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Chi H, Chen H, Wang R, Zhang J, Jiang L, Zhang S, Jiang C, Huang J, Quan X, Liu Y, Zhang Q, Yang G. Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model. Front Oncol 2023; 13:1244578. [PMID: 37601672 PMCID: PMC10437932 DOI: 10.3389/fonc.2023.1244578] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Background Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. Methods In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Results Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Conclusion Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.
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Affiliation(s)
- Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Haiqing Chen
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Rui Wang
- Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China
- Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
- Academician (Expert) Workstation of Sichuan Province, Luzhou, China
| | - Jieying Zhang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Lai Jiang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Shengke Zhang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Chenglu Jiang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jinbang Huang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Xiaomin Quan
- Beijing University of Chinese Medicine, Beijing, China
- Beijing University of Chinese Medicine Second Affiliated DongFang Hospital, Beijing, China
| | - Yunfei Liu
- Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
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2
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Mridha MF, Prodeep AR, Hoque ASMM, Islam MR, Lima AA, Kabir MM, Hamid MA, Watanobe Y. A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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Affiliation(s)
- M. F. Mridha
- Department of Computer Science and Engineering, American International University Bangladesh, Dhaka 1229, Bangladesh
| | - Akibur Rahman Prodeep
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - A. S. M. Morshedul Hoque
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Rashedul Islam
- Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
| | - Aklima Akter Lima
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Muhammad Mohsin Kabir
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh
| | - Md. Abdul Hamid
- Department of Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yutaka Watanobe
- Department of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
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3
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Pethuraj MS, Aboobaider BBM, Salahuddin LB. Analyzing
CT
images for detecting lung cancer by applying the computational intelligence‐based optimization techniques. Comput Intell 2022. [DOI: 10.1111/coin.12567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5912051. [PMID: 34691378 PMCID: PMC8528612 DOI: 10.1155/2021/5912051] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/27/2021] [Accepted: 09/27/2021] [Indexed: 01/15/2023]
Abstract
Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.
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5
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Zvarevashe K, Olugbara OO. Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm. INTELL DATA ANAL 2020. [DOI: 10.3233/ida-194747] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Speech emotion recognition has become the heart of most human computer interaction applications in the modern world. The growing need to develop emotionally intelligent devices has opened up a lot of research opportunities. Most researchers in this field have applied the use of handcrafted features and machine learning techniques in recognising speech emotion. However, these techniques require extra processing steps and handcrafted features are usually not robust. They are computationally intensive because the curse of dimensionality results in low discriminating power. Research has shown that deep learning algorithms are effective for extracting robust and salient features in dataset. In this study, we have developed a custom 2D-convolution neural network that performs both feature extraction and classification of vocal utterances. The neural network has been evaluated against deep multilayer perceptron neural network and deep radial basis function neural network using the Berlin database of emotional speech, Ryerson audio-visual emotional speech database and Surrey audio-visual expressed emotion corpus. The described deep learning algorithm achieves the highest precision, recall and F1-scores when compared to other existing algorithms. It is observed that there may be need to develop customized solutions for different language settings depending on the area of applications.
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6
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A mapping study of ensemble classification methods in lung cancer decision support systems. Med Biol Eng Comput 2020; 58:2177-2193. [PMID: 32621068 DOI: 10.1007/s11517-020-02223-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 06/25/2020] [Indexed: 10/23/2022]
Abstract
Achieving a high level of classification accuracy in medical datasets is a capital need for researchers to provide effective decision systems to assist doctors in work. In many domains of artificial intelligence, ensemble classification methods are able to improve the performance of single classifiers. This paper reports the state of the art of ensemble classification methods in lung cancer detection. We have performed a systematic mapping study to identify the most interesting papers concerning this topic. A total of 65 papers published between 2000 and 2018 were selected after an automatic search in four digital libraries and a careful selection process. As a result, it was observed that diagnosis was the task most commonly studied; homogeneous ensembles and decision trees were the most frequently adopted for constructing ensembles; and the majority voting rule was the predominant combination rule. Few studies considered the parameter tuning of the techniques used. These findings open several perspectives for researchers to enhance lung cancer research by addressing the identified gaps, such as investigating different classification methods, proposing other heterogeneous ensemble methods, and using new combination rules. Graphical abstract Main features of the mapping study performed in ensemble classification methods applied on lung cancer decision support systems.
