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Islam R, Imran A, Rabbi MF. Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning. Prostate Cancer 2024; 2024:1588891. [PMID: 38783970 PMCID: PMC11115994 DOI: 10.1155/2024/1588891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/19/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024] Open
Abstract
Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models' efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study's results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest's classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.
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Affiliation(s)
- Rafiqul Islam
- Department of IoT and Robotics Engineering, Bangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh
| | - Al Imran
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
| | - Md. Fazle Rabbi
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
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Li L, Yang J, Por LY, Khan MS, Hamdaoui R, Hussain L, Iqbal Z, Rotaru IM, Dobrotă D, Aldrdery M, Omar A. Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques. Heliyon 2024; 10:e26192. [PMID: 38404820 PMCID: PMC10884486 DOI: 10.1016/j.heliyon.2024.e26192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/30/2024] [Accepted: 02/08/2024] [Indexed: 02/27/2024] Open
Abstract
Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
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Affiliation(s)
- Liangyu Li
- Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
- Health Informatics Laboratory, Cancer Research Institute, Chifeng Cancer Hospital (Second Affiliated Hospital of Chifeng University), Medical Department, Chifeng University, Chifeng City, Inner Mongolia Autonomous Region, 024000, China
| | - Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lip Yee Por
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Mohammad Shahbaz Khan
- Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, United States
| | - Rim Hamdaoui
- Department of Computer Science, College of Science and Human Studies Dawadmi, Shaqra University, Shaqra, Riyadh, Saudi Arabia
| | - Lal Hussain
- Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
- Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
| | - Zahoor Iqbal
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
| | - Ionela Magdalena Rotaru
- Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
| | - Dan Dobrotă
- Faculty of Engineering, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania
| | - Moutaz Aldrdery
- Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia
| | - Abdulfattah Omar
- Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Saudi Arabia
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Rathore S, Iftikhar MA, Chaddad A, Singh A, Gillani Z, Abdulkadir A. Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107812. [PMID: 37757566 DOI: 10.1016/j.cmpb.2023.107812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 05/14/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy. PURPOSE To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients. MATERIALS AND METHODS Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983-2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS. RESULTS The average age of patients was 51.2years (women: n = 77, age-range=18-84years; men: n = 83, age-range=21-80years). The median OS of the participants was 494.5 (range,3-4752), 481 (range,7-4752), and 524.5 days (range,3-2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87). CONCLUSION The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.
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Affiliation(s)
- Saima Rathore
- AVID Radiopharmaceuticals, Philadelphia, PA, USA; Eli Lilly and Company, Indianapolis, IN, USA.
| | | | - Ahmad Chaddad
- School of Artificial Intelligence, GUET, Guilin, China
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zeeshan Gillani
- Comsats University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Ahmed Abdulkadir
- Center for Research in Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Artificial Intelligence, Zurich University of Applied Sciences, Winterthur, ZH, Switzerland
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da Silva HEC, Santos GNM, Leite AF, Mesquita CRM, Figueiredo PTDS, Stefani CM, de Melo NS. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. PLoS One 2023; 18:e0292063. [PMID: 37796946 PMCID: PMC10553229 DOI: 10.1371/journal.pone.0292063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND AND PURPOSE In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients. DATA SOURCES The acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately. RESULTS In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis. CONCLUSIONS The detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems. TRIAL REGISTRATION Systematic review registration. Prospero registration number: CRD42022307403.
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Affiliation(s)
| | | | - André Ferreira Leite
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | | | | | - Cristine Miron Stefani
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
| | - Nilce Santos de Melo
- Faculty of Health Science, Dentistry of Department, Brasilia University, Brasilia, Brazil
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Mokoatle M, Marivate V, Mapiye D, Bornman R, Hayes VM. A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. BMC Bioinformatics 2023; 24:112. [PMID: 36959534 PMCID: PMC10037872 DOI: 10.1186/s12859-023-05235-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/17/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. METHODS In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. RESULTS The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.
