1
|
Dong H, Xi Y, Liu K, Chen L, Li Y, Pan X, Zhang X, Ye X, Ding Z. A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study. Eur J Radiol 2024; 176:111532. [PMID: 38820952 DOI: 10.1016/j.ejrad.2024.111532] [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: 01/08/2024] [Revised: 05/14/2024] [Accepted: 05/24/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVE To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.
Collapse
Affiliation(s)
- Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Yuzhen Xi
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yang Li
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xingwei Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - XiaoDan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China.
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
| |
Collapse
|
2
|
Liang B, Tong C, Nong J, Zhang Y. Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01152-4. [PMID: 38861072 DOI: 10.1007/s10278-024-01152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 06/12/2024]
Abstract
Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer.
Collapse
Affiliation(s)
- Baoyu Liang
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China
| | - Chao Tong
- School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, 100191, Beijing, China.
| | - Jingying Nong
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
| | - Yi Zhang
- The Department of Thoracic Surgery, Xuanwu Hospital, Cancer Center of National Clinical Research Center for Geriatric Diseases, Capital Medical University, 45 Changchun Street, Xicheng District, 100053, Beijing, China
| |
Collapse
|
3
|
Li Y, Yu R, Chang H, Yan W, Wang D, Li F, Cui Y, Wang Y, Wang X, Yan Q, Liu X, Jia W, Zeng Q. Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:976-987. [PMID: 38347392 PMCID: PMC11169103 DOI: 10.1007/s10278-024-00988-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 06/13/2024]
Abstract
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
Collapse
Affiliation(s)
- Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruize Yu
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Dawei Wang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China.
| |
Collapse
|
4
|
Nair SS, Devi VM, Bhasi S. Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier. Heliyon 2024; 10:e29032. [PMID: 38617949 PMCID: PMC11015404 DOI: 10.1016/j.heliyon.2024.e29032] [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: 05/08/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/16/2024] Open
Abstract
Background Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.
Collapse
Affiliation(s)
- Sneha S. Nair
- Department of Physics, Noorul Islam Centre for Higher Education, Kumarakovil, Kanyakumari District, Tamil Nadu, India
| | - V.N. Meena Devi
- Department of Physics, Noorul Islam Centre for Higher Education, Kumarakovil, Kanyakumari District, Tamil Nadu, India
| | - Saju Bhasi
- Department of Radiation Physics, Regional Cancer Centre, Thiruvananthapuram, Kerala, India
| |
Collapse
|
5
|
Zhang H, Deng Y, Xiaojie M, Zou Q, Liu H, Tang N, Luo Y, Xiang X. CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy. Heliyon 2024; 10:e23923. [PMID: 38223741 PMCID: PMC10787243 DOI: 10.1016/j.heliyon.2023.e23923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024] Open
Abstract
Objective Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). Methods In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. Results A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. Conclusion CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.
Collapse
Affiliation(s)
- Hanjing Zhang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yu Deng
- The Affiliated Cancer Hospital of Guizhou Medical University, GuiYang, Guizhou Province, 550000, China
| | - M.A. Xiaojie
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Qian Zou
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Huanhui Liu
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Ni Tang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yuanyuan Luo
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Xuejing Xiang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| |
Collapse
|
6
|
Yan D, Zhao Z, Duan J, Qu J, Shi L, Wang Q, Zhang H. Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications. Front Oncol 2024; 13:1263685. [PMID: 38264739 PMCID: PMC10803514 DOI: 10.3389/fonc.2023.1263685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
Background Breast cancer is the key global menace to women's health, which ranks first by mortality rate. The rate reduction and early diagnostics of breast cancer are the mainstream of medical research. Immunohistochemical examination is the most important link in the process of breast cancer treatment, and its results directly affect physicians' decision-making on follow-up medical treatment. Purpose This study aims to develop a computer-aided diagnosis (CAD) method based on deep learning to classify breast ultrasound (BUS) images according to immunohistochemical results. Methods A new depth learning framework guided by BUS image data analysis was proposed for the classification of breast cancer nodes in BUS images. The proposed CAD classification network mainly comprised three innovation points. First, a multilevel feature distillation network (MFD-Net) based on CNN, which could extract feature layers of different scales, was designed. Then, the image features extracted at different depths were fused to achieve multilevel feature distillation using depth separable convolution and reverse depth separable convolution to increase convolution depths. Finally, a new attention module containing two independent submodules, the channel attention module (CAM) and the spatial attention module (SAM), was introduced to improve the model classification ability in channel and space. Results A total of 500 axial BUS images were retrieved from 294 patients who underwent BUS examination, and these images were detected and cropped, resulting in breast cancer node BUS image datasets, which were classified according to immunohistochemical findings, and the datasets were randomly subdivided into a training set (70%) and a test set (30%) in the classification process, with the results of the four immune indices output simultaneously from training and testing, in the model comparison experiment. Taking ER immune indicators as an example, the proposed model achieved a precision of 0.8933, a recall of 0.7563, an F1 score of 0.8191, and an accuracy of 0.8386, significantly outperforming the other models. The results of the designed ablation experiment also showed that the proposed multistage characteristic distillation structure and attention module were key in improving the accuracy rate. Conclusion The extensive experiments verify the high efficiency of the proposed method. It is considered the first classification of breast cancer by immunohistochemical results in breast cancer image processing, and it provides an effective aid for postoperative breast cancer treatment, greatly reduces the difficulty of diagnosis for doctors, and improves work efficiency.
