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.

Symmetry recovery in zygomaticomaxillary complex fractures compared to normal unfractured population: A new reliable 3D evaluation.

OBJECTIVE: This study aims to quantify the facial symmetry of surgically treated zygomaticomaxillary complex (ZMC) fractures through a new reliable three-dimensional evaluation method, which is crucial for improving post-operative aesthetic and functional outcomes. MATERIAL AND METHODS: Healthy patients and patients with surgically treated ZMC fractures were retrospectively reviewed. Using Brainlab Elements® the zygomatic bone and the orbit of each patient was segmented and mirrored. Subsequently, the mirrored side was matched with the other side via volume-based registration, using the segmented orbit as reference. Volumetric asymmetry was measured using 3-matic software, and a surface-based matching technique was used to calculate the mean absolute differences (MAD) between the surfaces of the two sides of the ZMC. The reliability of this novel method using volume-based registration was tested, and the intra-class correlation coefficient was assessed. RESULTS: The MAD between the surfaces of the left and right sides in the control group was 0.51 mm (±0.09). As for the ZMC fracture group, MAD was 0.78 mm (±0.20) and 0.72 mm (±0.15) pre- and post-operatively, respectively. The MAD showed statistically significant differences between pre- and post-operative groups (p = 0.005) and between control and post-operative groups (p < 0.001). The intra-class correlation coefficient was high (≥0.99). CONCLUSIONS: This evaluation method using mirroring and volume-based registration to determine the symmetrical position of the ZMC is reliable. The surface-based measurements revealed an improved symmetry after surgery. However, the symmetry of the treated patients remained lower than the control group.

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.

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma.

BACKGROUND: The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios. METHODS: We developed a semiautomated image analysis pipeline to detect areas belonging to the tumor tissue compartment. Perimeter and area measurements of all detected tissue regions were derived, and a specific mathematical formula was applied to reflect the perimeter/area ratio in a comparable, observer-independent manner across digitized WSIs. We demonstrated the plausibility of the MPP score by correlating it with well-established clinicopathologic parameters. We then performed survival analysis to assess the relevance of the MPP score, with an emphasis on different surgical margin scenarios. Machine learning models were developed to assess the relevance of the MPP score in predicting survival and occult cervical nodal metastases. RESULTS: The MPP score was associated with unfavorable tumor growth and infiltration patterns, the presence of lymph node metastases, the extracapsular spread of tumor cells, and higher tumor thickness. Higher MPP scores were associated with worse overall survival (OS) and tongue carcinoma-specific survival (TCSS), both when assessing all pT-categories and pT1-pT2 categories only; moreover, higher MPP scores were associated with a significantly worse TCSS in cases where a cancer-free surgical margin of <5 mm could be achieved on the main surgical specimen. This discriminatory capacity remained constant when examining pT1-pT2 categories only. Importantly, the MPP score could successfully define cases at risk in terms of metastatic disease in pT1-pT2 cancer where tumor thickness failed to exhibit a significant predictive value. Machine learning (ML) models incorporating the MPP score could predict the 5-year TCSS efficiently. Furthermore, we demonstrated that machine learning models that predict occult cervical lymph node involvement can benefit from including the MPP score. CONCLUSIONS: We introduced an objective, quantifiable, and observer-independent parameter, the MPP score, representing the aggressiveness of tumor growth and infiltration in TSCC. We showed its prognostic relevance especially in pT1-pT2 category TSCC, and its possible use in ML models predicting TCSS and occult lymph node metastases.

Phosphatase and Tensin Homolog (PTEN) Expression as a Surrogate Biomarker Correlated With the Depth of Invasion in Cutaneous Malignant Melanoma.

Objective The aim of this study is to evaluate the expression of the phosphatase and tensin homolog (PTEN), which is a tumor suppressor gene that is implicated in the pathogenesis of cutaneous malignant melanoma, in normal skin and melanoma tissue samples. The study also aimed to correlate PTEN expression levels with various clinicopathological parameters of melanoma lesions, thus highlighting the utility of PTEN expression as a prognostic biomarker for melanoma. Study design Immunohistochemistry (IHC) staining was performed on tissue microarray samples representing normal skin and melanoma biopsies of different clinicopathological parameters. Tissue photomicrographs were evaluated with Aperio ImageScope, which has a positive-pixel-counting algorithm built in. Subsequently, a histochemical score (H-score) was derived from the percentage of positive cells (%-staining) and their staining intensity. The H-scores were averaged in groups of tissue samples representing the different melanomas' tumor (T), node (N), and distant metastasis (M), also known as TNM parameters, as set forth by the American Joint Committee on Cancer (AJCC) classification. The mean H-scores were statistically compared using a two-tailed unpaired t-test. Results The PTEN protein expression was measured by IHC and found to be correlated with tumor thickness (T), which is a reliable indicator for survival rates. Specifically, PTEN was significantly downregulated in tumors with a thickness over 2 mm (T3+T4) compared to tumors with a thickness at or below 2 mm (T1+T2). Conclusions The PTEN protein expression, as measured by immunohistochemistry, helped differentiate between tumors with a thickness over 2 mm and tumors with a thickness at or below 2 mm, suggesting PTEN as a potential surrogate marker for the melanoma's invasion depth along with possible prognostic implications. Longitudinal studies evaluating risk stratification based on the expression of PTEN are needed to establish the utility of this promising biomarker in the clinic as an adjunct for pathological examination.

