Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions

Fractal Dimension, Circularity, and Solidity of Cell Clusters in Liquid-Based Endometrial Cytology Are Potentially Useful for Endometrial Cancer Detection and Prognosis Prediction

Endometrial cancer (EC) in women is increasing globally, necessitating improved diagnostic methods and prognosis prediction. While endometrial histology is the conventional approach, liquid-based endometrial cytology may benefit from novel analytical techniques for cell clusters. A clinical study was conducted at the University of Fukui Hospital from 2012 to 2018, involving 210 patients with endometrial cytology. The liquid-based cytology images were analyzed using cell cluster analysis with Image J software. Logistic regression, ROC analysis, and survival analysis were employed to assess the diagnostic accuracy and prognosis between cell cluster analysis and EC/atypical endometrial hyperplasia (AEH). Circularity and fractal dimension demonstrated significant associations with EC and AEH, regardless of age and cytology results. The ROC analysis revealed improved diagnostic accuracy when combining fractal dimension with cytology, particularly in menopausal age groups. Lower circularity and solidity were independently associated with poor overall survival, while higher fractal dimension values correlated with poorer overall survival in Grades 2 and 3 endometrial cancers. The combination of circularity and fractal dimension with cytology improved diagnostic accuracy for both EC and AEH. Moreover, circularity, solidity, and fractal dimension may serve as prognostic indicators for endometrial cancer, contributing to the development of more refined screening and diagnostic strategies.

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Evaluating the use of machine learning in endometrial cancer: a systematic review

OBJECTIVE

To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models.

METHODS

This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0.

RESULTS

Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%).

CONCLUSION

Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer.

PROSPERO REGISTRATION NUMBER

CRD42021269565.

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence

In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endometrial cancer, biopsies are viewed and diagnosed by pathologists. Pathology is increasingly digitised, with slides viewed as images on screens rather than through the lens of a microscope. The availability of these images is driving automation via the application of AI. A model that classifies slides in the manner proposed would allow prioritisation of these slides for pathologist review and hence reduce time to diagnosis for patients with cancer. Previous studies using AI on endometrial biopsies have examined slightly different tasks, for example using images alongside genomic data to differentiate between cancer subtypes. We took 2909 slides with "malignant" and "other or benign" areas annotated by pathologists. A fully supervised convolutional neural network (CNN) model was trained to calculate the probability of a patch from the slide being "malignant" or "other or benign". Heatmaps of all the patches on each slide were then produced to show malignant areas. These heatmaps were used to train a slide classification model to give the final slide categorisation as either "malignant", "other or benign" or "insufficient". The final model was able to accurately classify 90% of all slides correctly and 97% of slides in the malignant class; this accuracy is good enough to allow prioritisation of pathologists' workload.

Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks

OBJECTIVES

The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs).

METHODS

We used Li Brush to acquire endometrial cells from patients. Liquid-based cytology technology was used to provide slides. The slides were scanned and divided into malignant and benign groups. We proposed two (a U-net segmentation and a DenseNet classification) networks to identify images. Another four classification networks were used for comparison tests.

RESULTS

A total of 113 (42 malignant and 71 benign) endometrial samples were collected, and a dataset containing 15,913 images was constructed. A total of 39,000 ECCs patches were obtained by the segmentation network. Then, 26,880 and 11,520 patches were used for training and testing, respectively. On the premise that the training set reached 100%, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. The remaining 600 malignant patches were used for verification.

CONCLUSIONS

An artificial intelligence system was successfully built to classify malignant and benign ECCs.