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Wang Q, Shen F, Shen L, Huang J, Sheng W. Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network. J Digit Imaging 2019; 32:971-979. [PMID: 31062113 PMCID: PMC6841817 DOI: 10.1007/s10278-019-00221-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.
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Affiliation(s)
- Qin Wang
- Shanghai Jiao Tong University, Shanghai, 201100, China
| | - Fengyi Shen
- Shanghai Jiao Tong University, Shanghai, 201100, China
| | - Linyao Shen
- Shanghai Jiao Tong University, Shanghai, 201100, China
| | - Jia Huang
- Shanghai Chest Hospital, Shanghai, 200030, China
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8
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Xu L, Guo Z, Liu X. Prediction of essential genes in prokaryote based on artificial neural network. Genes Genomics 2019; 42:97-106. [DOI: 10.1007/s13258-019-00884-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Accepted: 10/30/2019] [Indexed: 12/12/2022]
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9
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Moitra D, Mandal RK. Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN). Health Inf Sci Syst 2019; 7:14. [PMID: 31406570 DOI: 10.1007/s13755-019-0077-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Accepted: 07/24/2019] [Indexed: 02/07/2023] Open
Abstract
Purpose A large chunk of lung cancers are of the type non-small cell lung cancer (NSCLC). Both the treatment planning and patients' prognosis depend greatly on factors like AJCC staging which is an abstraction over TNM staging. Many significant efforts have so far been made towards automated staging of NSCLC, but the groundbreaking application of a deep neural networks (DNNs) is yet to be observed in this domain of study. DNN is capable of achieving higher level of accuracy than the traditional artificial neural networks (ANNs) as it uses deeper layers of convolutional neural network (CNN). The objective of the present study is to propose a simple yet fast CNN model combined with recurrent neural network (RNN) for automated AJCC staging of NSCLC and to compare the outcome with a few standard machine learning algorithms along with a few similar studies. Methods The NSCLC radiogenomics collection from the cancer imaging archive (TCIA) dataset was considered for the study. The tumor images were refined and filtered by resizing, enhancing, de-noising, etc. The initial image processing phase was followed by texture based image segmentation. The segmented images were fed into a hybrid feature detection and extraction model which was comprised of two sequential phases: maximally stable extremal regions (MSER) and the speeded up robust features (SURF). After a prolonged experiment, the desired CNN-RNN model was derived and the extracted features were fed into the model. Results The proposed CNN-RNN model almost outperformed the other machine learning algorithms under consideration. The accuracy remained steadily higher than the other contemporary studies. Conclusion The proposed CNN-RNN model performed commendably during the study. Further studies may be carried out to refine the model and develop an improved auxiliary decision support system for oncologists and radiologists.
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10
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Zhang R, Zhao R, Zhao X, Wu D, Zheng W, Feng X, Zhou F. pyHIVE, a health-related image visualization and engineering system using Python. BMC Bioinformatics 2018; 19:452. [PMID: 30477418 PMCID: PMC6258460 DOI: 10.1186/s12859-018-2477-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 11/09/2018] [Indexed: 12/29/2022] Open
Abstract
Background Imaging is one of the major biomedical technologies to investigate the status of a living object. But the biomedical image based data mining problem requires extensive knowledge across multiple disciplinaries, e.g. biology, mathematics and computer science, etc. Results pyHIVE (a Health-related Image Visualization and Engineering system using Python) was implemented as an image processing system, providing five widely used image feature engineering algorithms. A standard binary classification pipeline was also provided to help researchers build data models immediately after the data is collected. pyHIVE may calculate five widely-used image feature engineering algorithms efficiently using multiple computing cores, and also featured the modules of Principal Component Analysis (PCA) based preprocessing and normalization. Conclusions The demonstrative example shows that the image features generated by pyHIVE achieved very good classification performances based on the gastrointestinal endoscopic images. This system pyHIVE and the demonstrative example are freely available and maintained at http://www.healthinformaticslab.org/supp/resources.php.
