1
|
Shivwanshi RR, Nirala N. Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment. Biomed Phys Eng Express 2024; 10:045005. [PMID: 38663368 DOI: 10.1088/2057-1976/ad4360] [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: 02/04/2024] [Accepted: 04/25/2024] [Indexed: 05/08/2024]
Abstract
The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.
Collapse
Affiliation(s)
- Resham Raj Shivwanshi
- Department of Biomedical Engineering, National Institute of Technology Raipur, 49201, India
| | - Neelamshobha Nirala
- Department of Biomedical Engineering, National Institute of Technology Raipur, 49201, India
| |
Collapse
|
2
|
Ren H, Wang Q, Xiao Z, Mo R, Guo J, Hide GR, Tu M, Zeng Y, Ling C, Li P. Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-00967-5. [PMID: 38565729 DOI: 10.1007/s10278-024-00967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/01/2023] [Accepted: 11/14/2023] [Indexed: 04/04/2024]
Abstract
This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.
Collapse
Affiliation(s)
- He Ren
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Qiubo Wang
- Respiratory Department, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhengguang Xiao
- Department of Radiology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Runwei Mo
- School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200030, China
| | - Jiachen Guo
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gareth Richard Hide
- Department of Surgery, Faculty of Health Sciences Medical School, University of the Witwatersrand, Parktown, Johannesburg, South Africa
| | - Mengting Tu
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yanan Zeng
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chen Ling
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Ping Li
- College of Medical Instrumentation and Collaborative Innovation Canter, Shanghai University of Medicine and Health Sciences, Shanghai, China.
| |
Collapse
|
3
|
Quanyang W, Yao H, Sicong W, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shijun Z. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med 2024; 13:e7140. [PMID: 38581113 PMCID: PMC10997848 DOI: 10.1002/cam4.7140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
Collapse
Affiliation(s)
- Wu Quanyang
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Sicong
- Magnetic Resonance Imaging ResearchGeneral Electric Healthcare (China)BeijingChina
| | - Qi Linlin
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| |
Collapse
|
4
|
Zhao X, Li C, Wu J, Li X. Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:2680-2693. [PMID: 35867360 DOI: 10.1109/tnnls.2022.3190836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance the contextual information expression ability. Mapping-based feature spaces aim to preserve the structure information, but the complex computation and the unexplainability of image features have a great impact on their applications. To this end, we propose an explicit feature space called Riemannian manifold feature space (RMFS) to present the contextual information in a unified way. First, the Gaussian probability distribution function (pdf) is introduced to characterize the features of a pixel in its neighborhood system in the image domain. Then, the feature-related pdfs are mapped to a Riemannian manifold, which constructs the proposed RMFS. In RMFS, a point can express the complex contextual information of corresponding pixel in the image domain, and pixels representing the same object are linearly distributed. This gives us a chance to convert nonlinear image segmentation problems to linear computation. To verify the superiority of the expression ability of the proposed RMFS, a linear clustering algorithm and a fuzzy linear clustering algorithm are proposed. Experimental results show that the proposed RMFS-based algorithms outperform their counterparts in the spectral feature space and the RMFS-based ones without the linear distribution characteristics. This indicates that the RMFS can better express features of an image than spectral feature space, and the expressed features can be easily used to construct linear segmentation models.
Collapse
|
5
|
Chang HH, Wu CZ, Gallogly AH. Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:347-362. [PMID: 38343233 DOI: 10.1007/s10278-023-00928-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 03/02/2024]
Abstract
Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.
Collapse
Affiliation(s)
- Herng-Hua Chang
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan.
| | - Cheng-Zhe Wu
- Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan
| | - Audrey Haihong Gallogly
- Department of Radiation Oncology, Keck Medical School, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
6
|
Zhan X, Long H, Gou F, Wu J. A semantic fidelity interpretable-assisted decision model for lung nodule classification. Int J Comput Assist Radiol Surg 2023:10.1007/s11548-023-03043-5. [PMID: 38141069 DOI: 10.1007/s11548-023-03043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023]
Abstract
PURPOSE Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification. METHODS First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model. RESULTS We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules. CONCLUSION The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians' confidence in clinical decision-making.
Collapse
Affiliation(s)
- Xiangbing Zhan
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
| | - Huiyun Long
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC, 3800, Australia.
| |
Collapse
|
7
|
Ma L, Wan C, Hao K, Cai A, Liu L. A novel fusion algorithm for benign-malignant lung nodule classification on CT images. BMC Pulm Med 2023; 23:474. [PMID: 38012620 PMCID: PMC10683224 DOI: 10.1186/s12890-023-02708-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 10/12/2023] [Indexed: 11/29/2023] Open
Abstract
The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.