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Affiliation(s)
- Mpho Mokoatle
- Department of Computer Science, University of Pretoria, Pretoria, South Africa.
| | - Vukosi Marivate
- Department of Computer Science, University of Pretoria, Pretoria, South Africa
| | | | - Riana Bornman
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - Vanessa M Hayes
- School of Medical Sciences, The University of Sydney, Sydney, Australia
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
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Yang J, Yee PL, Khan AA, Karamti H, Eldin ET, Aldweesh A, Jery AE, Hussain L, Omar A. Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features. Digit Health 2023; 9:20552076231172632. [PMID: 37256015 PMCID: PMC10226179 DOI: 10.1177/20552076231172632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/01/2023] Open
Abstract
Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.
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Affiliation(s)
- Jing Yang
- Faculty of Computer Science and
Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Por Lip Yee
- Faculty of Computer Science and
Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Abdullah Ayub Khan
- Department of Computer Science and
Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi,
Pakistan
| | - Hanen Karamti
- Department of Computer Sciences,
College of Computer and Information Sciences, Princess Nourah bint Abdulrahman
University, Riyadh, Saudi Arabia
| | - Elsayed Tag Eldin
- Faculty of Engineering and Technology, Future University in Egypt, New Cairo, Cairo, Egypt
| | - Amjad Aldweesh
- College of Computer Science and
Information Technology, Shaqra University, Shaqra, Saudi Arabia
| | - Atef El Jery
- Department of Chemical Engineering,
College of Engineering, King Khalid University, Abha, Saudi Arabia
- National Engineering School of Gabes,
Gabes University, Zrig Gabes, Tunisia
| | - Lal Hussain
- Department of Computer Science and
Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu
and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
- Department of Computer Science and
Information Technology, University of Azad Jammu and Kashmir, Athmuqam, Azad
Kashmir, Pakistan
| | - Abdulfattah Omar
- Department of English, College of
Science & Humanities, Prince Sattam Bin Abdulaziz
University, Al-Kharj, Saudi Arabia
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Ramamurthy K, Varikuti AR, Gupta B, Aswani N. A deep learning network for Gleason grading of prostate biopsies using EfficientNet. BIOMED ENG-BIOMED TE 2022; 68:187-198. [PMID: 36332194 DOI: 10.1515/bmt-2022-0201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
Abstract
Objectives
The most crucial part in the diagnosis of cancer is severity grading. Gleason’s score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.
Methods
In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.
Result
To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.
Conclusions
The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.
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Affiliation(s)
- Karthik Ramamurthy
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
| | - Abinash Reddy Varikuti
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Bhavya Gupta
- School of Computer Science Engineering, Vellore Institute of Technology , Chennai , India
| | - Nehal Aswani
- School of Electronics Engineering, Vellore Institute of Technology , Chennai , India
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Hussain L, Malibari AA, Alzahrani JS, Alamgeer M, Obayya M, Al-Wesabi FN, Mohsen H, Hamza MA. Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI. Sci Rep 2022; 12:15389. [PMID: 36100621 PMCID: PMC9470580 DOI: 10.1038/s41598-022-19563-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractAccurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
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Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.
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A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.
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Ayyad SM, Badawy MA, Shehata M, Alksas A, Mahmoud A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. A New Framework for Precise Identification of Prostatic Adenocarcinoma. SENSORS 2022; 22:s22051848. [PMID: 35270995 PMCID: PMC8915102 DOI: 10.3390/s22051848] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 02/01/2023]
Abstract
Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.
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Affiliation(s)
- Sarah M. Ayyad
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Mohamed A. Badawy
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohamed Shehata
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ahmed Alksas
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Ali Mahmoud
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt; (M.A.B.); (M.A.E.-G.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;
| | - Nahla B. Abdel-Hamid
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Labib M. Labib
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - H. Arafat Ali
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
- Faulty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35516, Egypt
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.A.); (A.M.)
- Correspondence:
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Imaging of Neuroendocrine Prostatic Carcinoma. Cancers (Basel) 2021; 13:cancers13225765. [PMID: 34830919 PMCID: PMC8616225 DOI: 10.3390/cancers13225765] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 12/27/2022] Open
Abstract
Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that typically has a high metastatic potential and poor prognosis in comparison to the adenocarcinoma subtype. Although it can arise de novo, NEPC much more commonly occurs as a mechanism of treatment resistance during therapy for conventional prostatic adenocarcinoma, the latter is also termed as castration-resistant prostate cancer (CRPC). The incidence of NEPC increases after hormonal therapy and they represent a challenge, both in the radiological and pathological diagnosis, as well as in the clinical management. This article provides a comprehensive imaging review of prostatic neuroendocrine tumors.
