Cancer Diagnosis Problem Research Articles

Prediction and validation of circulating G-quadruplexes as a novel biomarker in colorectal cancer.

There are some problems in the clinical diagnosis of colorectal cancer (CRC), such as the difficulty in saving samples, so it is the most popular research work to develop a diagnostic index and method that is easy to obtain, convenient to save and stable. G-quadruplex (G4) is a unique structure found in DNA and it plays a crucial biological role in tumor formation. G4 is derived from DNA with good stability, and the DNA of serum samples is easy to obtain. Therefore, G4 has the potential as an ideal marker for CRC diagnosis. However, it has not received more attention. Through bioinformatics-based G4 mutation prediction in the genome, we discovered that the G4 quantity in SW480 cells was lower than that of the reference gene. However, it was unclear how the G4 quantity changed in the actual samples. We detected the G4 content by fluorescence in cells and human serum samples. G4 content was significantly higher than that in NCM480 (P<0.001). To further explore the relationship between tumorigenesis and G4, we knocked out the TP53 gene in SW480 cells and found that the G4 content decreased significantly (64%) (P<0.001). The difference in G4 content is a key factor in distinguishing between normal and tumor cells. Furthermore, we detected G4 in serum samples from 27 healthy individuals and 27 patients with CRC and found that G4 was significantly increased in those with CRC (P<0.001) by 1.94 fold. We also evaluated the G4 model using receiver operating characteristic (ROC), with an area under the curve of 0.91, and found it to have excellent specificity and sensitivity. The increased G4 is an important characteristic in patients with CRC and has clinical application value as a novel biomarker. The relationship between G4 and TP53 regulation may be a potential target for future cancer studies, and as attention to this area of research increases, the underlying mechanisms may be better understood, potentially benefiting clinical cancer treatment.

INFLUENCE OF THE WAR ON THE ORGANIZATION OF MEDICAL ASSISTANCE FOR ONCOLOGICAL DISEASES

The article discusses current issues of providing oncology medical care at the present time. Current legislation has been studied and modern approaches to the structure and features of the organization of providing medical care for malignant neoplasms during the war in Ukraine have been analyzed.&#x0D; Introduction. The war as a result of the Russian attack made it difficult for patients to access necessary medical services and medicines. The most problematic in the organization of cancer control in the regions are the timely detection of patients, adequate staging of the malignant process, registration of deaths, as well as the system of dispensary observation of cancer patients, without the establishment of which it is impossible to achieve success in increasing the survival rate of cancer patients. High levels of neglect of visual malignant neoplasms in many regions indicate that dental, gynecological and other services of the general hospital network of these regions do not take an active part in solving the problem of timely cancer diagnosis [1, 2, 4].&#x0D; During the war, the Medical Guarantee Program continues to operate, and cancer patients can receive government-provided treatment free of charge. Guarantees cover medical care at every stage of treatment: diagnosis, chemotherapy and radiological treatment, support in outpatient or inpatient settings, surgery, rehabilitation, palliative care. A patient can receive medical assistance free of charge upon a doctor's referral [2, 11].&#x0D; An analysis of the onco-epidemiological situation in Ukraine in recent years revealed a significant decrease in the number of both patients and deaths from cancer compared to previous years. The quantitative characteristics of morbidity were obviously affected by the epidemic of COVID-19 and the war in the country, which changed the organization of the diagnostic process and the patient's ability to get to a specialized oncology care facility [4].&#x0D; The purpose of the work is to study the organization of medical care for oncological diseases during the period of martial law.&#x0D; Research materials and methods. An analysis of the legal framework was carried out. The following methods were used: content analysis, systematic approach and analysis.

Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures.

Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced.

A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN-SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN-SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.