Collapse
Affiliation(s)
- Ling Ma
- College of Software, Nankai University, Tianjin, 300350, China
| | - Chuangye Wan
- College of Software, Nankai University, Tianjin, 300350, China
| | - Kexin Hao
- College of Software, Nankai University, Tianjin, 300350, China
| | - Annan Cai
- College of Software, Nankai University, Tianjin, 300350, China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
| |
Collapse
|
8
|
Wang Y, Li L, Li C, Xi Y, Lin Y, Wang S. Expert knowledge guided manifold representation learning for magnetic resonance imaging-based glioma grading. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
|
9
|
Shen Z, Cao P, Yang J, Zaiane OR. WS-LungNet: A two-stage weakly-supervised lung cancer detection and diagnosis network. Comput Biol Med 2023; 154:106587. [PMID: 36709519 DOI: 10.1016/j.compbiomed.2023.106587] [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: 09/07/2022] [Revised: 01/13/2023] [Accepted: 01/22/2023] [Indexed: 01/26/2023]
Abstract
Computer-aided lung cancer diagnosis (CAD) system on computed tomography (CT) helps radiologists guide preoperative planning and prognosis assessment. The flexibility and scalability of deep learning methods are limited in lung CAD. In essence, two significant challenges to be solved are (1) Label scarcity due to cost annotations of CT images by experienced domain experts, and (2) Label inconsistency between the observed nodule malignancy and the patients' pathology evaluation. These two issues can be considered weak label problems. We address these issues in this paper by introducing a weakly-supervised lung cancer detection and diagnosis network (WS-LungNet), consisting of a semi-supervised computer-aided detection (Semi-CADe) that can segment 3D pulmonary nodules based on unlabeled data through adversarial learning to reduce label scarcity, as well as a cross-nodule attention computer-aided diagnosis (CNA-CADx) for evaluating malignancy at the patient level by modeling correlations between nodules via cross-attention mechanisms and thereby eliminating label inconsistency. Through extensive evaluations on the LIDC-IDRI public database, we show that our proposed method achieves 82.99% competition performance metric (CPM) on pulmonary nodule detection and 88.63% area under the curve (AUC) on lung cancer diagnosis. Extensive experiments demonstrate the advantage of WS-LungNet on nodule detection and malignancy evaluation tasks. Our promising results demonstrate the benefits and flexibility of the semi-supervised segmentation with adversarial learning and the nodule instance correlation learning with the attention mechanism. The results also suggest that making use of the unlabeled data and taking the relationship among nodules in a case into account are essential for lung cancer detection and diagnosis.
Collapse
Affiliation(s)
- Zhiqiang Shen
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Peng Cao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China.
| | - Jinzhu Yang
- College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Osmar R Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Canada
| |
Collapse
|
10
|
Modak S, Abdel-Raheem E, Rueda L. Applications of Deep Learning in Disease Diagnosis of Chest Radiographs: A Survey on Materials and Methods. BIOMEDICAL ENGINEERING ADVANCES 2023. [DOI: 10.1016/j.bea.2023.100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
|
11
|
A Series-Based Deep Learning Approach to Lung Nodule Image Classification. Cancers (Basel) 2023; 15:cancers15030843. [PMID: 36765801 PMCID: PMC9913559 DOI: 10.3390/cancers15030843] [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: 11/19/2022] [Revised: 01/24/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Although many studies have shown that deep learning approaches yield better results than traditional methods based on manual features, CADs methods still have several limitations. These are due to the diversity in imaging modalities and clinical pathologies. This diversity creates difficulties because of variation and similarities between classes. In this context, the new approach from our study is a hybrid method that performs classifications using both medical image analysis and radial scanning series features. Hence, the areas of interest obtained from images are subjected to a radial scan, with their centers as poles, in order to obtain series. A U-shape convolutional neural network model is then used for the 4D data classification problem. We therefore present a novel approach to the classification of 4D data obtained from lung nodule images. With radial scanning, the eigenvalue of nodule images is captured, and a powerful classification is performed. According to our results, an accuracy of 92.84% was obtained and much more efficient classification scores resulted as compared to recent classifiers.
Collapse
|
12
|
Liu M, Wu J, Wang N, Zhang X, Bai Y, Guo J, Zhang L, Liu S, Tao K. The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis. PLoS One 2023; 18:e0273445. [PMID: 36952523 PMCID: PMC10035910 DOI: 10.1371/journal.pone.0273445] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 02/03/2023] [Indexed: 03/25/2023] Open
Abstract
Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.
Collapse
Affiliation(s)
- Mingsi Liu
- Department of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, China
| | - Jinghui Wu
- College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Nian Wang
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
| | - Xianqin Zhang
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
| | - Yujiao Bai
- School of Basic Medical Sciences, Chengdu Medical College, Chengdu, Sichuan, China
- Non-Coding RNA and Drug Discovery Key Laboratory of Sichuan Province, Chengdu Medical College, Chengdu, Sichuan, China
| | - Jinlin Guo
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lin Zhang
- Department of Pharmacy, Shaoxing people's Hospital, Shaoxing, Zhejiang, China
| | - Shulin Liu
- Department of the First Affiliated Hospital of Chengdu Medical College, Sichuan, China
| | - Ke Tao
- College of Life Science, Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
13
|
Bilal A, Shafiq M, Fang F, Waqar M, Ullah I, Ghadi YY, Long H, Zeng R. IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3. SENSORS (BASEL, SWITZERLAND) 2022; 22:9603. [PMID: 36559970 PMCID: PMC9786099 DOI: 10.3390/s22249603] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/28/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.