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Affiliation(s)
- Ruochi Zhang
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China
| | - Ruixue Zhao
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China
| | - Xinyang Zhao
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China
| | - Di Wu
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, 130012, Jilin, China
| | - Weiwei Zheng
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China
| | - Xin Feng
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
| | - Fengfeng Zhou
- BioKnow Health Informatics Lab, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
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11
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Product image classification using Eigen Colour feature with ensemble machine learning. EGYPTIAN INFORMATICS JOURNAL 2018. [DOI: 10.1016/j.eij.2017.10.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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3D multi-view convolutional neural networks for lung nodule classification. PLoS One 2017; 12:e0188290. [PMID: 29145492 PMCID: PMC5690636 DOI: 10.1371/journal.pone.0188290] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 10/11/2017] [Indexed: 12/23/2022] Open
Abstract
The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.
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13
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Adamek M, Wachuła E, Szabłowska-Siwik S, Boratyn-Nowicka A, Czyżewski D. Risk factors assessment and risk prediction models in lung cancer screening candidates. ANNALS OF TRANSLATIONAL MEDICINE 2016; 4:151. [PMID: 27195269 DOI: 10.21037/atm.2016.04.03] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
From February 2015, low-dose computed tomography (LDCT) screening entered the armamentarium of diagnostic tools broadly available to individuals at high-risk of developing lung cancer. While a huge number of pulmonary nodules are identified, only a small fraction turns out to be early lung cancers. The majority of them constitute a variety of benign lesions. Although it entails a burden of the diagnostic work-up, the undisputable benefit emerges from: (I) lung cancer diagnosis at earlier stages (stage shift); (II) additional findings enabling the implementation of a preventive action beyond the realm of thoracic oncology. This review presents how to utilize the risk factors from distinct categories such as epidemiology, radiology and biomarkers to target the fraction of population, which may benefit most from the introduced screening modality.
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Affiliation(s)
- Mariusz Adamek
- 1 The Chair and Department of Thoracic Surgery, The Professor S. Szyszko Teaching Hospital No. 1, Zabrze, Poland ; 2 Department of Clinical Oncology, Medical University of Silesia, Katowice, Poland
| | - Ewa Wachuła
- 1 The Chair and Department of Thoracic Surgery, The Professor S. Szyszko Teaching Hospital No. 1, Zabrze, Poland ; 2 Department of Clinical Oncology, Medical University of Silesia, Katowice, Poland
| | - Sylwia Szabłowska-Siwik
- 1 The Chair and Department of Thoracic Surgery, The Professor S. Szyszko Teaching Hospital No. 1, Zabrze, Poland ; 2 Department of Clinical Oncology, Medical University of Silesia, Katowice, Poland
| | - Agnieszka Boratyn-Nowicka
- 1 The Chair and Department of Thoracic Surgery, The Professor S. Szyszko Teaching Hospital No. 1, Zabrze, Poland ; 2 Department of Clinical Oncology, Medical University of Silesia, Katowice, Poland
| | - Damian Czyżewski
- 1 The Chair and Department of Thoracic Surgery, The Professor S. Szyszko Teaching Hospital No. 1, Zabrze, Poland ; 2 Department of Clinical Oncology, Medical University of Silesia, Katowice, Poland
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Adetiba E, Olugbara OO. Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation. PLoS One 2015; 10:e0143542. [PMID: 26625358 PMCID: PMC4666594 DOI: 10.1371/journal.pone.0143542] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Accepted: 11/05/2015] [Indexed: 11/18/2022] Open
Abstract
Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.
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Affiliation(s)
- Emmanuel Adetiba
- ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Oludayo O. Olugbara
- ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
- * E-mail:
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