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Hameed BMZ, Prerepa G, Patil V, Shekhar P, Zahid Raza S, Karimi H, Paul R, Naik N, Modi S, Vigneswaran G, Prasad Rai B, Chłosta P, Somani BK. Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future. Ther Adv Urol 2021; 13:17562872211044880. [PMID: 34567272 PMCID: PMC8458681 DOI: 10.1177/17562872211044880] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 08/21/2021] [Indexed: 12/29/2022] Open
Abstract
Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.
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Affiliation(s)
- B M Zeeshan Hameed
- Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
| | - Gayathri Prerepa
- Department of Electronics and Communication, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Vathsala Patil
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Pranav Shekhar
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Syed Zahid Raza
- Department of Urology, Dr. B.R. Ambedkar Medical College, Bengaluru, Karnataka, India
| | - Hadis Karimi
- Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Rahul Paul
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nithesh Naik
- International Training and Research in Uro-oncology and Endourology (iTRUE) Group, Manipal, India
| | - Sachin Modi
- Department of Interventional Radiology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Ganesh Vigneswaran
- Department of Interventional Radiology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Bhavan Prasad Rai
- International Training and Research in Uro-oncology and Endourology (iTRUE) Group Manipal, India
| | - Piotr Chłosta
- Department of Urology, Jagiellonian University in Kraków, Kraków, Poland
| | - Bhaskar K Somani
- International Training and Research in Uro-oncology and Endourology (iTRUE) Group, Manipal, India
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14
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Hussain L, Huang P, Nguyen T, Lone KJ, Ali A, Khan MS, Li H, Suh DY, Duong TQ. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. Biomed Eng Online 2021; 20:63. [PMID: 34183038 PMCID: PMC8240261 DOI: 10.1186/s12938-021-00899-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/09/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. Materials and method This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests. Results Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance. Conclusion AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.,Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.,Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA.,Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY, 10467, USA
| | - Pauline Huang
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Tony Nguyen
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Kashif J Lone
- Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
| | - Amjad Ali
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Muhammad Salman Khan
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine At Stony, Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Doug Young Suh
- College of Electronics and Convergence Engineering, Kyung Hee University, Seoul, South Korea.
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY, 10467, USA
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15
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A Multi-Channel and Multi-Spatial Attention Convolutional Neural Network for Prostate Cancer ISUP Grading. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11104321] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathological diagnosis. Deep learning (DL) is able to automatically extract features from whole-slide images of prostate biopsies annotated by skilled pathologists and to classify the severity of PCa. A whole-slide image of biopsies has many irrelevant features that weaken the performance of DL models. To enable DL models to focus more on cancerous tissues, we propose a Multi-Channel and Multi-Spatial (MCMS) Attention module that can be easily plugged into any backbone CNN to enhance feature extraction. Specifically, MCMS learns a channel attention vector to assign weights to channels in the feature map by pooling from multiple attention branches with different reduction ratios; similarly, it also learns a spatial attention matrix to focus on more relevant areas of the image, by pooling from multiple convolutional layers with different kernel sizes. The model is verified on the most extensive multi-center PCa dataset that consists of 11,000 H&E-stained histopathology whole-slide images. Experimental results demonstrate that an MCMS-assisted CNN can effectively boost prediction performance in accuracy (ACC) and quadratic weighted kappa (QWK), compared with prior studies. The proposed model and results can serve as a credible benchmark for future research in automated PCa grading.
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16
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Anjum S, Hussain L, Ali M, Abbasi AA, Duong TQ. Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2882-2908. [PMID: 33892576 DOI: 10.3934/mbe.2021146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.