Machine Learning for Endometrial Cancer Prediction and Prognostication

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

Diagnosis of endometrium hyperplasia and screening of endometrial intraepithelial neoplasia in histopathological images using a global-to-local multi-scale convolutional neural network

BACKGROUND AND OBJECTIVE

Endometrial hyperplasia (EH), a uterine pathology characterized by an increased gland-to-stroma ratio compared to normal endometrium (NE), may precede the development of endometrial cancer (EC). Particularly, atypical EH also known as endometrial intraepithelial neoplasia (EIN), has been proven to be a precursor of EC. Thus, diagnosing different EH (EIN, hyperplasia without atypia (HwA) and NE) and screening EIN from non-EIN are crucial for the health of female reproductive system. Computer-aided-diagnosis (CAD) was used to diagnose endometrial histological images based on machine learning and deep learning. However, these studies perform single-scale image analysis and thus can only characterize partial endometrial features. Empirically, both global (cytological changes relative to background) and local features (gland-to-stromal ratio and lesion dimension) are helpful in identifying endometrial lesions.

METHODS

We proposed a global-to-local multi-scale convolutional neural network (G2LNet) to diagnose different EH and to screen EIN in endometrial histological images stained by hematoxylin and eosin (H&E). The G2LNet first used a supervised model in the global part to extract contextual features of endometrial lesions, and simultaneously deployed multi-instance learning in the local part to obtain textural features from multiple image patches. The contextual and textural features were used together to diagnose different endometrial lesions after fusion by a convolutional block attention module. In addition, we visualized the salient regions on both the global image and local images to investigate the interpretability of the model in endometrial diagnosis.

RESULTS

In the five-fold cross validation on 7812 H&E images from 467 endometrial specimens, G2LNet achieved an accuracy of 97.01% for EH diagnosis and an area-under-the-curve (AUC) of 0.9902 for EIN screening, significantly higher than state-of-the-arts. In external validation on 1631 H&E images from 135 specimens, G2LNet achieved an accuracy of 95.34% for EH diagnosis, which was comparable to that of a mid-level pathologist (95.71%). Specifically, G2LNet had advantages in diagnosing EIN, while humans performed better in identifying NE and HwA.

CONCLUSIONS

The developed G2LNet that integrated both the global (contextual) and local (textural) features may help pathologists diagnose endometrial lesions in clinical practices, especially to improve the accuracy and efficiency of screening for precancerous lesions.

Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review

OBJECTIVE

Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers.

METHODS

A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy.

RESULTS

Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review.

CONCLUSIONS

In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.

What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.

Application of artificial intelligence in gynecologic malignancies: A review

With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

Applying Artificial Intelligence to Gynecologic Oncology: A Review

Importance

Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies.

Objective

The aim of this study was to review the role of AI in gynecologic oncology.

Evidence Acquisition

Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers.

Results

A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response.

Conclusions and Relevance

Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

Background:

Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science.

Procedures:

In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers.

Results:

These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes.

Conclusions:

Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

The relationship of human papillomavirus and cytology co-testing results with endometrial and ovarian cancer diagnoses

BACKGROUND

To investigate whether routine cervical screening using human papillomavirus (HPV) and cytology co-testing effectively identifies women with endometrial (EC) or ovarian (OvC) cancer.

METHODS

In 2003, Kaiser Permanente Northern California implemented triennial co-testing in women aged ≥30 years. Index screening results (n = 2,385,729) were linked to subsequent EC (n = 3434) and OvC (n = 1113) diagnoses from January 1, 2003 to December 31, 2017. EC were categorized as type 1 or 2, and, selectively, EC and OvC diagnoses were stratified on whether symptoms were present at the time of the co-test. Fractions and absolute risks of EC or OvC of each co-testing result were calculated.

RESULTS

Most EC (82.18%) and OvC (88.68%) were preceded by a negative HPV and negative cytology co-test. More EC were preceded by atypical squamous cells of undetermined significance (ASC-US) or more severe (ASC-US+) cytology and negative HPV test (n = 290) (8.44% of EC) compared to a negative cytology and a positive HPV test (n = 31) (0.89% of EC) (p < 0.001). The absolute risk of any EC diagnosis following ASC-US+ and negative HPV test was 0.48%. Atypical glandular cells (AGC) cytology and a negative HPV result preceded 6.92% of any EC diagnosis, with an absolute risk of 4.02%, but preceded only 1.13% of type 2 EC cases, with an absolute risk of 0.24%, in asymptomatic women. AGC cytology and a negative HPV result preceded 1.44% of OvC, with an absolute risk of 0.28%.