Collapse
Affiliation(s)
- Anas Bilal
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
| | - Muhammad Shafiq
- School of Information Engineering, Qujing Normal University, Qujing 655011, China
| | - Fang Fang
- College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou 571126, China
| | - Muhammad Waqar
- Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan
| | - Inam Ullah
- BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju-si 28644, Republic of Korea
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi 64141, United Arab Emirates
| | - Haixia Long
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
| | - Rao Zeng
- College of Information Science and Technology, Hainan Normal University, Haikou 571158, China
| |
Collapse
|
14
|
Grandinetti J, Gao Y, Gonzalez Y, Deng J, Shen C, Jia X. MR image reconstruction from undersampled data for image-guided radiation therapy using a patient-specific deep manifold image prior. Front Oncol 2022; 12:1013783. [PMID: 36479074 PMCID: PMC9720169 DOI: 10.3389/fonc.2022.1013783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/31/2022] [Indexed: 06/13/2024] Open
Abstract
Introduction Recent advancements in radiotherapy (RT) have allowed for the integration of a Magnetic Resonance (MR) imaging scanner with a medical linear accelerator to use MR images for image guidance to position tumors against the treatment beam. Undersampling in MR acquisition is desired to accelerate the imaging process, but unavoidably deteriorates the reconstructed image quality. In RT, a high-quality MR image of a patient is available for treatment planning. In light of this unique clinical scenario, we proposed to exploit the patient-specific image prior to facilitate high-quality MR image reconstruction. Methods Utilizing the planning MR image, we established a deep auto-encoder to form a manifold of image patches of the patient. The trained manifold was then incorporated as a regularization to restore MR images of the same patient from undersampled data. We performed a simulation study using a patient case, a real patient study with three liver cancer patient cases, and a phantom experimental study using data acquired on an in-house small animal MR scanner. We compared the performance of the proposed method with those of the Fourier transform method, a tight-frame based Compressive Sensing method, and a deep learning method with a patient-generic manifold as the image prior. Results In the simulation study with 12.5% radial undersampling and 15% increase in noise, our method improved peak-signal-to-noise ratio by 4.46dB and structural similarity index measure by 28% compared to the patient-generic manifold method. In the experimental study, our method outperformed others by producing reconstructions of visually improved image quality.
Collapse
Affiliation(s)
| | | | | | | | | | - Xun Jia
- Innovative Technology of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| |
Collapse
|
15
|
Chen P, Liu Y, Wen Y, Zhou C. Non-small cell lung cancer in China. Cancer Commun (Lond) 2022; 42:937-970. [PMID: 36075878 PMCID: PMC9558689 DOI: 10.1002/cac2.12359] [Citation(s) in RCA: 145] [Impact Index Per Article: 72.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/21/2022] [Accepted: 08/24/2022] [Indexed: 04/08/2023] Open
Abstract
In China, lung cancer is a primary cancer type with high incidence and mortality. Risk factors for lung cancer include tobacco use, family history, radiation exposure, and the presence of chronic lung diseases. Most early-stage non-small cell lung cancer (NSCLC) patients miss the optimal timing for treatment due to the lack of clinical presentations. Population-based nationwide screening programs are of significant help in increasing the early detection and survival rates of NSCLC in China. The understanding of molecular carcinogenesis and the identification of oncogenic drivers dramatically facilitate the development of targeted therapy for NSCLC, thus prolonging survival in patients with positive drivers. In the exploration of immune escape mechanisms, programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitor monotherapy and PD-1/PD-L1 inhibitor plus chemotherapy have become a standard of care for advanced NSCLC in China. In the Chinese Society of Clinical Oncology's guidelines for NSCLC, maintenance immunotherapy is recommended for locally advanced NSCLC after chemoradiotherapy. Adjuvant immunotherapy and neoadjuvant chemoimmunotherapy will be approved for resectable NSCLC. In this review, we summarized recent advances in NSCLC in China in terms of epidemiology, biology, molecular pathology, pathogenesis, screening, diagnosis, targeted therapy, and immunotherapy.
Collapse
Affiliation(s)
- Peixin Chen
- School of MedicineTongji UniversityShanghai200092P. R. China
- Department of Medical OncologyShanghai Pulmonary HospitalSchool of MedicineTongji UniversityShanghai200433P. R. China
| | - Yunhuan Liu
- Department of Respiratory and Critical Care MedicineHuadong HospitalFudan UniversityShanghai200040P. R. China
| | - Yaokai Wen
- School of MedicineTongji UniversityShanghai200092P. R. China
- Department of Medical OncologyShanghai Pulmonary HospitalSchool of MedicineTongji UniversityShanghai200433P. R. China
| | - Caicun Zhou
- School of MedicineTongji UniversityShanghai200092P. R. China
- Department of Medical OncologyShanghai Pulmonary HospitalSchool of MedicineTongji UniversityShanghai200433P. R. China
| |
Collapse
|
16
|
Benign-malignant classification of pulmonary nodule with deep feature optimization framework. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
17
|
Saberi-Movahed F, Mohammadifard M, Mehrpooya A, Rezaei-Ravari M, Berahmand K, Rostami M, Karami S, Najafzadeh M, Hajinezhad D, Jamshidi M, Abedi F, Mohammadifard M, Farbod E, Safavi F, Dorvash M, Mottaghi-Dastjerdi N, Vahedi S, Eftekhari M, Saberi-Movahed F, Alinejad-Rokny H, Band SS, Tavassoly I. Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods. Comput Biol Med 2022; 146:105426. [PMID: 35569336 PMCID: PMC8979841 DOI: 10.1016/j.compbiomed.2022.105426] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/01/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients' characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.