Augmented and Virtual Reality for Preoperative Trauma Planning, Focusing on Orbital Reconstructions: A Systematic Review.

BACKGROUND: This systematic review summarizes recent literature on the use of extended reality, including augmented reality (AR), mixed reality (MR), and virtual reality (VR), in preoperative planning for orbital fractures. METHODS: A systematic search was conducted in PubMed, Embase, Web of Science and Cochrane on 6 April 2023. The included studies compared extended reality with conventional planning techniques, focusing on computer-aided surgical simulation based on Computed Tomography data, patient-specific implants (PSIs), fracture reconstruction of the orbital complex, and the use of extended reality. Outcomes analyzed were technical accuracy, planning time, operative time, complications, total cost, and educational benefits. RESULTS: A total of 6381 articles were identified. Four articles discussed the educational use of VR, while one clinical prospective study examined AR for assisting orbital fracture management. CONCLUSION: AR was demonstrated to ameliorate the accuracy and precision of the incision and enable the better identification of deep anatomical tissues in real time. Consequently, intraoperative imaging enhancement helps to guide the orientation of the orbital reconstruction plate and better visualize the precise positioning and fixation of the PSI of the fractured orbital walls. However, the technical accuracy of 2-3 mm should be considered. VR-based educational tools provided better visualization and understanding of craniofacial trauma compared to conventional 2- or 3-dimensional images.

The Emerging Role of Automation, Measurement Standardization, and Artificial Intelligence in Foot and Ankle Imaging: An Update.

In the past few years, advances in clinical imaging in the realm of foot and ankle have been consequential and game changing. Improvements in the hardware aspects, together with the development of computer-assisted interpretation and intervention tools, have led to a noticeable improvement in the quality of health care for foot and ankle patients. Focusing on the mainstay imaging tools, including radiographs, computed tomography scans, and ultrasound, in this review study, the authors explored the literature for reports on the new achievements in improving the quality, accuracy, accessibility, and affordability of clinical imaging in foot and ankle.

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 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.

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.

Decreased lamin A and B1 expression results in nuclear enlargement in serous ovarian carcinoma, whereas lamin A-expressing tumor cells metastasize to lymph nodes.

BACKGROUND: Lamins, located beneath the nuclear membrane, are involved in maintaining nuclear stiffness and morphology. The nuclei of tumor cells are enlarged in serous carcinoma, a histologic subtype of ovarian cancer that is notable for its poor prognosis. The present study investigated the association of lamin A, B1, and B2 expression with nuclear morphology and metastatic route in serous ovarian carcinoma. METHODS: We performed immunohistochemistry for lamins A, B1, and B2 using specimens of patients who underwent surgery for serous ovarian carcinoma in Gunma University Hospital between 2009 and 2020. Following staining, the specimens were scanned using a whole-slide scanner and processed using computer-assisted image analysis. RESULTS: The positivity rates for lamins A and B1 as well as the rank sum of the positivity rates for lamins A, B1, and B2 were negatively correlated with the mean and standard deviation of the nuclear area. Interestingly, the positivity rate for lamin A was significantly higher in metastatic lesions than in primary tumors in cases with lymph node metastasis. DISCUSSION: Previous studies indicated that decreased lamin A led to nuclear enlargement and deformation and that lamin B1 was required to maintain the meshworks of lamins A and B2 to maintain nuclear morphology. The present study findings suggest that decreased lamin A and B1 expression might lead to nuclear enlargement and deformation and raise the possibility that tumor cells maintaining or not losing lamin A expression might metastasize to lymph nodes.

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.

DJ-1 Oncogene as a Potential Diagnostic and Prognostic Biomarker for Head and Neck Cancer.