Collapse
Affiliation(s)
- Ding Yan
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zijian Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Jiajun Duan
- School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
| | - Jia Qu
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Ultrasound, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Linlin Shi
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Qian Wang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Huawei Zhang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| |
Collapse
|
7
|
Kondamuri SR, Thadikemalla VSG, Suryanarayana G, Karthik C, Reddy VS, Sahithi VB, Anitha Y, Yogitha V, Valli PR. Chest CT Image based Lung Disease Classification - A Review. Curr Med Imaging 2024; 20:1-14. [PMID: 38389342 DOI: 10.2174/0115734056248176230923143105] [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: 03/01/2023] [Revised: 07/22/2023] [Accepted: 08/22/2023] [Indexed: 02/24/2024]
Abstract
Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders.
Collapse
Affiliation(s)
- Shri Ramtej Kondamuri
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | | | - Gunnam Suryanarayana
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | - Chandran Karthik
- Department of Robotics and Automation, Jyothi Engineering College, Thrissur, Kerala 679531, India
| | - Vanga Siva Reddy
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | - V Bhuvana Sahithi
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | - Y Anitha
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | - V Yogitha
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| | - P Reshma Valli
- Department of ECE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, 520007, India
| |
Collapse
|
8
|
Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
Collapse
Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
| |
Collapse
|
9
|
Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Müller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph 2023; 110:102310. [PMID: 37979340 DOI: 10.1016/j.compmedimag.2023.102310] [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: 07/18/2023] [Revised: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
Collapse
Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Nicola Falcionelli
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Paolo Sernani
- Department of Law, University of Macerata (UNIMC), Macerata, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Aldo Franco Dragoni
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy.
| |
Collapse
|
10
|
Rinaldi L, Guerini Rocco E, Spitaleri G, Raimondi S, Attili I, Ranghiero A, Cammarata G, Minotti M, Lo Presti G, De Piano F, Bellerba F, Funicelli G, Volpe S, Mora S, Fodor C, Rampinelli C, Barberis M, De Marinis F, Jereczek-Fossa BA, Orecchia R, Rizzo S, Botta F. Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients. Cancers (Basel) 2023; 15:4553. [PMID: 37760521 PMCID: PMC10527057 DOI: 10.3390/cancers15184553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.
Collapse
Affiliation(s)
- Lisa Rinaldi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Elena Guerini Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
| | - Gianluca Spitaleri
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Ilaria Attili
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Alberto Ranghiero
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Giulio Cammarata
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Marta Minotti
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Francesca De Piano
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Federica Bellerba
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Gianluigi Funicelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Stefania Volpe
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Serena Mora
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiana Fodor
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiano Rampinelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Massimo Barberis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Filippo De Marinis
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Roberto Orecchia
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
- Scientific Direction, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), Via Tesserete 46, 6900 Lugano, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Via G. Buffi 13, 6900 Lugano, Switzerland
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| |
Collapse
|
11
|
Duan S, Dong W, Hua Y, Zheng Y, Ren Z, Cao G, Wu F, Rong T, Liu B. Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms. Infect Drug Resist 2023; 16:4325-4334. [PMID: 37424672 PMCID: PMC10329448 DOI: 10.2147/idr.s417663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/28/2023] [Indexed: 07/11/2023] Open
Abstract
Purpose To explore the application of deep learning (DL) methods based on T2 sagittal MR images for discriminating between spinal tuberculosis (STB) and spinal metastases (SM). Patients and Methods A total of 121 patients with histologically confirmed STB and SM across four institutions were retrospectively analyzed. Data from two institutions were used for developing deep learning models and internal validation, while the remaining institutions' data were used for external testing. Utilizing MVITV2, EfficientNet-B3, ResNet101, and ResNet34 as backbone networks, we developed four distinct DL models and evaluated their diagnostic performance based on metrics such as accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score, and confusion matrix. Furthermore, the external test images were blindly evaluated by two spine surgeons with different levels of experience. We also used Gradient-Class Activation Maps to visualize the high-dimensional features of different DL models. Results For the internal validation set, MVITV2 outperformed other models with an accuracy of 98.7%, F1 score of 98.6%, and AUC of 0.98. Other models followed in this order: EfficientNet-B3 (ACC: 96.1%, F1 score: 95.9%, AUC: 0.99), ResNet101 (ACC: 85.5%, F1 score: 84.8%, AUC: 0.90), and ResNet34 (ACC: 81.6%, F1 score: 80.7%, AUC: 0.85). For the external test set, MVITV2 again performed excellently with an accuracy of 91.9%, F1 score of 91.5%, and an AUC of 0.95. EfficientNet-B3 came second (ACC: 85.9, F1 score: 91.5%, AUC: 0.91), followed by ResNet101 (ACC:80.8, F1 score: 80.0%, AUC: 0.87) and ResNet34 (ACC: 78.8, F1 score: 77.9%, AUC: 0.86). Additionally, the diagnostic accuracy of the less experienced spine surgeon was 73.7%, while that of the more experienced surgeon was 88.9%. Conclusion Deep learning based on T2WI sagittal images can help discriminate between STB and SM, and can achieve a level of diagnostic performance comparable with that produced by experienced spine surgeons.