CONCLUSIONS

Abnormal cervical screening tests, even AGC cytology, rarely precedes and poorly predict women with EC or OvC.

Comprehensive evaluation of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus for PMOP in ovariectomized rats based on MLP-ANN methods

ETHNOPHARMACOLOGICAL RELEVANCE

Kidney deficiency is the main pathogenesis of osteoporosis based on the theory of "kidney governing bones" in traditional Chinese medicine (TCM). Osteoporosis is a systemic disease; kidney deficiency influences the growth, aging and reproduction of human body, reflecting in endocrine, nerve, immunity, metabolism and other functions. Multi-target drugs composed of natural non-toxic products from kidney-reinforcing herbs, are being investigated for the treatment of osteoporosis. Therefore, it is necessary and imperative to develop an objective and comprehensive method to evaluate and compare the effects of herbs with listed drugs.

AIM OF THE STUDY

This study was designed to evaluate and compare the therapeutic effects and the underlying molecular mechanism of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) with Raloxifene hydrochloride (RH) in ovariectomy (OVX)-induced postmenopausal osteoporosis (PMOP) rats based on the multi-layer perception (MLP)-artificial neural network (ANN) model.

MATERIALS AND METHODS

Female SD rats were subjected to either sham surgery (n = 8) or bilateral OVX (n = 48). One week after recovering from surgery, the OVX-induced rats were randomly divided into three groups: OVX model group (n = 32, every 8 rats were killed at the end of the 5th, 9th, 11th or 13th week after OVX), EL group (treated with EL 0.35 g/kg, n = 8), and RH group (treated with RH 6.25 mg/kg, n = 8). The rats in the treatment groups were administrated once a day for 12 weeks, then sacrificed. We observed bone mass and quality, bone remodeling, the function of estrogen and TGF-β1/Smads pathway in all rats.

RESULTS

Both EL and RH could increase bone mineral density, enhance bone strength, relieve bone micro-structure degeneration, re-balance bone remodeling, regulate estrogen dysfunction, and up-regulate TGF-β1 expression. The evaluation of the MLP-ANN model showed that EL and RH had markedly anti-PMOP effects, and there was no significant difference in the comprehensive evaluation of anti-osteoporosis between the two drugs. However, RH had better effects on bone mass and quality and TGF-β1/Smads pathway than EL; EL had better effects on estrogen function than RH.

CONCLUSION

Combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) exhibited bone-protective effects on PMOP. The MLP-ANN method evaluated the efficacy of drugs more comprehensively, which provided a new direction for the evaluation and comparison of drugs.

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Objective

This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant.

Results

The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed.

Conclusion

AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Liver Fibrosis Assessment in a Cohort of Greek HIV Mono-Infected Patients by Non-Invasive Biomarkers

BACKGROUND

Non-alcoholic Fatty Liver Disease (NAFLD) is common in HIV-infected individuals. Liver biopsy remains the gold-standard procedure for the diagnosis of liver fibrosis, but both Transient Elastography (TE) and Non-invasive Biomarkers (NIBMs) have emerged as alternatives.

OBJECTIVES

Our study's aim was to validate commonly used NIBMs for the assessment of liver fibrosis in a cohort of Greek HIV-mono-infected patients.

METHODS

Inclusion criteria were confirmed HIV-infection and age>18 years and exclusion criteria HBV or HCV seropositivity, liver disease other than NAFLD, alcohol abuse, ascites, transaminases levels>4xULN(upper limit of normal) and Body-Mass index(BMI)>40. Liver stiffness (LS) measurement with TE and thorough laboratory work up and medical history were acquired at study entry. FIB-4, APRI, NFS, BARD, Forns and Lok scores were calculated for each patient.

