scholarly journals Histological Severity Risk Factors Identification in Juvenile-Onset Recurrent Respiratory Papillomatosis: How Immunohistochemistry and AI Algorithms Can Help?

2021 ◽  
Vol 11 ◽  
Author(s):  
Charles Lépine ◽  
Paul Klein ◽  
Thibault Voron ◽  
Marion Mandavit ◽  
Dominique Berrebi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Image Analysis ◽  
Deep Learning ◽  
Severe Disease ◽  
Recurrent Respiratory Papillomatosis ◽  
Juvenile Onset ◽  
Strong Intensity ◽  
Potential Biomarker ◽  
Significant Difference

Juvenile-onset recurrent respiratory papillomatosis (JoRRP) is a condition characterized by the repeated growth of benign exophytic papilloma in the respiratory tract. The course of the disease remains unpredictable: some children experience minor symptoms, while others require multiple interventions due to florid growth. Our study aimed to identify histologic severity risk factors in patients with JoRRP. Forty-eight children from two French pediatric centers were included retrospectively. Criteria for a severe disease were: annual rate of surgical endoscopy ≥ 5, spread to the lung, carcinomatous transformation or death. We conducted a multi-stage study with image analysis. First, with Hematoxylin and eosin (HE) digital slides of papilloma, we searched for morphological patterns associated with a severe JoRRP using a deep-learning algorithm. Then, immunohistochemistry with antibody against p53 and p63 was performed on sections of FFPE samples of laryngeal papilloma obtained between 2008 and 2018. Immunostainings were quantified according to the staining intensity through two automated workflows: one using machine learning, the other using deep learning. Twenty-four patients had severe disease. For the HE analysis, no significative results were obtained with cross-validation. For immunostaining with anti-p63 antibody, we found similar results between the two image analysis methods. Using machine learning, we found 23.98% of stained nuclei for medium intensity for mild JoRRP vs. 36.1% for severe JoRRP (p = 0.041); and for medium and strong intensity together, 24.14% for mild JoRRP vs. 36.9% for severe JoRRP (p = 0.048). Using deep learning, we found 58.32% for mild JoRRP vs. 67.45% for severe JoRRP (p = 0.045) for medium and strong intensity together. Regarding p53, we did not find any significant difference in the number of nuclei stained between the two groups of patients. In conclusion, we highlighted that immunochemistry with the anti-p63 antibody is a potential biomarker to predict the severity of the JoRRP.

scholarly journals The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study

2020 ◽  
Vol 58 (7) ◽  
pp. 1106-1115 ◽  
Author(s):  
Yufen Zheng ◽  
Ying Zhang ◽  
Hongbo Chi ◽  
Shiyong Chen ◽  
Minfei Peng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Count ◽  
Disease Progression ◽  
Neutrophil Count ◽  
Calibration Curve ◽  
Lymphocyte Count ◽  
Severe Disease ◽  
Clinical Impact ◽  
Predictive Tool ◽  
Potential Biomarker

AbstractObjectivesIn December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19.MethodsIn this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy.ResultsThe data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients.ConclusionsWe designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

scholarly journals Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Primož Godec ◽  
Matjaž Pančur ◽  
Nejc Ilenič ◽  
Andrej Čopar ◽  
Martin Stražar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Data Analysis ◽  
Protein Localization ◽  
Visual Programming ◽  
Developmental Competence ◽  
Image Clustering ◽  
Small Scale ◽  
Learning Approaches

Abstract Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

scholarly journals Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents

2020 ◽  
Vol 34 (09) ◽  
pp. 13636-13637
Author(s):  
Wanita Sherchan ◽  
Sue Ann Chen ◽  
Simon Harris ◽  
Nebula Alam ◽  
Khoi-Nguyen Tran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Deep Learning ◽  
Language Processing ◽  
Regulatory Compliance ◽  
Rapid Identification ◽  
Traffic Light ◽  
Risk Status ◽  
Government Regulations ◽  
Financial Advice

This paper describes Cognitive Compliance - a solution that automates the complex manual process of assessing regulatory compliance of personal financial advice. The solution uses natural language processing (NLP), machine learning and deep learning to characterise the regulatory risk status of personal financial advice documents with traffic light rating for various risk factors. This enables comprehensive coverage of the review and rapid identification of documents at high risk of non-compliance with government regulations.

Survival outcomes according to the tumor mutation burden and PD-L1 expression in hepatobiliary tumors.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 566-566
Author(s):  
Jorge Sánchez-García ◽  
Fidel Lopez-Verdugo ◽  
Andrew Gagnon ◽  
Diane Alonso ◽  
Shiro Fujita ◽  
...  
Keyword(s):  
Risk Factors ◽  
Checkpoint Inhibitors ◽  
Smoking Status ◽  
Immune Therapy ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Potential Biomarker ◽  
Significant Difference ◽  
Hepatobiliary Tumors ◽  
Next Generation Sequencing Ngs