Background Current methods used to diagnose and prognosticate oropharyngeal cancer have contributed to unfavorable patient survival rates that have not significantly improved for the last several decades. Precision medicine oncology relies on molecular diagnostics and biomarkers to supplement existing methods of detecting and prognosticating cancers. This study evaluated the expression of DJ-1, an oncogene that is implicated in the pathogenesis of oral squamous cell carcinoma (OSCC), the most common type of head and neck cancer, to determine its utility as a diagnostic and prognostic biomarker. Methodology Immunohistochemistry (IHC) was performed on 13 normal oral mucosa tissue samples and 143 OSCC tissue samples of varying histopathological grades. Computer-assisted image analysis was performed using the Aperio ImageScope software from Leica Biosystems (Buffalo Grove, IL), which utilizes an algorithm of positive pixel counting for the quantification of immunoreactivity and the percentage of positive cell staining, generating a histo-score (H-score). The comparisons of the average H-scores of the different groups were made using a two-tailed T-test with P ≤ 0.05 set as the level of significance. Results The study found a significant increase in DJ-1 expression in oral squamous cell carcinoma tissue samples in comparison to the normal oral mucosa tissue samples. Additionally, the study documented a significant upregulation in DJ-1 expression in the OSCC tissue samples with high histopathological grades compared to the OSCC tissue samples with low histopathological grades. Conclusions DJ-1 expression patterns were able to reliably differentiate between oral squamous cell carcinoma and the normal counterpart tissues of the oral mucosa, thereby highlighting its role as a potential diagnostic biomarker. Moreover, DJ-1 expression significantly correlates with the OSCC histological grade, which serves as an indicator of the differentiation status and a predictor of the biological behavior of malignant neoplasms, adding to DJ-1's potential utility as a prognostic biomarker for this common type of head and neck 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.

Multifactorial estimation of clinical outcome in HPV-associated oropharyngeal squamous cell carcinoma via automated image analysis of routine diagnostic H&E slides and neural network modelling.

OBJECTIVE: Routine haematoxylin and eosin (H&E) photomicrographs from human papillomavirus-associated oropharyngeal squamous cell carcinomas (HPV + OpSCC) contain a wealth of prognostic information. In this study, we developed a high content image analysis (HCIA) workflow to quantify features of H&E images from HPV + OpSCC patients to identify prognostic features and predict patient outcomes. METHODS: First, we have developed an open-source HCIA tool for single-cell segmentation and classification of H&E images. Subsequently, we have used our HCIA tool to analyse a set of 889 images from diagnostic H&E slides in a retrospective cohort of HPV + OpSCC patients with favourable (FO, n = 60) or unfavourable (UO, n = 30) outcomes. We have identified and measured 31 prognostic features which were quantified in each sample and used to train a neural network (NN) model to predict patient outcomes. RESULTS: Univariate and multivariate statistical analyses revealed significant differences between FO and UO patients in 31 and 17 variables, respectively (P < 0.05). At the single-image level, the NN model had an overall accuracy of 72.5% and 71.2% in recognising FO and UO patients when applied to test or validation sets, respectively. When considering 10 images per patient, the accuracy of the NN model increased to 86.7% in the test set. CONCLUSION: Our open-source H&E analysis workflow and predictive models confirm previously reported prognostic features and identifies novel factors which predict HPV + OpSCC outcomes with promising accuracy. Our work supports the use of machine learning in digital pathology to exploit clinically relevant features in routine diagnostic pathology without additional biomarkers.

Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.

Radiomics has been a promising imaging biomarker for many malignant diseases. We developed a novel radiomics strategy that incorporating radiomics features extracted from dual-view mammograms and clinical parameters for identifying benign and malignant breast lesions, and validated whether the radiomics assessment could improve the accurate diagnosis of breast cancer. A total of 380 patients (mean age, 52 ± 7 years) with 621 breast lesions utilizing mammograms on craniocaudal (CC) and mediolateral oblique (MLO) views were randomly allocated into the training (n = 486) and testing (n = 135) sets in this retrospective study. A total of 1184 and 2368 radiomics features were extracted from single-position region of interest (ROI) and position-paired ROI, separately. Clinical parameters were then combined for better prediction. Recursive feature elimination and least absolute shrinkage and selection operator methods were applied to select optimal predictive features. Random forest was used to conduct the predictive model. Intraclass correlation coefficient test was used to assess repeatability and reproducibility of features. After preprocessing, 467 radiomics features and clinical parameters remained in the single-view and dual-view models. The performance and significance of models were quantified by the area under the curve (AUC), sensitivity, specificity, and accuracy. The correlation analysis between variables was evaluated using the correlation ratio and Pearson correlation coefficient. The model using a combination of dual-view radiomics and clinical parameters achieved a favorable performance (AUC: 0.804, 95% CI: 0.668-0.916), outperformed single-view model and model without clinical parameters. Incorporating with radiomics features of dual-view (CC&MLO) mammogram, age, breast density, and type of suspicious lesions can provide a noninvasive approach to evaluate the malignancy of breast lesions and facilitate clinical decision-making.

Phosphotungstic Acid-treated Picrosirius Red Staining Improves Whole-slide Quantitative Analysis of Collagen in Histological Specimens.