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Affiliation(s)
- Sadia Anjum
- Department of IT, Hazara University, Mansehra 21120, KPK, Pakistan
| | - Lal Hussain
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, Pakistan
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, Neelum Campus, Athmuqam 13230, Pakistan
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA
| | - Mushtaq Ali
- Department of IT, Hazara University, Mansehra 21120, KPK, Pakistan
| | - Adeel Ahmed Abbasi
- Department of Computer Science & IT, University of Azad Jammu and Kashmir, King Abdullah Campus, Muzaffarabad 13100, Pakistan
- School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China
| | - Tim Q. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 111 East 210th Street, Bronx, NY 10467, USA
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17
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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18
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Machine learning towards intelligent systems: applications, challenges, and opportunities. Artif Intell Rev 2021. [DOI: 10.1007/s10462-020-09948-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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19
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Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L. Radiomic features for prostate cancer grade detection through formal verification. Radiol Med 2021; 126:688-697. [PMID: 33394366 DOI: 10.1007/s11547-020-01314-8] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 11/16/2020] [Indexed: 02/07/2023]
Abstract
AIM Prostate cancer represents the most common cancer afflicting men. It may be asymptomatic at the early stage. In this paper, we propose a methodology aimed to detect the prostate cancer grade by computing non-invasive shape-based radiomic features directly from magnetic resonance images. MATERIALS AND METHODS We use a freely available dataset composed by coronal magnetic resonance images belonging to 112 patients. We represent magnetic resonance slices in terms of formal model, and we exploit model checking to check whether a set of properties (formulated with the support of pathologists and radiologists) is verified on the formal model. Each property is related to a different cancer grade with the aim to cover all the cancer grade groups. RESULTS An average specificity equal to 0.97 and an average sensitivity equal to 1 have been obtained with our methodology. CONCLUSION The experimental analysis demonstrates the effectiveness of radiomics and formal verification for Gleason grade group detection from magnetic resonance.
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Affiliation(s)
- Antonella Santone
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Federico Donnarumma
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Pasquale Guerriero
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Andrea Giovagnoni
- Department of Radiology, Ospedali Riuniti, Universit Politecnica delle Marche, Ancona, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy
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20
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Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19. ELECTRONIC DEVICES, CIRCUITS, AND SYSTEMS FOR BIOMEDICAL APPLICATIONS 2021. [PMCID: PMC8084755 DOI: 10.1016/b978-0-323-85172-5.00020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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21
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Momenzadeh N, Hafezalseheh H, Nayebpour M, Fathian M, Noorossana R. A hybrid machine learning approach for predicting survival of patients with prostate cancer: A SEER-based population study. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100763] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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22
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Venkatesh V, Anishin Raj MM, Mohamed Sajith K, Anushiadevi R, Suriya Praba T. A precision-based diagnostic model ADOBE-accurate detection of breast cancer using logistic regression approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly.
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Affiliation(s)
- Veeramuthu Venkatesh
- School of Computing SASTRA Deemed University Tirumalaisamudram, Thanjavur, Tamilnadu, India
| | - M. M. Anishin Raj
- CSE, Viswajyothi College of Engineering & Technology, Vazhakulam, Kerala
| | - K. Mohamed Sajith
- School of Computing SASTRA Deemed University Tirumalaisamudram, Thanjavur, Tamilnadu, India
| | - R. Anushiadevi
- School of Computing SASTRA Deemed University Tirumalaisamudram, Thanjavur, Tamilnadu, India
| | - T. Suriya Praba
- School of Computing SASTRA Deemed University Tirumalaisamudram, Thanjavur, Tamilnadu, India
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23
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Hussain L, Nguyen T, Li H, Abbasi AA, Lone KJ, Zhao Z, Zaib M, Chen A, Duong TQ. Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. Biomed Eng Online 2020; 19:88. [PMID: 33239006 PMCID: PMC7686836 DOI: 10.1186/s12938-020-00831-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
- Department of Computer Science and IT, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan.
| | - Tony Nguyen
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Adeel A Abbasi
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
| | - Kashif J Lone
- Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
| | - Zirun Zhao
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Mahnoor Zaib
- Department of Computer Science and IT, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
| | - Anne Chen
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Tim Q Duong
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
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Hussain L, Saeed S, Awan IA, Idris A, Nadeem MSA, Chaudhry QUA. Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies. Curr Med Imaging 2020; 15:595-606. [PMID: 32008569 DOI: 10.2174/1573405614666180718123533] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 05/26/2018] [Accepted: 07/10/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. OBJECTIVE The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. METHODS In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. RESULTS The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). CONCLUSION The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.