Collapse
Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Weijie Dong
- Department of Orthopedics, Beijing Chest Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yali Zheng
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, People’s Republic of China
| | - Zengsuonan Ren
- Department of Orthopaedic Surgery, People’s Hospital of Hainan Tibetan Autonomous Prefecture, Hainan Tibetan Autonomous Prefecture, Qinghai Province, People’s Republic of China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Fangfang Wu
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, People’s Republic of China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| |
Collapse
|
12
|
Ma G, Jin Y, Pan J, Zhang S, Metaxas DN, Zhu W. Automatic Lung Cancer Subtypes Classification on CT Images with Self-generated Multi-modality Hybrid Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083482 DOI: 10.1109/embc40787.2023.10341181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Lung cancer is a malignant tumor with rapid progression and high fatality rate. According to histological morphology and cell behaviours of cancerous tissues, lung cancer can be classified into a variety of subtypes. Since different cancer subtype corresponds to distinct therapies, the early and accurate diagnosis is critical for following treatments and prognostic managements. In clinical practice, the pathological examination is regarded as the gold standard for cancer subtypes diagnosis, while the disadvantage of invasiveness limits its extensive use, leading the non-invasive and fast-imaging computed tomography (CT) test a more commonly used modality in early cancer diagnosis. However, the diagnostic results of CT test are less accurate due to the relatively low image resolution and the atypical manifestations of cancer subtypes. In this work, we propose a novel automatic classification model to offer the assistance in accurately diagnosing the lung cancer subtypes on CT images. Inspired by the findings of cross-modality associations between CT images and their corresponding pathological images, our proposed model is developed to incorporate general histopathological information into CT imagery-based lung cancer subtypes diagnostic by omitting the invasive tissue sample collection or biopsy, and thereby augmenting the diagnostic accuracy. Experimental results on both internal evaluation datasets and external evaluation datasets demonstrate that our proposed model outputs more accurate lung cancer subtypes diagnostic predictions compared to existing CT-based state-of-the-art (SOTA) classification models, by achieving significant improvements in both accuracy (ACC) and area under the receiver operating characteristic curve (AUC).Clinical Relevance- This work provides a method for automatically classifying the lung cancer subtypes on CT images.
Collapse
|
13
|
Duan S, Cao G, Hua Y, Hu J, Zheng Y, Wu F, Xu S, Rong T, Liu B. Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods. World Neurosurg 2023; 175:e823-e831. [PMID: 37059360 DOI: 10.1016/j.wneu.2023.04.029] [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: 02/16/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.
Collapse
Affiliation(s)
- Shuo Duan
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanmei Cao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichun Hua
- Department of Medical Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junnan Hu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yali Zheng
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Fangfang Wu
- Department of Respiratory, Critical Care, and Sleep Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuai Xu
- Department of Spinal Surgery, Peking University People's Hospital, Peking University, Beijing, China
| | - Tianhua Rong
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baoge Liu
- Department of Orthopaedic Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
| |
Collapse
|
14
|
Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif Intell Med 2023; 141:102572. [PMID: 37295902 DOI: 10.1016/j.artmed.2023.102572] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/16/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
Collapse
Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Danial Javaheri
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Braveen M, Nachiyappan S, Seetha R, Anusha K, Ahilan A, Prasanth A, Jeyam A. ALBAE feature extraction based lung pneumonia and cancer classification. Soft comput 2023:1-14. [PMID: 37362264 PMCID: PMC10187954 DOI: 10.1007/s00500-023-08453-w] [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] [Accepted: 05/06/2023] [Indexed: 06/28/2023]
Abstract
Lung cancer is a deadly disease showing uncontrolled proliferation of malignant cells in the lungs. If the lung cancer is detected in early stages, it can be cured before critical stage. In recent years, new technologies have gained much attention in the healthcare industry however, the unpredictable appearance of tumors, finding their presence, determining its shape, size and high discrepancy in medical images are the challenging tasks. To overcome this issue a novel Ant lion-based Autoencoders (ALbAE) model is proposed for efficient classification of lung cancer and pneumonia. Initially Computed Tomography (CT) images are pre-processed using median filters to remove noise artifacts and improving the quality of the images. Consequently, the relevant features such as image edges, pixel rates of the images and blood clots are extracted by ant lion-based autoencoder (ALbAE) technique. Finally, in classification stage, the lung CT images are classified into three different categories such as normal lung, cancer affected lung and pneumonia affected lung using Random forest technique. The effectiveness of the implemented design is estimated by different parameters such as precision, recall, Accuracy and F1-measure. The proposed approach attains 97% accuracy; 98% of recall and F-measure rate is attained through the developed design and the proposed model gains 96% of precision score. Experimental outcomes show that the proposed model performs better than existing SVM, ELM, and MLP in classifying lung cancer and pneumonia.