Collapse
Affiliation(s)
| | | | - Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
| | | | - Kamal Berahmand
- School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
| | - Mehrdad Rostami
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
| | - Saeed Karami
- Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | - Mohammad Najafzadeh
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | | | - Mina Jamshidi
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Farshid Abedi
- Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | | | - Elnaz Farbod
- Baruch College, City University of New York, New York, USA
| | - Farinaz Safavi
- Neuroimmunology and Neurovirology Branch, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, USA
| | - Mohammadreza Dorvash
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Viewbank, VIC, Australia
| | - Negar Mottaghi-Dastjerdi
- Department of Pharmacognosy and Pharmaceutical Biotechnology, School of Pharmacy, Iran University of Medical Sciences, Tehran, Iran
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran,Corresponding author
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029, USA,Corresponding author
| |
Collapse
|
18
|
A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3341924. [PMID: 35782073 PMCID: PMC9249522 DOI: 10.1155/2022/3341924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/28/2022] [Accepted: 06/08/2022] [Indexed: 11/17/2022]
Abstract
Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.
Collapse
|
19
|
Han J, Xiao N, Yang W, Luo S, Zhao J, Qiang Y, Chaudhary S, Zhao J. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg 2022; 17:1049-1057. [PMID: 35445285 PMCID: PMC9020752 DOI: 10.1007/s11548-022-02625-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
Collapse
Affiliation(s)
- Jiahao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shichao Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jun Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
| |
Collapse
|
20
|
Liu D, Liu F, Tie Y, Qi L, Wang F. Res-trans networks for lung nodule classification. Int J Comput Assist Radiol Surg 2022; 17:1059-1068. [PMID: 35290646 DOI: 10.1007/s11548-022-02576-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/02/2022] [Indexed: 12/09/2022]
Abstract
PURPOSE Lung cancer usually presents as pulmonary nodules on early diagnostic images, and accurately estimating the malignancy of pulmonary nodules is crucial to the prevention and diagnosis of lung cancer. Recently, deep learning algorithms based on convolutional neural networks have shown potential for pulmonary nodules classification. However, the size of the nodules is very diverse, ranging from 3 to 30 mm, which makes classifying them to be a challenging task. In this study, we propose a novel architecture called Res-trans networks to classify nodules in computed tomography (CT) scans. METHODS We designed local and global blocks to extract features that capture the long-range dependencies between pixels to adapt to the correct classification of lung nodules of different sizes. Specifically, we designed residual blocks with convolutional operations to extract local features and transformer blocks with self-attention to capture global features. Moreover, the Res-trans network has a sequence fusion block that aggregates and extracts the sequence feature information output by the transformer block that improves classification accuracy. RESULTS Our proposed method is extensively evaluated on the public LIDC-IDRI dataset, which contains 1,018 CT scans. A tenfold cross-validation result shows that our method obtains better performance with AUC = 0.9628 and Accuracy = 0.9292 compared with recently leading methods. CONCLUSION In this paper, a network that can capture local and global features is proposed to classify nodules in chest CT. Experimental results show that our proposed method has better classification performance and can help radiologists to accurately analyze lung nodules.
Collapse
Affiliation(s)
- Dongxu Liu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Fenghui Liu
- Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yun Tie
- School of Information Engineering, Zhengzhou University, Zhengzhou, China.
| | - Lin Qi
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Feng Wang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
21
|
Li C, Wang H, Jiang Y, Fu W, Liu X, Zhong R, Cheng B, Zhu F, Xiang Y, He J, Liang W. Advances in lung cancer screening and early detection. Cancer Biol Med 2022; 19:j.issn.2095-3941.2021.0690. [PMID: 35535966 PMCID: PMC9196057 DOI: 10.20892/j.issn.2095-3941.2021.0690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/03/2022] [Indexed: 11/18/2022] Open
Abstract
Lung cancer is associated with a heavy cancer-related burden in terms of patients' physical and mental health worldwide. Two randomized controlled trials, the US-National Lung Screening Trial (NLST) and Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON), indicated that low-dose CT (LDCT) screening results in a statistically significant decrease in mortality in patients with lung cancer, LDCT has become the standard approach for lung cancer screening. However, many issues in lung cancer screening remain unresolved, such as the screening criteria, high false-positive rate, and radiation exposure. This review first summarizes recent studies on lung cancer screening from the US, Europe, and Asia, and discusses risk-based selection for screening and the related issues. Second, an overview of novel techniques for the differential diagnosis of pulmonary nodules, including artificial intelligence and molecular biomarker-based screening, is presented. Third, current explorations of strategies for suspected malignancy are summarized. Overall, this review aims to help clinicians understand recent progress in lung cancer screening and alleviate the burden of lung cancer.