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Affiliation(s)
- Lal Hussain
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Sharjil Saeed
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of Computer Sciences & Information Technology, University of Poonch Rawalakot, Rawalakot, Pakistan
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
| | - Qurat-Ul-Ain Chaudhry
- Department of Computer Sciences & Information Technology, University of Azad Jammu and Kashmir, City Campus 13100, Muzaffarabad, Azad Kashmir, Pakistan
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25
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Brunese L, Mercaldo F, Reginelli A, Santone A. Radiomics for Gleason Score Detection through Deep Learning. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5411. [PMID: 32967291 PMCID: PMC7570598 DOI: 10.3390/s20185411] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 09/18/2020] [Indexed: 02/07/2023]
Abstract
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising: they are ranging between 0.96 and 0.98 for Gleason score prediction.
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Affiliation(s)
- Luca Brunese
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
| | - Francesco Mercaldo
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
- Institute for Informatics and Telematics, National Research Council of Italy, 56121 Pisa, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80100 Napoli, Italy;
| | - Antonella Santone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy; (L.B.); (A.S.)
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Livieris I, Pintelas E, Kanavos A, Pintelas P. An Improved Self-Labeled Algorithm for Cancer Prediction. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:331-342. [PMID: 32468549 DOI: 10.1007/978-3-030-32622-7_31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models from different perspectives. The former attempts to achieve strong generalization by using multiple learners, while the latter attempts to achieve strong generalization by exploiting unlabeled data. In this work, we propose an improved semi-supervised self-labeled algorithm for cancer prediction, based on ensemble methodologies. Our preliminary numerical experiments illustrate the efficacy and efficiency of the proposed algorithm, proving that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.
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Affiliation(s)
- Ioannis Livieris
- Department of Mathematics, University of Patras, Patras, Greece.
| | - Emmanuel Pintelas
- Department of Electrical & Computer Engineering, University of Patras, Patras, Greece
| | - Andreas Kanavos
- Department of Mathematics, University of Patras, Patras, Greece.
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Tanos R, Tosato G, Otandault A, Al Amir Dache Z, Pique Lasorsa L, Tousch G, El Messaoudi S, Meddeb R, Diab Assaf M, Ychou M, Du Manoir S, Pezet D, Gagnière J, Colombo P, Jacot W, Assénat E, Dupuy M, Adenis A, Mazard T, Mollevi C, Sayagués JM, Colinge J, Thierry AR. Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2000486. [PMID: 32999827 PMCID: PMC7509651 DOI: 10.1002/advs.202000486] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 05/30/2020] [Indexed: 05/24/2023]
Abstract
While the utility of circulating cell-free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted.
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Abbasi AA, Hussain L, Awan IA, Abbasi I, Majid A, Nadeem MSA, Chaudhary QA. Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodyn 2020; 14:523-533. [PMID: 32655715 PMCID: PMC7334337 DOI: 10.1007/s11571-020-09587-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 01/24/2020] [Accepted: 03/27/2020] [Indexed: 01/20/2023] Open
Abstract
Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.
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Affiliation(s)
- Adeel Ahmed Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Lal Hussain
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Imran Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Abdul Majid
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Malik Sajjad Ahmed Nadeem
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
| | - Quratul-Ain Chaudhary
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100 Pakistan
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Kilic A, Goyal A, Miller JK, Gjekmarkaj E, Tam WL, Gleason TG, Sultan I, Dubrawksi A. Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery. Ann Thorac Surg 2020; 109:1811-1819. [DOI: 10.1016/j.athoracsur.2019.09.049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 08/28/2019] [Accepted: 09/12/2019] [Indexed: 10/25/2022]
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Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 2019; 19:281. [PMID: 31864346 PMCID: PMC6925840 DOI: 10.1186/s12911-019-1004-8] [Citation(s) in RCA: 375] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 12/11/2019] [Indexed: 12/17/2022] Open
Abstract
Background Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. Methods In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. Results We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. Conclusion This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.