Collapse
Affiliation(s)
- M. Braveen
- Assistant professor senior, School of Computer Science and Engineering, Vellore institute of technology, Chennai, Tamil Nadu India
| | - S. Nachiyappan
- Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu India
| | - R. Seetha
- Associate Professor, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu India
| | - K. Anusha
- Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu India
| | - A. Ahilan
- Associate Professor, Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu India
| | - A. Prasanth
- Assistant Professor, Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, India
| | - A. Jeyam
- Assistant Professor, Computer Science and Engineering, Lord Jegannath College of Engineering and Technology, Kanyakumari, Tamil Nadu 629402 India
| |
Collapse
|
17
|
Demircioğlu A. Are deep models in radiomics performing better than generic models? A systematic review. Eur Radiol Exp 2023; 7:11. [PMID: 36918479 PMCID: PMC10014394 DOI: 10.1186/s41747-023-00325-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/13/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Application of radiomics proceeds by extracting and analysing imaging features based on generic morphological, textural, and statistical features defined by formulas. Recently, deep learning methods were applied. It is unclear whether deep models (DMs) can outperform generic models (GMs). METHODS We identified publications on PubMed and Embase to determine differences between DMs and GMs in terms of receiver operating area under the curve (AUC). RESULTS Of 1,229 records (between 2017 and 2021), 69 studies were included, 61 (88%) on tumours, 68 (99%) retrospective, and 39 (56%) single centre; 30 (43%) used an internal validation cohort; and 18 (26%) applied cross-validation. Studies with independent internal cohort had a median training sample of 196 (range 41-1,455); those with cross-validation had only 133 (43-1,426). Median size of validation cohorts was 73 (18-535) for internal and 94 (18-388) for external. Considering the internal validation, in 74% (49/66), the DMs performed better than the GMs, vice versa in 20% (13/66); no difference in 6% (4/66); and median difference in AUC 0.045. On the external validation, DMs were better in 65% (13/20), GMs in 20% (4/20) cases; no difference in 3 (15%); and median difference in AUC 0.025. On internal validation, fused models outperformed GMs and DMs in 72% (20/28), while they were worse in 14% (4/28) and equal in 14% (4/28); median gain in AUC was + 0.02. On external validation, fused model performed better in 63% (5/8), worse in 25% (2/8), and equal in 13% (1/8); median gain in AUC was + 0.025. CONCLUSIONS Overall, DMs outperformed GMs but in 26% of the studies, DMs did not outperform GMs.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| |
Collapse
|
18
|
Zhang C, Huang Y, Fang C, Liang Y, Jiang D, Li J, Ma H, Jiang W, Feng Y. Construction and validation of a prognostic model based on ten signature cell cycle-related genes for early-stage lung squamous cell carcinoma. Cancer Biomark 2023; 36:313-326. [PMID: 36938730 DOI: 10.3233/cbm-220227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
BACKGROUND We performed a bioinformatics analysis to screen for cell cycle-related differentially expressed genes (DEGs) and constructed a model for the prognostic prediction of patients with early-stage lung squamous cell carcinoma (LSCC). METHODS From a gene expression omnibus (GEO) database, the GSE157011 dataset was randomly divided into an internal training group and an internal testing group at a 1:1 ratio, and the GSE30219, GSE37745, GSE42127, and GSE73403 datasets were merged as the external validation group. We performed single-sample gene set enrichment analysis (ssGSEA), univariate Cox analysis, and difference analysis, and identified 372 cell cycle-related genes. Additionally, we combined LASSO/Cox regression analysis to construct a prognostic model. Then, patients were divided into high-risk and low-risk groups according to risk scores. The internal testing group, discovery set, and external verification set were used to assess model reliability. We used a nomogram to predict patient prognoses based on clinical features and risk values. Clinical relevance analysis and the Human Protein Atlas (HPA) database were used to verify signature gene expression. RESULTS Ten cell cycle-related DEGs (EIF2B1, FSD1L, FSTL3, ORC3, HMMR, SETD6, PRELP, PIGW, HSD17B6, and GNG7) were identified and a model based on the internal training group constructed. From this, patients in the low-risk group had a higher survival rate when compared with the high-risk group. Time-dependent receiver operating characteristic (tROC) and Cox regression analyses showed the model was efficient and accurate. Clinical relevance analysis and the HPA database showed that DEGs were significantly dysregulated in LSCC tissue. CONCLUSION Our model predicted the prognosis of early-stage LSCC patients and demonstrated potential applications for clinical decision-making and individualized therapy.
Collapse
Affiliation(s)
- Chengpeng Zhang
- Department of Thoracic Surgery, Suzhou Ninth People's Hospital, Suzhou, Jiangsu, China.,Department of Thoracic Surgery, Suzhou Ninth People's Hospital, Suzhou, Jiangsu, China
| | - Yong Huang
- Department of Thoracic Surgery, Haimen People's Hospital, Nantong, Jiangsu, China.,Department of Thoracic Surgery, Suzhou Ninth People's Hospital, Suzhou, Jiangsu, China
| | - Chen Fang
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.,Department of Thoracic Surgery, Suzhou Ninth People's Hospital, Suzhou, Jiangsu, China
| | - Yingkuan Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Dong Jiang
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jiaxi Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Haitao Ma
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Wei Jiang
- Department of Thoracic Surgery, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China
| | - Yu Feng
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| |
Collapse
|
19
|
Mohammed MA, Lakhan A, Abdulkareem KH, Garcia-Zapirain B. A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA). Comput Biol Med 2023; 154:106617. [PMID: 36753981 DOI: 10.1016/j.compbiomed.2023.106617] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
These days, the ratio of cancer diseases among patients has been growing day by day. Recently, many cancer cases have been reported in different clinical hospitals. Many machine learning algorithms have been suggested in the literature to predict cancer diseases with the same class types based on trained and test data. However, there are many research rooms available for further research. In this paper, the studies look into the different types of cancer by analyzing, classifying, and processing the multi-omics dataset in a fog cloud network. Based on SARSA on-policy and multi-omics workload learning, made possible by reinforcement learning, the study made new hybrid cancer detection schemes. It consists of different layers, such as clinical data collection via laboratories and tool processes (biopsy, colonoscopy, and mammography) at the distributed omics-based clinics in the network. The study considers the different cancer classes such as carcinomas, sarcomas, leukemias, and lymphomas with their types in work and processes them using the multi-omics distributed clinics in work. In order to solve the problem, the study presents omics cancer workload reinforcement learning state action reward state action "SARSA" (OCWLS) schemes, which are made up of an on-policy learning scheme on different parameters like states, actions, timestamps, reward, accuracy, and processing time constraints. The goal is to process multiple cancer classes and workload feature matching while reducing the time it takes to process in clinical hospitals that are spread out. Simulation results show that OCWLS is better than other machine learning methods regarding+ processing time, extracting features from multiple classes of cancer, and matching in the system.
Collapse
Affiliation(s)
- Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq; eVIDA Lab, University of Deusto, 48007 Bilbao, Spain.
| | - Abdullah Lakhan
- Department of Computer Science, Dawood University of Engineering and Technology, Pakistan.
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq.
| | | |
Collapse
|
20
|
Qiu Y, Lin F, Chen W, Xu M. Pre-training in Medical Data: A Survey. MACHINE INTELLIGENCE RESEARCH 2023. [PMCID: PMC9942039 DOI: 10.1007/s11633-022-1382-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.
