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Hung SC, Wang YT, Tseng MH. An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images. Cancers (Basel) 2023; 15:4655. [PMID: 37760624 PMCID: PMC10526230 DOI: 10.3390/cancers15184655] [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: 08/21/2023] [Revised: 09/16/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023] Open
Abstract
Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) computer-aided diagnosis system can be developed for the automatic recognition of CT images of patients, creating a new form of intelligent medical service. For many years, lung cancer has been the leading cause of cancer-related deaths in Taiwan, with smoking and air pollution increasing the likelihood of developing the disease. The incidence of lung adenocarcinoma in never-smoking women has also increased significantly in recent years, resulting in an important public health problem. Early detection of lung cancer and prompt treatment can help reduce the mortality rate of patients with lung cancer. In this study, an improved 3D interpretable hierarchical semantic convolutional neural network named HSNet was developed and validated for the automatic diagnosis of lung cancer based on a collection of lung nodule images. The interpretable AI model proposed in this study, with different training strategies and adjustment of model parameters, such as cyclic learning rate and random weight averaging, demonstrated better diagnostic performance than the previous literature, with results of a four-fold cross-validation procedure showing calcification: 0.9873 ± 0.006, margin: 0.9207 ± 0.009, subtlety: 0.9026 ± 0.014, texture: 0.9685 ± 0.006, sphericity: 0.8652 ± 0.021, and malignancy: 0.9685 ± 0.006.
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Affiliation(s)
- Sheng-Chieh Hung
- Master Program in Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan;
| | - Yao-Tung Wang
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan;
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Ming-Hseng Tseng
- Master Program in Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan;
- Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
- Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan
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2
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Cellina M, Cacioppa LM, Cè M, Chiarpenello V, Costa M, Vincenzo Z, Pais D, Bausano MV, Rossini N, Bruno A, Floridi C. Artificial Intelligence in Lung Cancer Screening: The Future Is Now. Cancers (Basel) 2023; 15:4344. [PMID: 37686619 PMCID: PMC10486721 DOI: 10.3390/cancers15174344] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called "virtual biopsy". This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milano, Italy;
| | - Laura Maria Cacioppa
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Vittoria Chiarpenello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Marco Costa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Zakaria Vincenzo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Daniele Pais
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Maria Vittoria Bausano
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (M.C.); (V.C.); (M.C.); (Z.V.); (D.P.); (M.V.B.)
| | - Nicolò Rossini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
| | - Chiara Floridi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (L.M.C.); (N.R.); (A.B.)
- Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Division of Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
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3
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Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
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Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
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4
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Ewals LJS, van der Wulp K, van den Borne BEEM, Pluyter JR, Jacobs I, Mavroeidis D, van der Sommen F, Nederend J. The Effects of Artificial Intelligence Assistance on the Radiologists' Assessment of Lung Nodules on CT Scans: A Systematic Review. J Clin Med 2023; 12:jcm12103536. [PMID: 37240643 DOI: 10.3390/jcm12103536] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.
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Affiliation(s)
- Lotte J S Ewals
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Kasper van der Wulp
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Ben E E M van den Borne
- Department of Pulmonology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
| | - Jon R Pluyter
- Department of Experience Design, Royal Philips, 5656 AE Eindhoven, The Netherlands
| | - Igor Jacobs
- Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Dimitrios Mavroeidis
- Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Joost Nederend
- Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands
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5
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Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics (Basel) 2022; 12:diagnostics12112644. [PMID: 36359485 PMCID: PMC9689810 DOI: 10.3390/diagnostics12112644] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/26/2022] [Accepted: 10/29/2022] [Indexed: 11/30/2022] Open
Abstract
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.
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6
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Evolving Optimised Convolutional Neural Networks for Lung Cancer Classification. SIGNALS 2022. [DOI: 10.3390/signals3020018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Detecting pulmonary nodules early significantly contributes to the treatment success of lung cancer. Several deep learning models for medical image analysis have been developed to help classify pulmonary nodules. The design of convolutional neural network (CNN) architectures, however, is still heavily reliant on human domain knowledge. Manually designing CNN design solutions has been shown to limit the data’s utility by creating a co-dependency on the creator’s cognitive bias, which urges the development of smart CNN architecture design solutions. In this paper, an evolutionary algorithm is used to optimise the classification of pulmonary nodules with CNNs. The implementation of a genetic algorithm (GA) for CNN architectures design and hyperparameter optimisation is proposed, which approximates optimal solutions by implementing a range of bio-inspired mechanisms of natural selection and Darwinism. For comparison purposes, two manually designed deep learning models, FractalNet and Deep Local-Global Network, were trained. The results show an outstanding classification accuracy of the fittest GA-CNN (91.3%), which outperformed both manually designed models. The findings indicate that GAs pose advantageous solutions for diagnostic challenges, the development of which may to be fully automated in the future using GAs to design and optimise CNN architectures for various clinical applications.