Collapse
Affiliation(s)
- Caichen Li
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Huiting Wang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Yu Jiang
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Wenhai Fu
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
| | - Xiwen Liu
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Ran Zhong
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Bo Cheng
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
| | - Feng Zhu
- Department of Internal Medicine, Detroit Medical Center Sinai-Grace Hospital, Detroit, Michigan 48235, USA
| | - Yang Xiang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
| | - Jianxing He
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
- Department of Thoracic Surgery, Nanfang Hospital of Southern Medical University, Guangzhou 510515, China
| | - Wenhua Liang
- Department of Thoracic Oncology and Surgery, the First Affiliated Hospital of Guangzhou Medical University, China National Center for Respiratory Medicine, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China
- Dongguan Affiliated Hospital of Southern Medical University, Dongguan People Hospital, Dongguan 523059, China
- Department of Oncology, the First People’s Hospital of Zhaoqing, Zhaoqing 526020, China
| |
Collapse
|
22
|
Kocher MR, Chamberlin J, Waltz J, Snoddy M, Stringer N, Stephenson J, Kahn J, Mercer M, Baruah D, Aquino G, Kabakus I, Hoelzer P, Sahbaee P, Schoepf UJ, Burt JR. Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine. Heliyon 2022; 8:e08962. [PMID: 35243082 PMCID: PMC8873537 DOI: 10.1016/j.heliyon.2022.e08962] [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: 10/08/2021] [Revised: 12/20/2021] [Accepted: 02/11/2022] [Indexed: 12/05/2022] Open
Abstract
Background Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. Objective To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. Methods Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. Results 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07–1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). Conclusion Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. Clinical impact As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes. Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients. AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality. AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients.
Collapse
Affiliation(s)
- Madison R Kocher
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jordan Chamberlin
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeffrey Waltz
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Madalyn Snoddy
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Natalie Stringer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Joseph Stephenson
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jacob Kahn
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Megan Mercer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Dhiraj Baruah
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Gilberto Aquino
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Ismail Kabakus
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | | | | | - U Joseph Schoepf
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeremy R Burt
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| |
Collapse
|
23
|
Shaffie A, Soliman A, Eledkawy A, van Berkel V, El-Baz A. Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy. Cancers (Basel) 2022; 14:cancers14051117. [PMID: 35267425 PMCID: PMC8908987 DOI: 10.3390/cancers14051117] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/11/2022] [Accepted: 02/15/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Lung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan to diagnose the pulmonary nodule as benign or malignant. This system helps in the early detection of the pulmonary nodules and shows its ability to identify the pulmonary nodules precisely. Abstract Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves 92.55%, 91.70%, and 93.40% for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.
Collapse
Affiliation(s)
- Ahmed Shaffie
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (A.S.)
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (A.S.)
| | - Amr Eledkawy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt;
| | - Victor van Berkel
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY 40202, USA;
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (A.S.)
- Correspondence:
| |
Collapse
|
24
|
Zhang C, Wang G, Zhou J, Chen Z. The Influencing Legal and Factors of Migrant Children's Educational Integration Based on Convolutional Neural Network. Front Psychol 2022; 12:762416. [PMID: 35082718 PMCID: PMC8784919 DOI: 10.3389/fpsyg.2021.762416] [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: 08/21/2021] [Accepted: 12/10/2021] [Indexed: 11/13/2022] Open
Abstract
This research aims to analyze the influencing factors of migrant children's education integration based on the convolutional neural network (CNN) algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set (MR), Stanford sentiment data set (SST), and news opinion data set (MPQA) are used to analyze the classification accuracy, loss value, Hamming loss (HL), precision (Pre), recall (Re), and micro-F1 (F1) of the ALGCNN model. Then, on the big data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest (15.2 ± 1.38%), the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children (p < 0.05). The average final score of local children was greatly higher than that of migrant children (p < 0.05), whereas the scores of migrant children's listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model (ALGCNN) is established based on the CNN algorithm. In short, the children's education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children's educational integration, and this work provides a reference for the analysis of migrant children's educational integration.
Collapse
Affiliation(s)
- Chi Zhang
- School of Marxism, Northeast Forestry University, Harbin, China
| | - Gang Wang
- School of Marxism, Northeast Forestry University, Harbin, China
| | - Jinfeng Zhou
- China Biodiversity Conservation and Green Development Foundation, Beijing, China
| | - Zhen Chen
- School of Marxism, Northeastern University, Shenyang, China
| |
Collapse
|
25
|
Zheng B, Yang D, Zhu Y, Liu Y, Hu J, Bai C. 3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT. Med Phys 2021; 48:7826-7836. [PMID: 34655238 DOI: 10.1002/mp.15298] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/13/2021] [Accepted: 09/30/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Early detection is significant to reduce lung cancer-related death. Computer-aided detection system (CADs) can help radiologists to make an early diagnosis. In this paper, we propose a novel 3D gray density coding feature (3D GDC) and fuse it with extracted geometric features. The fusion feature and random forest are used for benign-malignant pulmonary nodule classification on Chest CT. METHODS First, a dictionary model is created to acquire codebook. It is used to obtain feature descriptors and includes 3D block database (BD) and distance matrix clustering centers. 3D BD is balanced and randomly selecting from benign and malignant pulmonary nodules of training data. Clustering centers is got by clustering the distance matrix, which is the distance between every two blocks in 3D BD. Then, feature descriptor is obtained by coding the pulmonary nodule with codebook, and 3D GDC feature is the result of histogram statistics on feature descriptor. Second, geometric features are extracted for fusion feature. Finally, random forest is performed for benign-malignant pulmonary nodule classification with fusion feature of the 3D gray density coding feature and the geometric features. RESULTS We verify the effectiveness of our method on the public LIDC-IDRI dataset and the private ZSHD dataset. For LIDC-IDRI dataset, compared with other state-of-the-art methods, we achieve more satisfactory results with 93.17 ± 1.94% for accuracy and 97.53 ± 1.62% for AUC. As for private ZSHD dataset, it contains a total of 238 lung nodules from 203 patients. The accuracy and AUC achieved by our method are 90.0% and 93.15%. CONCLUSIONS The results show that our method can provide doctors with more accurate results of benign-malignant pulmonary nodule classification for auxiliary diagnosis, and our method is more interpretable than 3D CNN methods, which can provide doctors with more auxiliary information.
