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Wang TW, Hong JS, Huang JW, Liao CY, Lu CF, Wu YT. Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol 2024; 197:110344. [PMID: 38806113 DOI: 10.1016/j.radonc.2024.110344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan.
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Xuan P, Chu X, Cui H, Nakaguchi T, Wang L, Ning Z, Ning Z, Li C, Zhang T. Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images. Comput Biol Med 2024; 177:108640. [PMID: 38833798 DOI: 10.1016/j.compbiomed.2024.108640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/25/2024] [Accepted: 05/18/2024] [Indexed: 06/06/2024]
Abstract
Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.
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Affiliation(s)
- Ping Xuan
- Department of Computer Science and Technology, Shantou University, Shantou, China; School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Xiuqiang Chu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Linlin Wang
- Department of Radiation Oncology, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhiyuan Ning
- School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia
| | - Zhiyu Ning
- School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia
| | | | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China; School of Mathematical Science, Heilongjiang University, Harbin, China.
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Siddiqui EA, Chaurasia V, Shandilya M. Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter. J Cancer Res Clin Oncol 2023; 149:11279-11294. [PMID: 37368121 DOI: 10.1007/s00432-023-04992-9] [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: 05/03/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023]
Abstract
Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.
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Affiliation(s)
| | | | - Madhu Shandilya
- Maulana Azad National Institute of Technology, Bhopal, 462003, India
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Chen Y, Zhang X, Li D, Park H, Li X, Liu P, Jin J, Shen Y. Automatic segmentation of thyroid with the assistance of the devised boundary improvement based on multicomponent small dataset. APPL INTELL 2023; 53:1-16. [PMID: 37363389 PMCID: PMC10015528 DOI: 10.1007/s10489-023-04540-5] [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] [Accepted: 02/23/2023] [Indexed: 03/17/2023]
Abstract
Deep learning has been widely considered in medical image segmentation. However, the difficulty of acquiring medical images and labels can affect the accuracy of the segmentation results for deep learning methods. In this paper, an automatic segmentation method is proposed by devising a multicomponent neighborhood extreme learning machine to improve the boundary attention region of the preliminary segmentation results. The neighborhood features are acquired by training U-Nets with the multicomponent small dataset, which consists of original thyroid ultrasound images, Sobel edge images and superpixel images. Afterward, the neighborhood features are selected by min-redundancy and max-relevance filter in the designed extreme learning machine, and the selected features are used to train the extreme learning machine to obtain supplementary segmentation results. Finally, the accuracy of the segmentation results is improved by adjusting the boundary attention region of the preliminary segmentation results with the supplementary segmentation results. This method combines the advantages of deep learning and traditional machine learning, boosting the accuracy of thyroid segmentation accuracy with a small dataset in a multigroup test.
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Affiliation(s)
- Yifei Chen
- Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China
- Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141 Korea
| | - Xin Zhang
- Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - Dandan Li
- Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - HyunWook Park
- Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141 Korea
| | - Xinran Li
- Mathematics, Harbin Institute of Technology, Harbin, 150001 China
| | - Peng Liu
- Heilongjiang Provincial Key Laboratory of Trace Elements and Human Health, Harbin Medical University, Harbin, 150081 China
| | - Jing Jin
- Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China
| | - Yi Shen
- Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001 China
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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
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Wang L. Deep Learning Techniques to Diagnose Lung Cancer. Cancers (Basel) 2022; 14:cancers14225569. [PMID: 36428662 PMCID: PMC9688236 DOI: 10.3390/cancers14225569] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.
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Affiliation(s)
- Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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Xuan P, Jiang B, Cui H, Jin Q, Cheng P, Nakaguchi T, Zhang T, Li C, Ning Z, Guo M, Wang L. Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107147. [PMID: 36206688 DOI: 10.1016/j.cmpb.2022.107147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging. METHODS We propose a new segmentation method, PRCS, by learning and integrating multi-channel contextual relations, and spatial and position dependencies across image regions. Firstly, to extract contextual relationships between different deep image feature tensor channels, we propose a new convolutional bi-directional gated recurrent unit based module for forward and backward learning. Secondly, a novel cross-channel region-level attention mechanism is proposed to discriminate the contributions of different local regions and associated features in the global learning process. Finally, spatial and position dependencies are formulated by a new position-enhanced self-attention mechanism. The new attention can measure the diverse contributions of other positions to a target position and obtain an enhanced adaptive feature vector for the target position. RESULTS Our model outperformed seven state-of-the-art segmentation methods on both public and in-house lung tumor datasets in terms of spatial overlapping and shape similarity. Ablation study results proved the effectiveness of three technical innovations and generalization ability on different 3D CNN segmentation backbones. CONCLUSION The proposed model enhanced the learning and propagation of contextual, spatial and position relations in 3D volumes, improving lung tumours' segmentation performance with large variations and indistinct boundaries. PRCS provides an effective automated approach to support precision diagnosis and treatment planning of lung cancer.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China; Department of Computer Science, School of Engineering, Shantou University, Shantou, China
| | - Bin Jiang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Qiangguo Jin
- School of Software, Northwestern Polytechnical University, Xi' an, China
| | - Peng Cheng
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin, China.
| | | | - Zhiyu Ning
- Sydney Polytechnic Institute, Sydney, Australia
| | | | - Linlin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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9
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Kazemi A, Shiri ME, Sheikhahmadi A, khodamoradi M. Classifying tumor brain images using parallel deep learning algorithms. Comput Biol Med 2022; 148:105775. [DOI: 10.1016/j.compbiomed.2022.105775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/01/2022] [Accepted: 06/19/2022] [Indexed: 11/28/2022]
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10
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Wang Y, Cai H, Pu Y, Li J, Yang F, Yang C, Chen L, Hu Z. The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer. FRONTIERS IN RADIOLOGY 2022; 2:810731. [PMID: 37492685 PMCID: PMC10365105 DOI: 10.3389/fradi.2022.810731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/30/2022] [Indexed: 07/27/2023]
Abstract
Malignant tumors is a serious public health threat. Among them, lung cancer, which has the highest fatality rate globally, has significantly endangered human health. With the development of artificial intelligence (AI) and its integration with medicine, AI research in malignant lung tumors has become critical. This article reviews the value of CAD, computer neural network deep learning, radiomics, molecular biomarkers, and digital pathology for the diagnosis, treatment, and prognosis of malignant lung tumors.
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Affiliation(s)
- Yue Wang
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Haihua Cai
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yongzhu Pu
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jindan Li
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Fake Yang
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Conghui Yang
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Long Chen
- Department of PET/CT Center, Cancer Center of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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