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Affiliation(s)
- Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia.
| | - Arif Khan
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia.,Health Market Quality Research Stream, Capital Markets CRC, Level 3, 55 Harrington Street, Sydney, NSW, Australia
| | - Md Ekramul Hossain
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Room 524, SIT Building (J12), Darlington, NSW, 2008, Australia
| | - Mohammad Ali Moni
- Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Camperdown, NSW, 2006, Australia
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31
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Hussain L, Saeed S, Idris A, Awan IA, Shah SA, Majid A, Ahmed B, Chaudhary QA. Regression analysis for detecting epileptic seizure with different feature extracting strategies. BIOMED ENG-BIOMED TE 2019; 64:619-642. [PMID: 31145684 DOI: 10.1515/bmt-2018-0012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 01/08/2019] [Indexed: 11/15/2022]
Abstract
Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Affiliation(s)
- Lal Hussain
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan, E-mail:
| | - Sharjil Saeed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Adnan Idris
- Department of Computer Sciences and Information Technology, The University of Poonch, Rawalakot 12350, Azad Kashmir, Pakistan
| | - Imtiaz Ahmed Awan
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Saeed Arif Shah
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan.,College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
| | - Abdul Majid
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Bilal Ahmed
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
| | - Quratul-Ain Chaudhary
- Department of Computer Sciences and Information Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan
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Bagher-Ebadian H, Janic B, Liu C, Pantelic M, Hearshen D, Elshaikh M, Movsas B, Chetty IJ, Wen N. Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis. Front Oncol 2019; 9:1313. [PMID: 31850209 PMCID: PMC6901911 DOI: 10.3389/fonc.2019.01313] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 11/12/2019] [Indexed: 12/17/2022] Open
Abstract
Purpose: The aim of this study was to identify and rank discriminant radiomics features extracted from MR multi-modal images to construct an adaptive model for characterization of Dominant Intra-prostatic Lesions (DILs) from normal prostatic gland tissues (NT). Methods and Materials: Two cohorts were retrospectively studied: Group A consisted of 98 patients and Group B 19 patients. Two image modalities were acquired using a 3.0T MR scanner: Axial T2 Weighted (T2W) and axial diffusion weighted (DW) imaging. A linear regression method was used to construct apparent diffusion coefficient (ADC) maps from DW images. DILs and the NT in the mirrored location were drawn on each modality. One hundred and sixty-eight radiomics features were extracted from DILs and NT. A Partial-Least-Squares-Correlation (PLSC) with one-way ANOVA along with bootstrapping ratio techniques were recruited to identify and rank the most discriminant latent variables. An artificial neural network (ANN) was constructed based on the optimal latent variable feature to classify the DILs and NTs. Nineteen patients were randomly chosen to test the contour variability effect on the radiomics analysis and the performance of the ANN. Finally, the trained ANN and a two dimension (2D) convolutional sampling method were combined and used to estimate DIL-NT probability map for two test cases. Results: Among 168 radiomics-based latent variables, only the first four variables of each modality in the PLSC space were found to be significantly different between the DILs and NTs. Area Under Receiver Operating Characteristic (AUROC), Positive Predictive and Negative Predictive values (PPV and NPV) for the conventional method were 94%, 0.95, and 0.92, respectively. When the feature vector was randomly permuted 10,000 times, a very strong permutation-invariant efficiency (p < 0.0001) was achieved. The radiomic-based latent variables of the NTs and DILs showed no statistically significant differences (Fstatistic < Fc = 4.11 with Confidence Level of 95% for all 8 variables) against contour variability. Dice coefficients between DIL-NT probability map and physician contours for the two test cases were 0.82 and 0.71, respectively. Conclusion: This study demonstrates the high performance of combining radiomics information extracted from multimodal MR information such as T2WI and ADC maps, and adaptive models to detect DILs in patients with PCa.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Branislava Janic
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Chang Liu
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Milan Pantelic
- Department of Radiology, Henry Ford Health System, Detroit, MI, United States
| | - David Hearshen
- Department of Radiology, Henry Ford Health System, Detroit, MI, United States
| | - Mohamed Elshaikh
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
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Hussain L, Aziz W, Alshdadi AA, Abbasi AA, Majid A, Marchal AR. Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps. Technol Health Care 2019; 28:259-273. [PMID: 31594269 DOI: 10.3233/thc-191803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment. OBJECTIVE In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements. METHODS To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels. RESULTS The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.
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Affiliation(s)
- Lal Hussain
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Wajid Aziz
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan.,College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Abdulrahman A Alshdadi
- College of Computer Sciences and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Adeel Ahmed Abbasi
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Abdul Majid
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
| | - Ali Raza Marchal
- Department of Computer Science and IT, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad 13100, Pakistan
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Taylor J, Meng X, Renson A, Smith AB, Wysock JS, Taneja SS, Huang WC, Bjurlin MA. Different models for prediction of radical cystectomy postoperative complications and care pathways. Ther Adv Urol 2019; 11:1756287219875587. [PMID: 31565072 PMCID: PMC6755632 DOI: 10.1177/1756287219875587] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 08/09/2019] [Indexed: 12/13/2022] Open
Abstract
Background: Radical cystectomy for bladder cancer has one of the highest rates of
morbidity among urologic surgery, but the ability to predict postoperative
complications remains poor. Our study objective was to create machine
learning models to predict complications and factors leading to extended
length of hospital stay and discharge to a higher level of care after
radical cystectomy. Methods: Using the American College of Surgeons National Surgical Quality Improvement
Program, peri-operative adverse outcome variables for patients undergoing
elective radical cystectomy for bladder cancer from 2005 to 2016 were
extracted. Variables assessed include occurrence of minor, infectious,
serious, or any adverse events, extended length of hospital stay, and
discharge to higher-level care. To develop predictive models of radical
cystectomy complications, we fit generalized additive model (GAM), least
absolute shrinkage and selection operator (LASSO) logistic, neural network,
and random forest models to training data using various candidate predictor
variables. Each model was evaluated on the test data using receiver
operating characteristic curves. Results: A total of 7557 patients were identified who met the inclusion criteria, and
2221 complications occurred. LASSO logistic models demonstrated the highest
area under curve for predicting any complications (0.63), discharge to a
higher level of care (0.75), extended length of stay (0.68), and infectious
(0.62) adverse events. This was comparable with random forest in predicting
minor (0.60) and serious (0.63) adverse events. Conclusions: Our models perform modestly in predicting radical cystectomy complications,
highlighting both the complex cystectomy process and the limitations of
large healthcare datasets. Identifying the most important variable leading
to each type of adverse event may allow for further strategies to model
cystectomy complications and target optimization of modifiable variables
pre-operative to reduce postoperative adverse events.
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Affiliation(s)
- Jacob Taylor
- Divison of Urologic Oncology, Department of
Urology, NYU Langone Health, New York, NY, USA
| | - Xiaosong Meng
- Divison of Urologic Oncology, Department of
Urology, NYU Langone Health, New York, NY, USA
| | - Audrey Renson
- Department of Clinical Research, NYU Langone
Hospital, Brooklyn, NY, USA
| | - Angela B. Smith
- Department of Urology, Lineberger Comprehensive
Cancer Center, University of North Carolina at Chapel Hill, NC, USA
| | - James S. Wysock
- Divison of Urologic Oncology, Department of
Urology, NYU Langone Health, New York, NY, USA
| | - Samir S. Taneja
- Divison of Urologic Oncology, Department of
Urology, NYU Langone Health, New York, NY, USA
| | - William C. Huang
- Divison of Urologic Oncology, Department of
Urology, NYU Langone Health, New York, NY, USA
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Clermont PL, Ci X, Pandha H, Wang Y, Crea F. Treatment-emergent neuroendocrine prostate cancer: molecularly driven clinical guidelines. INTERNATIONAL JOURNAL OF ENDOCRINE ONCOLOGY 2019. [DOI: 10.2217/ije-2019-0008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
An increasingly recognized mechanism of prostate cancer resistance is the transdifferentiation from adenocarcinoma to treatment-emergent neuroendocrine prostate cancer (t-NEPC), an extremely aggressive malignancy. The incidence of t-NEPC has been increasing in recent years, in part due to novel treatments that target the androgen receptor pathway. While clinicians historically had very few options for t-NEPC detection and treatment, recent research has uncovered key diagnostic tools and therapeutic targets that can be translated into improved patient care. In this article, we will outline the clinical features of t-NEPC and its molecular pathogenesis. Importantly, we will also discuss recently uncovered molecularly based strategies aimed at improving the diagnosis and treatment of t-NEPC. Finally, we will propose a unified algorithm that integrates clinical and molecular information for the clinical management of t-NEPC.
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Affiliation(s)
- Pier-Luc Clermont
- Department of Medicine, Laval University, Quebec, QB, G1V 0A6, Canada
| | - Xinpei Ci
- Department of Experimental Therapeutics, BC Cancer Research Centre, Vancouver, BC, Canada
- Department of Urology, Vancouver Prostate Centre, University of British Columbia, Vancouver, V5Z 4E6, Canada
| | - Hardev Pandha
- Department of Clinical & Experimental Medicine, Faculty of Health & Medical Science, Leggett Building, Daphne Jackson Road, University of Surrey, Guildford, GU2 7WG, UK
| | - Yuzhuo Wang
- Department of Experimental Therapeutics, BC Cancer Research Centre, Vancouver, BC, Canada
| | - Francesco Crea
- School of Life, Health & Chemical Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK
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Anract J, Duquesne I, Montagne P, Sibony M, Beuvon F, Peyromaure M, Barry Delongchamps N. [Optical coherence tomography of bladder resection specimen]. Prog Urol 2019; 29:449-455. [PMID: 31230855 DOI: 10.1016/j.purol.2019.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/01/2019] [Accepted: 05/18/2019] [Indexed: 11/18/2022]
Abstract
INTRODUCTION The diagnosis of bladder urothelial tumors is based on bladder resection and histological analysis of the specimen. The time to obtain the results of the histological analysis increases the treatment delay. Furthermore, the lack of muscle on the specimen forces the surgeon to practice on other procedure. Full field optical coherence tomography (FFOCT) is a recent imaging technique to analyze tissue. The aim of our study was to evaluate the feasibility and diagnostic accuracy of FFOCT to detect muscle and tumor in bladder resection specimen. PATIENTS AND METHODS We analyzed with the FFOCT technique bladder resection specimen of 24 consecutives patients. Three readers did the blind analyze of the images, looking for the presence of muscle and tumor on each specimen. Their results were compared with histological analysis to calculate diagnostic accuracy for each reader. RESULTS Mean sensibilities for the detection of muscle and tumor were respectively 75% and 81%. Mean specificities for the detection of muscle and tumor were respectively 78.3% and 55.3%. CONCLUSIONS Our results suggest that the FFOCT is feasible to analyze bladder resection specimen. Sensibilities and specificities calculated are encouraging for the detection of muscle and tumor. The accuracy of this detection and early-staging tool should be validated by larger studies. LEVEL OF EVIDENCE 3.
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Affiliation(s)
- J Anract
- Service d'urologie, hôpital Cochin, 163, boulevard de Port-Royal, 75014 Paris, France.
| | - I Duquesne
- Service d'urologie, hôpital Cochin, 163, boulevard de Port-Royal, 75014 Paris, France
| | - P Montagne
- Laboratoire LL-Tech, Pépinière Paris Santé Cochin, 29, rue du Faubourg-Saint-Jacques, 75014 Paris, France
| | - M Sibony
- Service d'anatomopathologie, hôpital Cochin, 27, rue du Faubourg-Saint-Jacques, 75014 Paris, France
| | - F Beuvon
- Service d'anatomopathologie, hôpital Cochin, 27, rue du Faubourg-Saint-Jacques, 75014 Paris, France
| | - M Peyromaure
- Service d'urologie, hôpital Cochin, 163, boulevard de Port-Royal, 75014 Paris, France
| | - N Barry Delongchamps
- Service d'urologie, hôpital Cochin, 163, boulevard de Port-Royal, 75014 Paris, France
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Abstract
Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.
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