Collapse
Affiliation(s)
- BingBing Zheng
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
| | - Yu Zhu
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Yatong Liu
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
| | - Jie Hu
- Department of Pulmonary Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chunxue Bai
- Department of Pulmonary Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
| |
Collapse
|
26
|
Zheng S, Shen Z, Pei C, Ding W, Lin H, Zheng J, Pan L, Zheng B, Huang L. Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106363. [PMID: 34478913 DOI: 10.1016/j.cmpb.2021.106363] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer-aided diagnosis (CAD) systems promote accurate diagnosis and reduce the burden of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography (LDCT) volume to malignant probability, and lacks clinical cognition. METHODS In this paper, we propose a joint radiology analysis and malignancy evaluation network called R2MNet to evaluate pulmonary nodule malignancy via the analysis of radiological characteristics. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping (CDAM) to visualize features and shed light on the decision process of deep neural networks (DNNs). RESULTS Experimental results on the lung image database consortium image collection (LIDC-IDRI) dataset demonstrate that the proposed method achieved an area under curve (AUC) of 96.27% and 97.52% on nodule radiology analysis and nodule malignancy evaluation, respectively. In addition, explanations of CDAM features proved that the shape and density of nodule regions are two critical factors that influence a nodule to be inferred as malignant. This process conforms to the diagnosis cognition of experienced radiologists. CONCLUSION The network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results by incorporating radiology analysis with nodule malignancy evaluation. Besides, model interpretation with CDAM features shed light on the focus regions of DNNs during the estimation of nodule malignancy probabilities.
Collapse
Affiliation(s)
- Shaohua Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Zhiqiang Shen
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Chenhao Pei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Wangbin Ding
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Haojin Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Jiepeng Zheng
- Thoracic Department, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Lin Pan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
| | - Bin Zheng
- Thoracic Department, Fujian Medical University Union Hospital, Fuzhou 350001, China.
| | - Liqin Huang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| |
Collapse
|
27
|
A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage. Phys Med 2021; 90:23-29. [PMID: 34530212 DOI: 10.1016/j.ejmp.2021.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/28/2021] [Accepted: 08/21/2021] [Indexed: 12/17/2022] Open
Abstract
PURPOSE With the future goal of defining a large dataset based on low-dose CT with labelled pulmonary lesions for lung cancer screening (LCS) research, the aim of this work is to propose and evaluate into a clinical context a tool for semi-automatic segmentation able to facilitate the process of labels collection from a LCS study (COSMOS, Continuous Observation of SMOking Subjects). METHODS Considering a preliminary set of manual annotations, a segmentation model based on a 2D-Unet was trained from scratch. Contour quality of the final 2D-Unet was assessed on an internal test set of manual annotations and on a subset of the public available LIDC dataset used as external test set. The tool for semi-automatic segmentation was then designed integrating the tested model into a Graphical User Interface. According to the opinion of two clinical users, the percentage of lesions properly contoured through the tool was quantified (Acceptance Rate, AR). The variability between segmentations derived by the two readers was estimated as mean percentage of difference (MPD) between the two sets of volumes and comparing the likelihood of malignancy derived from Volume Doubling Time (VDT). RESULTS Performance in test sets were found similar (DICE ~ 0.75(0.15)). Accordingly, a good mean AR (80.1%) resulted from the two readers. Variability in terms of MPD was equal to 23.6% while 2.7% was the VDTs percentage of disagreement. CONCLUSIONS A semi-automatic segmentation tool was developed and its applicability evaluated into a clinical context demonstrating the efficacy of the tool in facilitating the collection of labelled data.
Collapse
|
28
|
Saberi-Movahed F, Mohammadifard M, Mehrpooya A, Rezaei-Ravari M, Berahmand K, Rostami M, Karami S, Najafzadeh M, Hajinezhad D, Jamshidi M, Abedi F, Mohammadifard M, Farbod E, Safavi F, Dorvash M, Vahedi S, Eftekhari M, Saberi-Movahed F, Tavassoly I. Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.07.21259699. [PMID: 34268522 PMCID: PMC8282111 DOI: 10.1101/2021.07.07.21259699] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.