Collapse
Affiliation(s)
- Yixuan Qiu
- The University of Queensland, Brisbane, 4072 Australia
| | - Feng Lin
- The University of Queensland, Brisbane, 4072 Australia
| | - Weitong Chen
- The University of Adelaide, Adelaide, 5005 Australia
| | - Miao Xu
- The University of Queensland, Brisbane, 4072 Australia
| |
Collapse
|
21
|
Wang T, Yan D, Liu Z, Xiao L, Liang C, Xin H, Feng M, Zhao Z, Wang Y. Diagnosis of cervical lymph node metastasis with thyroid carcinoma by deep learning application to CT images. Front Oncol 2023; 13:1099104. [PMID: 36776294 PMCID: PMC9909181 DOI: 10.3389/fonc.2023.1099104] [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: 11/15/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The incidence of thyroid diseases has increased in recent years, and cervical lymph node metastasis (LNM) is considered an important risk factor for locoregional recurrence. This study aims to develop a deep learning-based computer-aided diagnosis (CAD) method to diagnose cervical LNM with thyroid carcinoma on computed tomography (CT) images. Methods A new deep learning framework guided by the analysis of CT data for automated detection and classification of LNs on CT images is proposed. The presented CAD system consists of two stages. First, an improved region-based detection network is designed to learn pyramidal features for detecting small nodes at different feature scales. The region proposals are constrained by the prior knowledge of the size and shape distributions of real nodes. Then, a residual network with an attention module is proposed to perform the classification of LNs. The attention module helps to classify LNs in the fine-grained domain, improving the whole classification network performance. Results A total of 574 axial CT images (including 676 lymph nodes: 103 benign and 573 malignant lymph nodes) were retrieved from 196 patients who underwent CT for surgical planning. For detection, the data set was randomly subdivided into a training set (70%) and a testing set (30%), where each CT image was expanded to 20 images by rotation, mirror image, changing brightness, and Gaussian noise. The extended data set included 11,480 CT images. The proposed detection method outperformed three other detection architectures (average precision of 80.3%). For classification, ROI of lymph node metastasis labeled by radiologists were used to train the classification network. The 676 lymph nodes were randomly divided into 70% of the training set (73 benign and 401 malignant lymph nodes) and 30% of the test set (30 benign and 172 malignant lymph nodes). The classification method showed superior performance over other state-of-the-art methods with an accuracy of 96%, true positive and negative rates of 98.8 and 80%, respectively. It outperformed radiologists with an area under the curve of 0.894. Discussion The extensive experiments verify the high efficiency of the proposed method. It is considered instrumental in a clinical setting to diagnose cervical LNM with thyroid carcinoma using preoperative CT images. The future research can consider adding radiologists' experience and domain knowledge into the deep-learning based CAD method to make it more clinically significant. Conclusion The extensive experiments verify the high efficiency of the proposed method. It is considered instrumental in a clinical setting to diagnose cervical LNM with thyroid carcinoma using preoperative CT images.
Collapse
Affiliation(s)
- Tiantian Wang
- Department of Thyroid Surgery, the Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou, China
| | - Ding Yan
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zhaodi Liu
- School of Medicine, Zhejiang University, Hangzhou, China
| | - Lianxiang Xiao
- Shandong Provincial Maternal and Child Health Care Hospital, Shandong University, Jinan, China
| | - Changhu Liang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Haotian Xin
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Mengmeng Feng
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Zijian Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China,*Correspondence: Zijian Zhao,
| | - Yong Wang
- Department of Thyroid Surgery, the Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou, China
| |
Collapse
|
22
|
Karthika K, Jothilakshmi GR. An early prediction of lung cancer, solid, liquid and semi-liquid deposition and its classification through measurement of physical characteristics using CT scan images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2163538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- K. Karthika
- Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India
| | - G. R. Jothilakshmi
- Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, India
| |
Collapse
|
23
|
Bove S, Fanizzi A, Fadda F, Comes MC, Catino A, Cirillo A, Cristofaro C, Montrone M, Nardone A, Pizzutilo P, Tufaro A, Galetta D, Massafra R. A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region. PLoS One 2023; 18:e0285188. [PMID: 37130116 PMCID: PMC10153708 DOI: 10.1371/journal.pone.0285188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.
Collapse
Affiliation(s)
- Samantha Bove
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | - Federico Fadda
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | | - Angelo Cirillo
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | | | | | | - Antonio Tufaro
- I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | | | | |
Collapse
|
24
|
D 2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans. Diagnostics (Basel) 2022; 13:diagnostics13010101. [PMID: 36611393 PMCID: PMC9818184 DOI: 10.3390/diagnostics13010101] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND OBJECTIVE In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. METHODS In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. RESULTS The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. CONCLUSION The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.
Collapse
|
25
|
Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
Collapse
Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
| |
Collapse
|
26
|
A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
Collapse
|
27
|
Guan X, Lu N, Zhang J. Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography. Front Oncol 2022; 12:950185. [PMID: 36452488 PMCID: PMC9702985 DOI: 10.3389/fonc.2022.950185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/24/2022] [Indexed: 10/24/2023] Open
Abstract
PURPOSE To construct the deep learning system (DLS) based on enhanced computed tomography (CT) images for preoperative prediction of staging and human epidermal growth factor receptor 2 (HER2) status in gastric cancer patients. METHODS The raw enhanced CT image dataset consisted of CT images of 389 patients in the retrospective cohort, The Cancer Imaging Archive (TCIA) cohort, and the prospective cohort. DLS was developed by transfer learning for tumor detection, staging, and HER2 status prediction. The pre-trained Yolov5, EfficientNet, EfficientNetV2, Vision Transformer (VIT), and Swin Transformer (SWT) were studied. The tumor detection and staging dataset consisted of 4860 enhanced CT images and annotated tumor bounding boxes. The HER2 state prediction dataset consisted of 38900 enhanced CT images. RESULTS The DetectionNet based on Yolov5 realized tumor detection and staging and achieved a mean Average Precision (IoU=0.5) (mAP_0.5) of 0.909 in the external validation cohort. The VIT-based PredictionNet performed optimally in HER2 status prediction with the area under the receiver operating characteristics curve (AUC) of 0.9721 and 0.9995 in the TCIA cohort and prospective cohort, respectively. DLS included DetectionNet and PredictionNet had shown excellent performance in CT image interpretation. CONCLUSION This study developed the enhanced CT-based DLS to preoperatively predict the stage and HER2 status of gastric cancer patients, which will help in choosing the appropriate treatment to improve the survival of gastric cancer patients.
Collapse
Affiliation(s)
| | | | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| |
Collapse
|
28
|
Xu Z, Ren H, Zhou W, Liu Z. ISANET: Non-small cell lung cancer classification and detection based on CNN and attention mechanism. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103773] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
29
|
Li H, Song Q, Gui D, Wang M, Min X, Li A. Reconstruction-assisted Feature Encoding Network for Histologic Subtype Classification of Non-small Cell Lung Cancer. IEEE J Biomed Health Inform 2022; 26:4563-4574. [PMID: 35849680 DOI: 10.1109/jbhi.2022.3192010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Accurate histological subtype classification between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using computed tomography (CT) images is of great importance to assist clinicians in determining treatment and therapy plans for non-small cell lung cancer (NSCLC) patients. Although current deep learning approaches have achieved promising progress in this field, they are often difficult to capture efficient tumor representations due to inadequate training data, and in consequence show limited performance. In this study, we propose a novel and effective reconstruction-assisted feature encoding network (RAFENet) for histological subtype classification by leveraging an auxiliary image reconstruction task to enable extra guidance and regularization for enhanced tumor feature representations. Different from existing reconstruction-assisted methods that directly use generalizable features obtained from shared encoder for primary task, a dedicated task-aware encoding module is utilized in RAFENet to perform refinement of generalizable features. Specifically, a cascade of cross-level non-local blocks are introduced to progressively refine generalizable features at different levels with the aid of lower-level task-specific information, which can successfully learn multi-level task-specific features tailored to histological subtype classification. Moreover, in addition to widely adopted pixel-wise reconstruction loss, we introduce a powerful semantic consistency loss function to explicitly supervise the training of RAFENet, which combines both feature consistency loss and prediction consistency loss to ensure semantic invariance during image reconstruction. Extensive experimental results show that RAFENet effectively addresses the difficult issues that cannot be resolved by existing reconstruction-based methods and consistently outperforms other state-of-the-art methods on both public and in-house NSCLC datasets.
Collapse
|
30
|
COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4254631. [PMID: 35845911 PMCID: PMC9284325 DOI: 10.1155/2022/4254631] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.
Collapse
|
31
|
Hatami N, Cho TH, Mechtouff L, Eker OF, Rousseau D, Frindel C. CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3430-3434. [PMID: 36085793 DOI: 10.1109/embc48229.2022.9871735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS. Clinical Relevance- - We present the first deep learning approach predicting the clinical outcome of stroke patients treated by mechanical thrombectomy which integrates imaging data at the voxel level with key clinical metadata. Combining clinical and imaging data to evaluate the potential benefit from therapy closely mirrors the clinical decision process. Our promising results suggest our predictive model could assist in acute stroke management.
Collapse
|
32
|
Tomassini S, Falcionelli N, Sernani P, Sbrollini A, Morettini M, Burattini L, Dragoni AF. Cloud-YLung for Non-Small Cell Lung Cancer Histology Classification from 3D Computed Tomography Whole-Lung Scans. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1556-1560. [PMID: 36085720 DOI: 10.1109/embc48229.2022.9871378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) represents up to 85% of all malignant lung nodules. Adenocarcinoma and squamous cell carcinoma account for 90% of all NSCLC histotypes. The standard diagnostic procedure for NSCLC histotype characterization implies cooperation of 3D Computed Tomography (CT), especially in the form of low-dose CT, and lung biopsy. Since lung biopsy is invasive and challenging (especially for deeply-located lung cancers and for those close to blood vessels or airways), there is the necessity to develop non-invasive procedures for NSCLC histology classification. Thus, this study aims to propose Cloud-YLung for NSCLC histology classification directly from 3D CT whole-lung scans. With this aim, data were selected from the openly-accessible NSCLC-Radiomics dataset and a modular pipeline was designed. Automatic feature extraction and classification were accomplished by means of a Convolutional Long Short-Term Memory (ConvLSTM)-based neural network trained from scratch on a scalable GPU cloud service to ensure a machine-independent reproducibility of the entire framework. Results show that Cloud- YLung performs well in discriminating both NSCLC histotypes, achieving a test accuracy of 75% and AUC of 84%. Cloud-YLung is not only lung nodule segmentation free but also the first that makes use of a ConvLSTM-based neural network to automatically extract high-throughput features from 3D CT whole-lung scans and classify them. Clinical relevance- Cloud-YLung is a promising framework to non-invasively classify NSCLC histotypes. Preserving the lung anatomy, its application could be extended to other pulmonary pathologies using 3D CT whole-lung scans.
Collapse
|
33
|
Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med 2022; 146:105691. [PMID: 35691714 DOI: 10.1016/j.compbiomed.2022.105691] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
Abstract
Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
Collapse
Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Nicola Falcionelli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Paolo Sernani
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| |
Collapse
|
34
|
DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.
Collapse
|
35
|
Vaiyapuri T, Dutta AK, Punithavathi ISH, Duraipandy P, Alotaibi SS, Alsolai H, Mohamed A, Mahgoub H. Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images. Healthcare (Basel) 2022; 10:healthcare10040677. [PMID: 35455854 PMCID: PMC9027672 DOI: 10.3390/healthcare10040677] [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: 02/18/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 12/13/2022] Open
Abstract
Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques. Automated detection and classification of pancreatic tumors can be provided by the computer-aided diagnosis (CAD) model using radiological images such as computed tomography (CT) and magnetic resonance imaging (MRI). The recently developed machine learning (ML) and deep learning (DL) models can be utilized for the automated and timely detection of pancreatic cancer. In light of this, this article introduces an intelligent deep-learning-enabled decision-making medical system for pancreatic tumor classification (IDLDMS-PTC) using CT images. The major intention of the IDLDMS-PTC technique is to examine the CT images for the existence of pancreatic tumors. The IDLDMS-PTC model derives an emperor penguin optimizer (EPO) with multilevel thresholding (EPO-MLT) technique for pancreatic tumor segmentation. Additionally, the MobileNet model is applied as a feature extractor with optimal auto encoder (AE) for pancreatic tumor classification. In order to optimally adjust the weight and bias values of the AE technique, the multileader optimization (MLO) technique is utilized. The design of the EPO algorithm for optimal threshold selection and the MLO algorithm for parameter tuning shows the novelty. A wide range of simulations was executed on benchmark datasets, and the outcomes reported the promising performance of the IDLDMS-PTC model on the existing methods.
Collapse
Affiliation(s)
- Thavavel Vaiyapuri
- Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - I. S. Hephzi Punithavathi
- Department of Computer Science and Engineering, Sphoorthy Engineering College, Telangana, Hyderabad 501510, India;
| | - P. Duraipandy
- Department of Electrical and Electronics Engineering, J. B. Institute of Engineering and Technology, Telangana, Hyderabad 500075, India;
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 21911, Saudi Arabia;
| | - Hadeel Alsolai
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo, Cairo 11745, Egypt;
| | - Hany Mahgoub
- Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
- Correspondence:
| |
Collapse
|
36
|
Luo X, Zhang J, Li Z, Yang R. Diagnosis of ulcerative colitis from endoscopic images based on deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103443] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
37
|
Kumar A, Dhara AK, Thakur SB, Sadhu A, Nandi D. Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images. PATTERN RECOGNITION AND IMAGE ANALYSIS 2021. [PMCID: PMC8711684 DOI: 10.1134/s1054661821040027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.
Collapse
Affiliation(s)
- Abhishek Kumar
- School of Computer and Information Sciences University of Hyderabad, 500046 Hyderabad, India
| | - Ashis Kumar Dhara
- Electrical Engineering National Institute of Technology, 713209 Durgapur, India
| | - Sumitra Basu Thakur
- Department of Chest and Respiratory Care Medicine, Medical College, 700073 Kolkata, India
| | - Anup Sadhu
- EKO Diagnostic, Medical College, 700073 Kolkata, India
| | - Debashis Nandi
- Computer Science and Engineering National Institute of Technology, 713209 Durgapur, India
| |
Collapse
|
38
|
Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. PRECISION CLINICAL MEDICINE 2021; 4:271-286. [PMID: 35692858 PMCID: PMC8982528 DOI: 10.1093/pcmedi/pbab026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 02/07/2023] Open
Abstract
Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.
Collapse
Affiliation(s)
- Chong Zhang
- Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jionghui Gu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangyang Zhu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Tong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongyang Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China
| |
Collapse
|
39
|
Su L, Zhao J, Su H, Wang Y, Huang W, Jiang X, Gao S. CircRNAs in lung adenocarcinoma: diagnosis and therapy. Curr Gene Ther 2021; 22:15-22. [PMID: 34856899 DOI: 10.2174/1566523221666211202095258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/13/2021] [Accepted: 10/22/2021] [Indexed: 11/22/2022]
Abstract
Lung adenocarcinoma (LUAD) is the common histological subtype of non-small-cell lung carcinoma (NSCLC). Circular RNAs (circRNAs) represent a new class of non-coding RNAs (ncRNAs) involved in the development of cancer. Accumulating evidence indicated that a large number of circular RNAs were found to be involved in many biological processes, including tumor initiation, proliferation and progression. These circRNAs present great potentials as new biomarkers and vital targets for disease diagnosis and prognosis. In this review, we mainly focus on the differentially expressed circRNAs and their functions in the pathogenesis of LUAD, which makes it possible for the utility of circRNAs as novel biomarkers for early diagnosis and therapy. Especially, it is helpful to develop circRNAs as crucial therapeutic targets, thus providing a promising biomedical application in the field of cancer gene therapy.
Collapse
Affiliation(s)
- Lijia Su
- The Hubei Key Laboratory of Tumor Microenvironment and immunotherapy, China Three Gorges University, Yichang. China
| | - Jinying Zhao
- The Third-Grade Pharmacological Laboratory of Traditional Chinese Medicine (Approved by State Administration of Traditional Chinese Medicine), China Three Gorges University, Yichang. China
| | - Huahua Su
- Department of Morphology, Medical Science College of China Three Gorges University, Yichang. China
| | - Yanhua Wang
- Department of Morphology, Medical Science College of China Three Gorges University, Yichang. China
| | - Wenfeng Huang
- Department of Morphology, Medical Science College of China Three Gorges University, Yichang. China
| | - Xuemei Jiang
- Department of Morphology, Medical Science College of China Three Gorges University, Yichang. China
| | - Shiyao Gao
- The Hubei provincial hospital of Traditional Chinese Medicine, Wuhan. China
| |
Collapse
|
40
|
Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5522452. [PMID: 34820455 PMCID: PMC8608546 DOI: 10.1155/2021/5522452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 10/20/2021] [Indexed: 01/29/2023]
Abstract
Objectives To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.