Lung cancer subtyping from gene expression data using general and enhanced Fuzzy min–max neural networks

AbstractIn this article, we address the problem of lung cancer diagnosis from gene expression data, which is now recognized as an effective means for early treatment and prevention of cancer. Specifically, we employ Fuzzy min–max (FMM) classifier for the task which is a well‐known neuro‐fuzzy neural network. The idea is to take advantage of the fuzzy class definitions whose boundaries are set using the min–max hyperbox constructed for each class. We implement two advanced FMM neural network architectures: general Fuzzy min–max (GFMM) and enhanced Fuzzy min–max (EFMM) for the classification of lung cancer subtypes from gene expression data. The advantage of GFMM is that it involves very simple operations for hyperbox manipulation, and can handle both labeled and unlabeled data. On the other hand, EFMM proposes three heuristic rules related to hyperbox expansion, contraction and the overlap test, which enhances the learning algorithm. We perform the classification of gene expression data using these two models, and then we analyze the performance by visualizing the hyperboxes obtained after training, and compare the accuracies of these classifiers with the state of the art. Least absolute shrinkage and selection operator (LASSO) is used for selecting the informative genes from the high‐dimensional gene expression data. From the empirical results, we observe that GFMM with LASSO gives the best performance of all, with validation accuracy of 98.04% and cross‐validation accuracy of 94.06%.

Correntropy-based robust multilayer extreme learning machines

In extreme learning machines (ELM), the hidden node parameters are randomly generated and the output weights can be analytically computed. To overcome the bad feature extraction ability of the shallow architecture of ELM, the hierarchical ELM has been extensively studied as a deep architecture with multilayer neural network. However, the commonly used mean square error (MSE) criterion is very sensitive to outliers and impulsive noises, generally existing in real world data. In this paper, we investigate the correntropy to improve the robustness of the multilayer ELM and provide sparser representation. The correntropy, as a nonlinear measure of similarity, is robust to outliers and can approximate different norms (from ℓ0 to ℓ2). A new full correntropy based multilayer extreme learning machine (FC-MELM) algorithm is proposed to handle the classification of datasets which are corrupted by impulsive noises or outliers. The contributions of this paper are three-folds: (1) The MSE based reconstruction loss is replaced by the correntropy based loss function; In this way, the robustness of the ELM based multilayer algorithms is enhanced. (2) The traditional ℓ1-based sparsity penalty term is also replaced by a correntropy-based sparsity penalty term, which can further improve the performance of the proposed algorithm with a sparser representation of the data. The combination of (1) and (2) provides the correntropy-based ELM autoencoder. (3) The FC-MELM is proposed by using the correntropy-based ELM autoencoder as a building block. It is notable that the FC-MELM is trained in a forward manner, which means fine-tuning procedure is not required. Thus, the FC-MELM has great advantage in learning efficiently when compared with traditional deep learning algorithms. The good property of the proposed algorithm is confirmed by the experiments on well-known benchmark datasets, including the MNIST datasets, the NYU Object Recognition Benchmark dataset, and the Moore network traffic dataset. Finally, the proposed FC-MELM algorithm is applied to address Computer Aided Cancer Diagnosis. Experiments conducted on the well-known Wisconsin Breast Cancer Data (Diagnostic) dataset are presented and show that the proposed FC-MELM outperforms state-of-the-art methods in solving computer aided cancer diagnosis problems.

Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data

With the advancement of genome-wide monitoring technologies, molecular expression data have become widely used for diagnosing cancer through tumor or blood samples. When mining molecular signature data, the process of comparing samples through an adaptive distance function is fundamental but difficult, as such datasets are normally heterogeneous and high dimensional. In this article, we present kernelized information-theoretic metric learning (KITML) algorithms that optimize a distance function to tackle the cancer diagnosis problem and scale to high dimensionality. By learning a nonlinear transformation in the input space implicitly through kernelization, KITML permits efficient optimization, low storage, and improved learning of distance metric. We propose two novel applications of KITML for diagnosing cancer using high-dimensional molecular profiling data: (1) for sample-level cancer diagnosis, the learned metric is used to improve the performance of k -nearest neighbor classification; and (2) for estimating the severity level or stage of a group of samples, we propose a novel set-based ranking approach to extend KITML. For the sample-level cancer classification task, we have evaluated on 14 cancer gene microarray datasets and compared with eight other state-of-the-art approaches. The results show that our approach achieves the best overall performance for the task of molecular-expression-driven cancer sample diagnosis. For the group-level cancer stage estimation, we test the proposed set-KITML approach using three multi-stage cancer microarray datasets, and correctly estimated the stages of sample groups for all three studies.