Collapse
Affiliation(s)
| | | | - Adel Mehrpooya
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia
| | | | - Kamal Berahmand
- School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane Australia
| | | | - Saeed Karami
- Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | - Mohammad Najafzadeh
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | | | - Mina Jamshidi
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Farshid Abedi
- Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | | | - Elnaz Farbod
- Baruch College, City University of New York, New York, USA
| | - Farinaz Safavi
- Neuroimmunology and Neurovirology Branch, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, Maryland, USA
| | - Mohammadreza Dorvash
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Viewbank, VIC, Australia
| | | | - Mahdi Eftekhari
- Department of Computer Engineering, University of Kerman, Kerman, Iran
| | - Farid Saberi-Movahed
- Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
| | - Iman Tavassoly
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY10029
| |
Collapse
|
29
|
Zhang Y, Chen Y, Li K, Jiang W, Zhang BC. Artificial Intelligence System Application in Miliary Lung Metastasis: Experience from a Rare Case. Risk Manag Healthc Policy 2021; 14:2825-2829. [PMID: 34262369 PMCID: PMC8274706 DOI: 10.2147/rmhp.s315152] [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: 04/13/2021] [Accepted: 06/23/2021] [Indexed: 12/09/2022] Open
Abstract
Introduction Miliary intrapulmonary carcinomatosis (MIPC) is very rare in the existing literature. We reported a lung adenocarcinoma patient presented with over 200 uniform size pulmonary nodules in all lung lobes at the initial examination. The application of artificial intelligence (AI) in lung cancer has been gradually reported, but not yet reported in MIPC. The application of AI in this rare disease is worth exploring. Patient Information A 57-year-old woman received chest computed tomography (CT) scan because of dry cough, intermittent chest wall and back pain for 3 weeks. CT imaging found over 200 uniform size pulmonary nodules in an evenly dispersed pattern at bilateral lungs with a 38×45mm new creature at the dorsal segment of the lower lobe of the left lung. However, as a very reliable diagnostic assistant system in CT imaging of lung cancer, AI can only identify 18 nodules in such classic metastatic lung cancer case. Conclusion This case provides classical imaging figures as textbook-like, even though there is no such classic imaging of lung metastases in the existing textbooks. This medical imaging material will impress medical students and help them learn about the disease deeply. This medical imaging material can warn patients to recognize the horror of lung cancer metastasis and has good popularization of science. This medical imaging material presents a new challenge for AI.
Collapse
Affiliation(s)
- Yu Zhang
- Department of Pathology, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, 430070, People's Republic of China
| | - Yan Chen
- Department of Anesthesiology, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, 430070, People's Republic of China
| | - Kun Li
- Department of Anesthesiology, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, 430070, People's Republic of China
| | - Wen Jiang
- Department of Health Medicine, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, 430070, People's Republic of China
| | - Bi-Cheng Zhang
- Cancer Center, Renmin Hospital of Wuhan University, Wuhan, 430060, People's Republic of China
| |
Collapse
|
30
|
Al-Shabi M, Shak K, Tan M. 3D axial-attention for lung nodule classification. Int J Comput Assist Radiol Surg 2021; 16:1319-1324. [PMID: 34060010 DOI: 10.1007/s11548-021-02415-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 05/20/2021] [Indexed: 11/27/2022]
Abstract
PURPOSE In recent years, Non-Local-based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, they still depend on a convenient local filter such as convolution as full 3D attention is expensive to compute and requires a big dataset, which might not be available. METHODS We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i.e., self-attention). Unlike a regular Non-Local network, the 3D Axial-Attention network applies the attention operation to each axis separately. Additionally, we solve the invariant position problem of the Non-Local network by proposing to add 3D positional encoding to shared embeddings. RESULTS We validated the proposed method on 442 benign nodules and 406 malignant nodules, extracted from the public LIDC-IDRI dataset by following a rigorous experimental setup using only nodules annotated by at least three radiologists. Our results show that the 3D Axial-Attention model achieves state-of-the-art performance on all evaluation metrics, including AUC and Accuracy. CONCLUSIONS The proposed model provides full 3D attention, whereby every element (i.e., pixel) in the 3D volume space attends to every other element in the nodule effectively. Thus, the 3D Axial-Attention network can be used in all layers without the need for local filters. The experimental results show the importance of full 3D attention for classifying lung nodules.
Collapse
Affiliation(s)
- Mundher Al-Shabi
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia.
| | - Kelvin Shak
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| |
Collapse
|
31
|
On the performance of lung nodule detection, segmentation and classification. Comput Med Imaging Graph 2021; 89:101886. [PMID: 33706112 DOI: 10.1016/j.compmedimag.2021.101886] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/11/2021] [Accepted: 02/02/2021] [Indexed: 01/10/2023]
Abstract
Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.
Collapse
|
32
|
Wei L, Owen D, Rosen B, Guo X, Cuneo K, Lawrence TS, Ten Haken R, El Naqa I. A deep survival interpretable radiomics model of hepatocellular carcinoma patients. Phys Med 2021; 82:295-305. [PMID: 33714190 PMCID: PMC8035300 DOI: 10.1016/j.ejmp.2021.02.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 02/13/2021] [Accepted: 02/19/2021] [Indexed: 02/07/2023] Open
Abstract
This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients' electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544-0.621), 0.629 (95%CI: 0.601-0.643), 0.581 (95%CI: 0.553-0.613) and 0.650 (95%CI: 0.635-0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.
Collapse
Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Xinzhou Guo
- Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Randall Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| |
Collapse
|
33
|
Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021; 10:1186-1199. [PMID: 33718055 PMCID: PMC7947422 DOI: 10.21037/tlcr-20-708] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.
