8.55; 95% CI = 1.96 - 37.21; p = 0.004) were found as independent predictors of the bad outcome.

CT volume of enhancement of disease (VED) can predict the early response to treatment and overall survival in patients with advanced HCC treated with sorafenib.

OBJECTIVES: To analyse the predictive value of the volume of enhancement of disease (VED), based on the CT arterial enhancement coefficient (ΔArt%), in the evaluation of the sorafenib response in patients with advanced hepatocellular carcinoma (HCC). METHODS: Patients with sorafenib-treated advanced HCC, who underwent a multiphase contrast-enhanced CT before (T0) and after 60-70 days of starting therapy (T1), were included. The same target lesions utilised for the response evaluation according to modified Response Evaluation Criteria in Solid Tumors criteria were retrospectively used for the ΔArt% calculation ([(HUarterial phase - HUunenhanced phase) / HUunenhanced phase] × 100). ΔArt% was weighted for the lesion volume to obtain the VED. We compared VEDT0 and VEDT1 values in patients with clinical benefit (CB) or progressive disease (PD). The impact of VED, ancillary imaging findings, and blood chemistries on survival probability was evaluated. RESULTS: Thirty-two patients (25 men, mean age 65.8 years) analysed between 2012 and 2016 were selected. At T1, 8 patients had CB and 24 had PD. VEDT0 was > 70% in 8/8 CB patients compared with 12/24 PD patients (p = 0.011). Patients with VEDT0 > 70% showed a significantly higher median survival than those with lower VEDT0 (451.5 days vs. 209.5 days, p = 0.032). Patients with VEDT0 > 70% and alpha-fetoproteinT0 ≤ 400 ng/ml had significantly longer survival than all other three combinations. In multivariate analysis, VEDT0 > 70% emerged as the only factor independently associated with survival (p = 0.037). CONCLUSION: In patients with advanced HCC treated with sorafenib, VED is a novel radiologic parameter obtained by contrast-enhanced CT, which could be helpful in selecting patients who are more likely to respond to sorafenib, and with a longer survival. KEY POINTS: • To achieve the best results of treatment with sorafenib in advanced HCC, a strict selection of patients is needed. • New radiologic parameters predictive of the response to sorafenib would be essential. • Volume of enhancement of disease (VED) is a novel radiologic parameter obtained by contrast-enhanced CT, which could be helpful in selecting patients who are more likely to respond to therapy, and with a longer survival.

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.

OBJECTIVES: Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. METHODS: Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. RESULTS: The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. CONCLUSIONS: Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. KEY POINTS: • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Are lesion features reproducible between 18F-FDG PET/CT images when acquired on analog or digital PET/CT scanners?

OBJECTIVES: To compare lesion features extracted from 18F-FDG PET/CT images acquired on analog and digital scanners, on consecutive imaging data from the same subjects. METHODS: Whole-body 18F-FDG PET/CT images from 55 oncological patients were acquired twice after a single 18F-FDG injection, with a digital and an analog PET/CT scanner, alternately. Twenty-nine subjects were examined first on the digital, and 26 first on the analog equipment. Image reconstruction was performed using manufacturer standard clinical protocols and protocols that fulfilled EARL1 specifications. Twenty-five features based on lesion standardized uptake value (SUV) and geometry were assessed. To compare these features, intraclass correlation coefficient (ICC), relative difference (RD), absolute value of RD (|RD|), and repeatability coefficient (RC) were used. RESULTS: In total, 323 18F-FDG avid lesions were identified. High agreement (ICC > 0.75) was obtained for most of the lesion features pulled out from both scanners' imaging data, especially when reconstruction protocols fulfilled EARL1 specifications. For EARL1 reconstruction images, the features frequently used in clinics, SUVmax, SUVpeak, SUVmean, metabolic tumor volume, and total lesion glycolysis, reached an ICC of 0.92, 0.95, 0.87, 0.98, and 0.98, and a median RD (digital-analog) of 3%, 5%, 4%, - 3% and 1%, respectively. Using standard reconstruction protocols, the ICC were 0.84, 0.93, 0.80, 0.98, and 0.98, and the RD were 20%, 11%, 13%, - 7%, and 7%, respectively. CONCLUSION: Under controlled acquisition and reconstruction parameters, most of the features studied can be used for research and clinical work. This is especially important for multicenter studies and patient follow-ups. KEY POINTS: • Using manufacturer standard clinical reconstruction protocols, lesions SUV was significantly higher when using the digital scanner, especially the SUVmax that was approximately 20% higher. • High agreement was obtained for the majority of the lesion features when using reconstruction protocols that fulfilled EARL1 specifications. • Longitudinal patient studies can be performed interchangeably between digital and analog scanners when both fulfill EARL1 specifications.

Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy.

Rodent progressive cardiomyopathy (PCM) encompasses a constellation of microscopic findings commonly seen as a spontaneous background change in rat and mouse hearts. Primary histologic features of PCM include varying degrees of cardiomyocyte degeneration/necrosis, mononuclear cell infiltration, and fibrosis. Mineralization can also occur. Cardiotoxicity may increase the incidence and severity of PCM, and toxicity-related morphologic changes can overlap with those of PCM. Consequently, sensitive and consistent detection and quantification of PCM features are needed to help differentiate spontaneous from test article-related findings. To address this, we developed a computer-assisted image analysis algorithm, facilitated by a fully convolutional network deep learning technique, to detect and quantify the microscopic features of PCM (degeneration/necrosis, fibrosis, mononuclear cell infiltration, mineralization) in rat heart histologic sections. The trained algorithm achieved high values for accuracy, intersection over union, and dice coefficient for each feature. Further, there was a strong positive correlation between the percentage area of the heart predicted to have PCM lesions by the algorithm and the median severity grade assigned by a panel of veterinary toxicologic pathologists following light microscopic evaluation. By providing objective and sensitive quantification of the microscopic features of PCM, deep learning algorithms could assist pathologists in discerning cardiotoxicity-associated changes.

A novel CT-based automated analysis method provides comparable results with MRI in measuring brain atrophy and white matter lesions.

PURPOSE: Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial. METHODS: We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016. RESULTS: The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97-98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland-Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI. CONCLUSIONS: MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated.

Computer-assisted image analysis of cytological specimens clarify the correlation between nuclear size and intranuclear cytoplasmic inclusions regardless of BRAFV600E mutation in papillary thyroid carcinoma.

OBJECTIVE: The morphological features of nuclei in cytological and histological specimens were compared and examined for the presence of BRAFV600E mutation and the appearance rate of intranuclear cytoplasmic inclusions (NI). METHODS: BRAFV600E mutation was identified using a mutation-specific antibody (clone; VE1) in 103 thyroid papillary carcinoma cases at Gunma University Hospital. The nuclear area, perimeter, and roundness of the corresponding cytological specimens and haematoxylin and eosin-stained specimens were analysed using image analysis software, and the appearance rate of NI was calculated and compared. RESULTS: BRAFV600E mutation was detected in 71 (69%) cases. The appearance rate of NI was significantly higher in the BRAFV600E mutation-positive group in cytological and histological specimens (P = .0070 and .0184, respectively). Significant differences were observed between the BRAFV600E mutation-negative and -positive groups in the average nuclear area and average nuclear perimeter in cytological specimens (P = .0137 and .0152, respectively). In addition, nuclear enlargement was correlated with the appearance rate of NI regardless of the presence of BRAFV600E mutation in cytological specimens. In the BRAFV600E mutation-negative group, the nuclear area and perimeter were significantly smaller in the lymph node metastasis-positive cases (P = .0182 and .0260, respectively). CONCLUSION: This study found that the appearance rate of NI was positively correlated with the nuclear area and perimeter and negatively correlated with nuclear roundness in cytological specimens. Furthermore, these results were observed regardless of the existence of BRAFV600E mutation. These results have never been previously reported and clearly demonstrate the usefulness of cytological specimens in computer-assisted image analysis.

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow.

Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman's rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman's rank correlation coefficients or Mann-Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = - 0.48 versus ρ = - 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool's training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.

MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma.

OBJECTIVES: To evaluate the diagnostic performance of MRI texture analysis (TA) for differentiation of pediatric craniofacial rhabdomyosarcoma (RMS) from infantile hemangioma (IH). METHODS: This study included 15 patients with RMS and 42 patients with IH who underwent MRI before an invasive procedure. All patients had a solitary lesion. T2-weighted and fat-suppressed contrast-enhanced T1-weighted axial images were used for TA. Two readers delineated the tumor borders for TA independently and evaluated the qualitative MRI characteristics in consensus. The differences of the texture features' values between the groups were assessed and ROC curves were calculated. Logistic regression analysis was used to analyze the value of TA with and without the combination of the qualitative MRI characteristics. A p value < 0.05 was considered statistically significant. RESULTS: Thirty-eight texture features were calculated for each tumor. Eighteen features on T2-weighted images and 25 features on contrast-enhanced T1-weighted images were significantly different between the RMSs and IHs. On contrast-enhanced T1-weighted images, the short-zone emphasis (SZE), which was a gray-level zone length matrix (GLZLM) parameter, had the largest area under the curve: 0.899 (sensitivity 93%, specificity 87%). The independent predictor for the RMS among the qualitative MRI characteristics was heterogeneous contrast enhancement (p < 0.001). Using only a GLZLM_SZE value of lower than 0.72 was found to be the best diagnostic parameter in predicting RMS (p < 0.001; 95% CI, 8.770-992.4). CONCLUSION: MRI-based TA may contribute to differentiate RMS from IH without invasive procedures. KEY POINTS: • Texture analysis may help to distinguish between rhabdomyosarcoma and infantile hemangioma without invasive procedures. • The gray-level zone length matrix parameters, especially the short-zone emphasis, may be a potential predictor for rhabdomyosarcoma. • Using contrast-enhanced T1-weighted images may be superior to T2-weighted images to differentiate rhabdomyosarcoma from infantile hemangioma in texture analysis.