566 Background: Hepatobiliary (HB) tumors are aggressive tumors with emerging evidence for increasing sensitivity to immune checkpoint inhibitors (ICI). Tumor mutation burden (TMB) was found to be a quantitative biomarker associated with production of neoantigens within the tumor and predict the sensitivity to immune therapy. Herein, we explore the TMB, microsatellite instability (MSI) and PD-L1 expression as a potential biomarker of response to immune therapy in HB tumors. Methods: We retrospectively assessed all patients with hepatobiliary malignancies who have undergone next generation sequencing (NGS) between October 2009 and June 2019. We then analysed the tumor mutation burden and PD-L1 of these tumors and also identified frequency of patients with no clinically actionable mutations. Results: In our total 61 patients with HB tumors predominantly were male (62.3%) with mean age of 63 years. Thirty-four patients had hepatocellular carcinoma, 22 patients had cholangiocarcinoma and 5 patients had gallbladder carcinoma. The most common risk factors were smoking status, cirrhosis, alcohol consumption and hepatitis C virus. The mean TMB reported was 3.2 (1.16 – 7.35). MSI was identified in 13 patients and one was indeterminate. Only 17 patients had PD-L1 positive. At least, 37 patients had one clinically actionable mutation while 24 patients had no clinically actionable mutations. Mean overall survival was 16.6 months, but no statistically significant difference was found by high PD-L1 (3 vs 3.7 months, p=0.3) expression. Conclusions: Our data suggests the TMB in HB tumors is low in general irrespective of their underlying risk factors. We also noted more than half had microsatellite stable tumors and PD-L1 expression. Future larger studies are needed to evaluate TMB, MSI and PD-L1 as a potential biomarker in hepatobiliary tumors to help select patients that will benefit from immune therapy.

scholarly journals Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Alaa Khadidos ◽  
Adil Khadidos ◽  
Olfat M. Mirza ◽  
Tawfiq Hasanin ◽  
Wegayehu Enbeyle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Locally Advanced ◽  
Textural Analysis ◽  
Machine Learning Techniques ◽  
Response To Treatment ◽  
Learning Techniques ◽  
Potential Applications ◽  
Analysis Models

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

scholarly journals A Preliminary Clinical Study on 100 Egyptian Psoriatic Patients

2016 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Metabolic Syndrome ◽  
Severe Disease ◽  
Pustular Psoriasis ◽  
Clinical Aspects ◽  
Juvenile Onset ◽  
Immune Mediated ◽  
Significant Difference ◽  
Egyptian Patients ◽  
Pasi Score

Background: Psoriasis is a chronic, immune-mediated, inflammatory condition frequently seen in the clinical practice with a reported prevalence of 0.6 to 4.8 percent in the general population. However, data on psoriasis in Egypt are scarce. So, our aim was to investigate the clinical characterization of psoriasis in 100 Egyptian patients. Method: One hundred Egyptian psoriasis patients were enrolled in this study. A detailed questionnaire was filled including demographic and clinical aspects of the disease. Some laboratory tests were done to search for associated diseases like diabetes, metabolic syndrome and hepatitis C virus (HCV) and to correlate them with the disease severity. The collected data were analyzed by SPSS version 17. Results: Thirty seven patients were diagnosed with juvenile-onset psoriasis. There was no significant difference between the mean PASI score for adult versus juvenile onset psoriasis. Pustular psoriasis affected 15% of the patients including children and infants. Metabolic syndrome was absent in juvenile onset psoriatic patients and wasn’t associated with a significantly higher PASI score in the adults affected. PASI score was significantly high in the HCV positive and the hypertensive patients. Conclusion: Although the study sample is quite small to reach definite judgments on psoriasis in Egypt, yet we noticed that early onset psoriasis is quite a common and challenging disease. Metabolic syndrome is not common in the studied children with psoriasis. Pustular psoriasis is a common entity even in children and infants. HCV is associated with a severe disease and might be an inducing factor for psoriasis.

scholarly journals Deep Neural Survival Networks for Cardiovascular Risk Prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

2021 ◽  
Author(s):  
Quincy A Hathaway ◽  
Naveena Yanamala ◽  
Matthew J Budoff ◽  
Partho P Sengupta ◽  
Irfan Zeb
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Deep Learning ◽  
Risk Prediction ◽  
External Validation ◽  
Imaging Biomarkers ◽  
Atherosclerotic Cardiovascular Disease ◽  
Ethnic Study ◽  
Ascvd Risk ◽  
Traditional Risk Factors

Background: There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches. Methods: 6,814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated. Results: In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.79, P≤0.001) and mortality (AUC: 0.86 vs. 0.80, P≤0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a 65% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P≤0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.07 vs. 2.66, P≤0.001) and mortality (6.28 vs. 4.67, P=0.014). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction. Conclusion: DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

scholarly journals Machine learning improves the prediction of febrile neutropenia in Korean inpatients undergoing chemotherapy for breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bum-Joo Cho ◽  
Kyoung Min Kim ◽  
Sanchir-Erdene Bilegsaikhan ◽  
Yong Joon Suh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Febrile Neutropenia ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Primary Prophylaxis ◽  
Machine Learning Algorithms ◽  
Significant Difference

Abstract Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

Deep Learning Techniques for Biomedical Image Analysis in Healthcare

2020 ◽  
pp. 31-46
Author(s):  
Sivakami A. ◽  
Balamurugan K. S. ◽  
Bagyalakshmi Shanmugam ◽  
Sudhagar Pitchaimuthu
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Interpretation ◽  
Medical Applications ◽  
Biomedical Image Analysis ◽  
Future Directions ◽  
Biomedical Image ◽  
Learning Techniques ◽  
Visual Problems

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

2021 ◽  
Author(s):  
◽  
Mahdieh Shabanian ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Neurological Disease ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Disease State ◽  
Institutional Review Board ◽  
Institutional Review

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

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