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7
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Silva F, Pereira T, Neves I, Morgado J, Freitas C, Malafaia M, Sousa J, Fonseca J, Negrão E, Flor de Lima B, Correia da Silva M, Madureira AJ, Ramos I, Costa JL, Hespanhol V, Cunha A, Oliveira HP. Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges. J Pers Med 2022; 12:480. [PMID: 35330479 PMCID: PMC8950137 DOI: 10.3390/jpm12030480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 12/15/2022] Open
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and "motivate" the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
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Affiliation(s)
- Francisco Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Inês Neves
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- ICBAS—Abel Salazar Biomedical Sciences Institute, University of Porto, 4050-313 Porto, Portugal
| | - Joana Morgado
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Mafalda Malafaia
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Joana Sousa
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
| | - João Fonseca
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FEUP—Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Eduardo Negrão
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - Isabel Ramos
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - José Luis Costa
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal
- IPATIMUP—Institute of Molecular Pathology and Immunology of the University of Porto, 4200-135 Porto, Portugal
| | - Venceslau Hespanhol
- CHUSJ—Centro Hospitalar e Universitário de São João, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- FMUP—Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
| | - António Cunha
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- UTAD—University of Trás-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal; (I.N.); (J.M.); (M.M.); (J.S.); (J.F.); (A.C.); (H.P.O.)
- FCUP—Faculty of Science, University of Porto, 4169-007 Porto, Portugal
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Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review. Diagnostics (Basel) 2022; 12:diagnostics12020298. [PMID: 35204388 PMCID: PMC8871398 DOI: 10.3390/diagnostics12020298] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 12/26/2022] Open
Abstract
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
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9
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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.
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10
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Mastouri R, Khlifa N, Neji H, Hantous-Zannad S. A bilinear convolutional neural network for lung nodules classification on CT images. Int J Comput Assist Radiol Surg 2020; 16:91-101. [PMID: 33140257 DOI: 10.1007/s11548-020-02283-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/21/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE Lung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images. METHODS Convolutional neural network (CNN) is considered as the leading model in deep learning and is highly recommended for the design of computer-aided diagnosis systems thanks to its promising results on medical image analysis. The proposed BCNN scheme consists of two-stream CNNs (VGG16 and VGG19) as feature extractors followed by a support vector machine (SVM) classifier for false positive reduction. Series of experiments are performed by introducing the bilinear vector features extracted from three BCNN combinations into various types of SVMs that we adopted instead of the original softmax to determine the most suitable classifier for our study. RESULTS The method performance was evaluated on 3186 images from the public LUNA16 database. We found that the BCNN [VGG16, VGG19] combination with and without SVM surpassed the [VGG16]2 and [VGG19]2 architectures, achieved an accuracy rate of 91.99% against 91.84% and 90.58%, respectively, and an area under the curve (AUC) rate of 95.9% against 94.8% and 94%, respectively. CONCLUSION The proposed method improved the outcomes of conventional CNN-based architectures and showed promising and satisfying results, compared to other works, with an affordable complexity. We believe that the proposed BCNN can be used as an assessment tool for radiologists to make a precise analysis of lung nodules and an early diagnosis of lung cancers.
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Affiliation(s)
- Rekka Mastouri
- Higher Institute of Medical Technologies of Tunis, Research Laboratory of Biophysics and Medical Technologies, University of Tunis el Manar, 1006, Tunis, Tunisia.
| | - Nawres Khlifa
- Higher Institute of Medical Technologies of Tunis, Research Laboratory of Biophysics and Medical Technologies, University of Tunis el Manar, 1006, Tunis, Tunisia
| | - Henda Neji
- Faculty of Medicine of Tunis, University of Tunis el Manar, 1007, Tunis, Tunisia.,Medical Imaging Department, Abderrahmen Mami Hospital, 2035, Ariana, Tunisia
| | - Saoussen Hantous-Zannad
- Faculty of Medicine of Tunis, University of Tunis el Manar, 1007, Tunis, Tunisia.,Medical Imaging Department, Abderrahmen Mami Hospital, 2035, Ariana, Tunisia
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