Nonparametric techniques to extract fuzzy rules for breast cancer diagnosis problem.

This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is constructed around each prototype. Finally, these clusters are interpreted as the fuzzy rules that relate the prognostic factors and the diagnosis results. The advantages of the proposed algorithm are, (a) there is no need to know the structure of the training data, (b) the number of fuzzy rules does not increase with the increase of the number of input dimensions, and (c) small number of fuzzy rules is generated. With the three generated fuzzy rules, 96.20% classification efficiency is achieved, which is comparable to other rule generation techniques.

Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis

The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Breast cancer is becoming a leading cause of death among women in the whole world; meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This paper presents a hybrid intelligent system for recognition of breast cancer tumors. The proposed system includes two main modules: the feature extraction module and the predictor module. In the feature extraction module, rough set theory is used to preprocess the attributes on condition that the important information is not lost, deletes redundant attributes and conflicting objects from decision table. In the predictor module, a combined classifier is proposed based on K-nearest neighbor classifier. Experiments have been conducted on a widely used Wisconsin breast cancer dataset taken from University of California Irvine. Experimental results show that the proposed hybrid system can improve the rate of correct diagnosis of cases. The proposed combined classifier with rough set-based feature selection achieves 99.41 % classification accuracy and uses only 4 features which is the best shown to date. Different performance metrics are used to show the effectiveness of the proposed hybrid system. With these results, the proposed method is very promising compared to the previously reported results and can be used confidently for other breast cancer diagnosis problems.

Diverse accurate feature selection for microarray cancer diagnosis

Gene expression microarray data provides simultaneous activity measurement of thousands of features facilitating a potential effective and reliable cancer diagnosis. An important and challenging task in microarray analysis refers to selecting the most relevant and significant genes for data cancer classification. A random subspace ensemble based method is proposed to address feature selection in gene expression cancer diagnosis. The introduced Diverse Accurate Feature Selection method relies on multiple individual classifiers built based on random feature subspaces. Each feature is assigned a score computed based on the pairwise diversity among individual classifiers and the ratio between individual and ensemble accuracies. This triggers the creation of a ranked list of features for which a final classifier is applied with an increased performance using minimum possible number of genes. Experimental results focus on the problem of gene expression cancer diagnosis based on microarray datasets publicly available. Numerical results show that the proposed method is competitive with related models from literature.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Smoothed log-concave maximum likelihood estimation with applications

We study the smoothed log-concave maximum likelihood estimator of a probability distribution on $\mathbb{R}^d$. This is a fully automatic nonparametric density estimator, obtained as a canonical smoothing of the log-concave maximum likelihood estimator. We demonstrate its attractive features both through an analysis of its theoretical properties and a simulation study. Moreover, we use our methodology to develop a new test of log-concavity, and show how the estimator can be used as an intermediate stage of more involved procedures, such as constructing a classifier or estimating a functional of the density. Here again, the use of these procedures can be justified both on theoretical grounds and through its finite sample performance, and we illustrate its use in a breast cancer diagnosis (classification) problem.

Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic

As the incidence of this disease has increased significantly in the recent years, expert systems and machine learning techniques to this problem have also taken a great attention from many scholars. This study aims at diagnosing and prognosticating breast cancer with a machine learning method based on random forest classifier and feature selection technique. By weighting, keeping useful features and removing redundant features in datasets, the method was obtained to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer Prognostic Dataset. On these datasets we obtained classification accuracy of 100% in the best case and of around 99.8% on average. This is very promising compared to the previously reported results. This result is for Wisconsin Breast Cancer Dataset but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

How do you feel about fertility and parenthood? The voices of young female cancer survivors

Young adult cancer survivors are often unaware of their fertility status and uninformed regarding their fertility and fertility preservation options. This qualitative research study explores the fertility and parenthood concerns of reproductive-age female cancer survivors and how they make parenthood decisions. Population- and clinic-based recruitment methods were used to identify a diverse group of survivors between the ages of 18 and 34years. Our final sample size included 22 participants who attended one of seven focus groups. We used cross-case, inductive analysis to identify themes. The following main themes were identified: (1) A hopeful but worried approach to fertility and parenthood, (2) Frustration with a lack of choice or control over fertility, (3) Young survivors want information about their fertility, (4) Young survivors want better continuity of care in survivorship, (5) Cancer diagnosis and related fertility problems introduce relationship challenges, and (6) Decisions about parenthood are complicated. The diverse group of young cancer survivors in this study identified several common needs and concerns regarding fertility and parenthood. This study illustrates that young survivors could benefit from improved information regarding their fertility and parenthood options throughout survivorship, better coordination of medical care, and support navigating many emotional and practical issues that arise when considering their reproductive and parenthood options.

Application of Neural Networks in Diagnosing Cancer Disease using Demographic Data

Artificial Neural Network is a branch of Artificial intelligence, has been accepted as a new technology in computer science. Neural Networks are currently a 'hot' research area in medicine, particularly in the fields of radiology, urology, cardiology, oncology and etc. It has a huge application in many areas such as education, business; medical, engineering and manufacturing .Neural Network plays an important role in a decision support system. In this paper, an attempt has been made to make use of neural networks in the medical field (carcinogenesis (pre-clinical study)). In carcinogenesis, artificial neural networks have been successfully applied to the problems in both pre-clinical and post-clinical diagnosis. The main aim of research in medical diagnostics is to develop more cost-effective and easy–to-use systems, procedures and methods for supporting clinicians. It has been used to analyze demographic data from lung cancer patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. For the lung cancer diagnosis problem, the concise rules extracted from the network achieve an high accuracy rate of on the training data set and on the test data set.

An Immuno-Genetic Hybrid Algorithm

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

Breast cancer diagnosis using an artificial neural network trained by group search optimizer

This paper presents a novel optimization algorithm: a group search optimizer (GSO) for training an artificial neural network (ANN) used for diagnosis of breast cancer. The GSO is inspired by animal social searching behaviour. Its global search performance has been proved competitive to other evolutionary algorithms and the particle swarm optimizer. The parameters of a three-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Wisconsin diagnostic breast cancer data from the UCI Machine Learning repository are employed as a benchmark classification problem to evaluate the proposed method. In comparison with other sophisticated machine learning techniques used for ANN training, including some ANN ensembles, the GSO for ANN, GSOANN, has a better convergence rate and generalization performances for the breast cancer diagnosis problem.

Problems in pathologic diagnosis of breast cancer

随着乳腺癌临床诊治技术的迅速发展,乳腺临床专科医师已经开始呼吁,改进病理诊断的模式,建立乳腺专科病理诊断机制[1].目前国内乳腺癌的病理诊断和临床要求相比,明显处于滞后状态,亟待改进和规范.在新形势下,病理工作者应当密切关注下述乳腺癌病理诊断中的问题。

Bagging Linear Sparse Bayesian Learning Models for Variable Selection in Cancer Diagnosis

This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction.

1. Present Conditions and Problems in Gene Diagnosis of Colorectal Cancer

1. Present Conditions and Problems in Gene Diagnosis of Colorectal Cancer