Collapse
Affiliation(s)
- Franciszek Binczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Wojciech Prazuch
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Paweł Bozek
- Department of Radiology and Radiodiagnostics, Medical University of Silesia, Katowice, Poland
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| |
Collapse
|
34
|
Abstract
Lung cancer is one of the most common diseases among humans and one of the major causes of growing mortality. Medical experts believe that diagnosing lung cancer in the early phase can reduce death with the illustration of lung nodule through computed tomography (CT) screening. Examining the vast amount of CT images can reduce the risk. However, the CT scan images incorporate a tremendous amount of information about nodules, and with an increasing number of images make their accurate assessment very challenging tasks for radiologists. Recently, various methods are evolved based on handcraft and learned approach to assist radiologists. In this paper, we reviewed different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists' assistance and present the comprehensive analysis of different methods.
Collapse
Affiliation(s)
- Shailesh Kumar Thakur
- Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India.
| | - Dhirendra Pratap Singh
- Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India
| | - Jaytrilok Choudhary
- Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India
| |
Collapse
|
35
|
Shen C, Tsai MY, Chen L, Li S, Nguyen D, Wang J, Jiang SB, Jia X. On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise. Phys Med Biol 2020; 65:245037. [PMID: 33152716 PMCID: PMC7870572 DOI: 10.1088/1361-6560/abc812] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups. The established DNN was able to predict malignancy rate of lung nodules based on CT images, achieving the area under the curve of 0.91 for the testing dataset in a tenfold cross validation as compared to radiologists' prediction. We then evaluated its robustness against noise perturbations. We added to the input CT images noise signals generated randomly or via an optimization scheme using a realistic noise model based on a noise power spectrum for a given mAs level, and monitored the DNN's output. The results showed that the CT noise was able to affect the prediction results of the established DNN model. With random noise perturbations at 100 mAs, DNN's predictions for 11.2% of training data and 17.4% of testing data were successfully altered by at least once. The percentage increased to 23.4% and 34.3%, respectively, for optimization-based perturbations. We further evaluated robustness of models with different architectures, parameters, number of output labels, etc, and robustness concern was found in these models to different degrees. To improve model robustness, we empirically proposed an adaptive training scheme. It fine-tuned the DNN model by including perturbations in the training dataset that successfully altered the DNN's perturbations. The adaptive scheme was repeatedly performed to gradually improve DNN's robustness. The numbers of perturbations at 100 mAs affecting DNN's predictions were reduced to 10.8% for training and 21.1% for testing by the adaptive training scheme after two iterations. Our study illustrated that robustness may potentially be a concern for an exemplary DL-based lung-nodule classification model for CT images, indicating the needs for evaluating and ensuring model robustness when developing similar models. The proposed adaptive training scheme may be able to improve model robustness.
Collapse
Affiliation(s)
- Chenyang Shen
- innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Min-Yu Tsai
- innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Liyuan Chen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Shulong Li
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Jing Wang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Steve B. Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| | - Xun Jia
- innovative Technology Of Radiotherapy Computations and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, University of Texas Southwestern Medical Center, Dallas, TX, 75235
| |
Collapse
|
36
|
Yin P, Mao N, Chen H, Sun C, Wang S, Liu X, Hong N. Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors. Front Oncol 2020; 10:564725. [PMID: 33178593 PMCID: PMC7596901 DOI: 10.3389/fonc.2020.564725] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/21/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. Materials and methods This single-center retrospective analysis included 459 patients with pathologically proven sacral tumors. After semi-automatic segmentation, 1,316 hand-crafted radiomics features of each patient were extracted. All models were built on training set (321 patients) and tested on validation set (138 patients). A DNN model and four machine learning classifiers (logistic regression [LR], random forest [RF], support vector machine [SVM] and k-nearest neighbor [KNN]) based on CT features and clinical characteristics were built, respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results In total, 459 patients (255 males, 204 females; mean age of 42.1 ± 17.8 years, range 4–82 years) were enrolled in this study, including 206 cases of benign tumor and 253 cases of malignant tumor. The sex, age and tumor size had significant differences between the benign tumors and malignant tumors (χ2sex = 10.854, Zage = −6.616, Zsize = 2.843, P < 0.05). The radscore, sex, and age were important indicators for differentiating benign and malignant sacral tumors (odds ratio [OR]1 = 2.492, OR2 = 2.236, OR3 = 1.037, P < 0.01). Among the four clinical-radiomics models (RMs), clinical-LR had the best performance in the validation set (AUC = 0.84, ACC = 0.81). The clinical-DNN model also achieved a high performance (an AUC of 0.83 and an ACC of 0.76 in the validation set) in identifying benign and malignant sacral tumors. Conclusions Both the clinical-LR and clinical-DNN models would have a high impact on assisting radiologists in their clinical diagnosis of sacral tumors.
Collapse
Affiliation(s)
- Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, Beijing Municipality, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Hao Chen
- Department of Radiology, Peking University People's Hospital, Beijing, Beijing Municipality, China
| | - Chao Sun
- Department of Radiology, Peking University People's Hospital, Beijing, Beijing Municipality, China
| | - Sicong Wang
- Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China
| | - Xia Liu
- Department of Radiology, Peking University People's Hospital, Beijing, Beijing Municipality, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, Beijing Municipality, China
| |
Collapse
|
37
|
Abstract
The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.
Collapse
|