Collapse
|
41
|
Aonpong P, Iwamoto Y, Han XH, Lin L, Chen YW. Improved Genotype-Guided Deep Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3561-3564. [PMID: 34892008 DOI: 10.1109/embc46164.2021.9630703] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Non-small cell lung cancer (NSCLC) is a type of lung cancer that has a high recurrence rate after surgery. Precise prediction of preoperative prognosis for NSCLC recurrence tends to contribute to the suitable preparation for treatment. Currently, many studied have been conducted to predict the recurrence of NSCLC based on Computed Tomography-images (CT images) or genetic data. The CT image is not expensive but inaccurate. The gene data is more expensive but has high accuracy. In this study, we proposed a genotype-guided radiomics method called GGR and GGR_Fusion to make a higher accuracy prediction model with requires only CT images. The GGR is a two-step method which is consists of two models: the gene estimation model using deep learning and the recurrence prediction model using estimated genes. We further propose an improved performance model based on the GGR model called GGR_Fusion to improve the accuracy. The GGR_Fusion uses the extracted features from the gene estimation model to enhance the recurrence prediction model. The experiments showed that the prediction performance can be improved significantly from 78.61% accuracy, AUC=0.66 (existing radiomics method), 79.09% accuracy, AUC=0.68 (deep learning method) to 83.28% accuracy, AUC=0.77 by the proposed GGR and 84.39% accuracy, AUC=0.79 by the proposed GGR_Fusion.Clinical Relevance-This study improved the preoperative recurrence of NSCLC prediction accuracy from 78.61% by the conventional method to 84.39% by our proposed method using only the CT image.
Collapse
|
42
|
Yan Q, Yi Y, Shen J, Shan F, Zhang Z, Yang G, Shi Y. Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses. Cancer Cell Int 2021; 21:539. [PMID: 34663307 PMCID: PMC8522214 DOI: 10.1186/s12935-021-02195-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 09/04/2021] [Indexed: 12/30/2022] Open
Abstract
Background Cumulative CT radiation damage was positively correlated with increased tumor risks. Although it has recently been known that non-radiation MRI is alternative for pulmonary imaging. There is little known about the value of MRI T1-mapping in the diagnosis of pulmonary nodules. This article aimed to investigate the value of native T1-mapping-based radiomics features in differential diagnosis of pulmonary lesions. Methods 73 patients underwent 3 T-MRI examination in this prospective study. The 99 pulmonary lesions on native T1-mapping images were segmented twice by one radiologist at indicated time points utilizing the in-house semi-automated software, followed by extraction of radiomics features. The inter-class correlation coefficient (ICC) was used for analyzing intra-observer’s agreement. Dimensionality reduction and feature selection were performed via univariate analysis, and least absolute shrinkage and selection operator (LASSO) analysis. Then, the binary logical regression (LR), support vector machine (SVM) and decision tree classifiers with the input of optimal features were selected for differentiating malignant from benign lesions. The receiver operative characteristics (ROC) curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated. Z-test was used to compare differences among AUCs. Results 107 features were obtained, of them, 19.5% (n = 21) had relatively good reliability (ICC ≥ 0.6). The remained 5 features (3 GLCM, 1 GLSZM and 1 shape features) by dimensionality reduction were useful. The AUC of LR was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70%, 85% and 80%. The AUC of SVM was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70, 85 and 80%. The AUC of decision tree was 0.69(95%CI: 0.49–0.87), with sensitivity, specificity and accuracy of 50, 85 and 73.3%. Conclusions The LR and SVM models using native T1-mapping-based radiomics features can differentiate pulmonary malignant from benign lesions, especially for uncertain nodules requiring long-term follow-ups.
Collapse
Affiliation(s)
- Qinqin Yan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Yinqiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Zhiyong Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
| |
Collapse
|
43
|
Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
Collapse
Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| |
Collapse
|
44
|
Choi Y, Aum J, Lee SH, Kim HK, Kim J, Shin S, Jeong JY, Ock CY, Lee HY. Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma. Cancers (Basel) 2021; 13:4077. [PMID: 34439230 PMCID: PMC8391458 DOI: 10.3390/cancers13164077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/02/2021] [Accepted: 08/09/2021] [Indexed: 01/18/2023] Open
Abstract
We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal-training and internal-validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16-2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.
Collapse
Affiliation(s)
- Yeonu Choi
- Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
| | - Jaehong Aum
- Lunit Inc., Seoul 06241, Korea; (J.A.); (S.S.)
| | - Se-Hoon Lee
- Division of Hemato-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
| | - Hong-Kwan Kim
- Department of Thoracic Surgery, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea; (H.-K.K.); (J.K.)
| | - Jhingook Kim
- Department of Thoracic Surgery, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea; (H.-K.K.); (J.K.)
| | | | - Ji Yun Jeong
- Department of Pathology, Kyungpook National University School of Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Korea;
| | | | - Ho Yun Lee
- Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
| |
Collapse
|
45
|
Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning. ENTROPY 2021; 23:e23040382. [PMID: 33804831 PMCID: PMC8063804 DOI: 10.3390/e23040382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/20/2021] [Accepted: 03/22/2021] [Indexed: 12/18/2022]
Abstract
The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.
Collapse
|