We tried to prevent nonspecific nuclear staining (NS-NS) of picrosirius red (PSR) staining by treating the specimens with one of the heteropoly acids phosphotungstic acid (PTA). We analyzed a total of 35 cases of non-cancerous liver tissue for fibrosis and NS-NS under PSR-alone, phosphomolybdic acid (PMA)-pretreated PSR (PMA + PSR), or PTA-pretreated PSR (PTA + PSR) condition. In addition, we analyzed the photosensitivity of PMA or PTA single stain specimens. PTA + PSR significantly suppressed NS-NS compared with PSR. The color of the specimens did not change into blue by 30 times the exposure to whole slide scanner (WSS) light. The PTA + PSR condition showed the highest correlation with the Ishak score (pathological evaluation of liver fibrosis) compared with other conditions. Furthermore, Sirius Red-positive percentage (SRP%) in PSR was increased in the NS-NS observed cases. SRP% in PMA + PSR was significantly affected by WSS light exposure time. Moreover, the deposition of non-polarized PSR-stained substances (NP-PSR+S) clinging to the collagen fibers potentially explains why SRP% seemed bigger under PSR than PTA + PSR. Our protocol enabled us to analyze the whole slide image of PSR staining by high magnification, which would contribute to the accurate analysis of collagen amount in the tissue sections.

Negative correlation between the nuclear size and nuclear Lamina component Lamin A in intraductal papillary mucinous neoplasms of the pancreas.

Background: The nuclear laminar protein Lamin A and inner nuclear membrane protein Emerin plays important role in sustaining nuclear structure. However, They have not investigated the significance of these proteins for development of pancreatic intraductal papillary mucinous neoplasm (IPMN). Methods: We examined pancreatic IPMN specimens for nuclear morphology and nuclear protein expression pattern of Lamin A and Emerin. Forty-two IPMN specimens were included, with 30 classified as intraductal papillary mucinous adenoma (IPMA) and 12 as intraductal papillary mucinous carcinoma (IPMC). Results: Classification according to histological subtype revealed that 26 specimens were of the gastric subtype (1 IPMC case), 8 were pancreatobiliary (6 IPMC cases), 6 were intestinal (3 IPMC cases), and 2 were oncocytic (all cases were IPMC). The frequency of IPMN subtypes in this study seemed to agree with those in previous reports. We analyzed Feulgen staining sections for nuclear morphological analysis using computer-assisted image analysis. Nuclear area and perimeter were significantly larger in IPMC than in IPMA. Finally, we examined the positive ratios of Lamin A and Emerin in immunohistochemical staining sections by image analysis. We found a negative correlation between the nuclear size and Lamin A-positive ratio, which was significantly lower in IPMC than that in IPMA. However, no significant correlation was observed between nuclear size and Emerin expression was observed, and no differences were found in the Emerin-positive ratio between IPMA and IPMC. Conclusion: Our results suggest that a decreased Lamin A positive ratio induces nuclear enlargement in adenomas, which thereby induce promotion to carcinomas. Furthermore, Lamin A expression can be a reliable biomarker for distinguishing between IPMC and IPMA.

Cornulin as a Potential Novel Biomarker for Cutaneous Squamous Cell Carcinoma.

Background This study aimed to evaluate the expression of an epidermal differentiation marker, cornulin, in cutaneous squamous cell carcinoma (cSCC). Cornulin has been found to be downregulated in various squamous cell carcinomas of other tissues; however, its expression in cSCC has never been studied. We predicted that cornulin expression in cSCC is reduced compared to the normal epidermis. Moreover, we hypothesized that an inverse relationship exists between cornulin expression and the loss of differentiation, as defined by histopathological grading of cSCC lesions. Methodology Samples of normal skin and cSCC lesions of variable histopathological grades were stained using immunohistochemistry. High-resolution tissue images were analyzed with Aperio ImageScope (Leica Biosystems) utilizing a positive-pixel-counting algorithm to quantify the staining intensity. Histo-score (H-score) was calculated based on staining intensity and percentage of positive cell staining. Mean H-scores were compared using an unpaired t-test. Results We documented cornulin expression in cSCC for the first time. Cornulin levels were downregulated by more than two-fold in cSCC compared to the normal epidermis. Additionally, we observed a 4.5-fold downregulation in cornulin expression in tumors with high histopathological grades when compared to low histopathological grade tumors. Conclusions Cornulin expression levels measured through immunohistochemistry staining can help distinguish among the different histopathological grades of cSCC. Therefore, we propose that cornulin detection can be an adjunct to pathological examination to evaluate the differentiation status of cSCC specimens. Longitudinal studies are needed to establish the utility of cornulin as a diagnostic and prognostic biomarker for cSCC.