RESULTS

A total of 157 patients were eligible for this study. Significant liver fibrosis, compatible with Metavir score of F3-F4, was found in only 11(7%) patients. These findings were in accordance with those of the NIBMs; the BARD score constituting the only exception, allocating 102(65%) patients as having significant liver fibrosis. In order to obtain a balance between sensitivity and specificity new cut-offs for each NIBM were calculated; FIB-4 score yielded the best results, since by changing the cut-off to 1.49 a sensitivity and specificity balanced for both close to 85% was achieved.

CONCLUSION

Our findings suggest that NIBMs can be used for the evaluation of liver fibrosis in HIV mono-infected patients. New cut-offs for NIBMs should probably be calculated, to help distinguishing patients with significant from those with mild/no fibrosis.

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3,300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76.91 ± 1.17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i.e., normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84.50% accuracy in the four-class classification task, as well as an AUC of 0.9829 with a 77.97% (95% confidence interval, CI, 65.27%∼87.71%) sensitivity and 100% (95% CI, 97.42%∼100.00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.

An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques

The objectives of this study are to assess various automated texture features obtained from the segmented colony regions of induced pluripotent stem cells (iPSCs) and confirm their potential for characterizing the colonies using different machine learning techniques. One hundred and fifty-one features quantified using shape-based, moment-based, statistical and spectral texture feature groups are extracted from phase-contrast microscopic colony images of iPSCs. The forward stepwise regression model is implemented to select the most appropriate features required for categorizing the colonies. Support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), decision tree (DT), and adaptive boosting (Adaboost) classifiers are used with ten-fold cross-validation to evaluate the texture features within each texture feature group and fused-features group to characterize healthy and unhealthy colonies of iPSCs. Overall, based on the classification performances of the four texture feature groups using the five classifier models, statistical features always exhibit a high predictive capacity (>87.5%). However, the classification performance using fused texture patterns with statistical, shape-based, and moment-based features was found to be robust and reliable with fewer false positive and false negative values compared to the features when either one is used for the classification of colonies of iPSCs. Furthermore, the results showcase that the SVM, RF and Adaboost classifiers deliver better classification performances than DT and MLP. Our findings suggest that the proposed automated fused statistical, shape-based, and moment-based texture pattern features trained with machine learning techniques are potentially more appropriate and helpful to biologists for characterizing colonies of stem cells.

Discovery and application of immune biomarkers for hematological malignancies

INTRODUCTION

Hematological malignancies originate and progress in primary and secondary lymphoid organs, where they establish a uniquely immune-suppressive tumour microenvironment. Although high-throughput transcriptomic and proteomic approaches are being employed to interrogate immune surveillance and escape mechanisms in patients with solid tumours, and to identify actionable targets for immunotherapy, our knowledge of the immunological landscape of hematological malignancies, as well as our understanding of the molecular circuits that underpin the establishment of immune tolerance, is not comprehensive. Areas covered: This article will discuss how multiplexed immunohistochemistry, flow cytometry/mass cytometry, proteomic and genomic techniques can be used to dynamically capture the complexity of tumour-immune interactions. Moreover, the analysis of multi-dimensional, clinically annotated data sets obtained from public repositories such as Array Express, TCGA and GEO is crucial to identify immune biomarkers, to inform the rational design of immune therapies and to predict clinical benefit in individual patients. We will also highlight how artificial neural network models and alternative methodologies integrating other algorithms can support the identification of key molecular drivers of immune dysfunction. Expert commentary: High-dimensional technologies have the potential to enhance our understanding of immune-cancer interactions and will support clinical decision making and the prediction of therapeutic benefit from immune-based interventions.

Novel Screening Tool for Stroke Using Artificial Neural Network

Background and Purpose—

The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting.

Methods—

Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method.

Results—

A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8–86.3) and 86.2% (95% confidence interval, 78.7–91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7–95.3).

Conclusions—

Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination.