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Priyadarshini K, Ali SA, Sivanandam K, Alagarsamy M. Human lung cancer classification and comprehensive analysis using different machine learning techniques. Microsc Res Tech 2024. [PMID: 39295255 DOI: 10.1002/jemt.24682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/14/2024] [Accepted: 08/16/2024] [Indexed: 09/21/2024]
Abstract
Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image-processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi-layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f-score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. RESEARCH HIGHLIGHTS: Lung cancer is a leading cause of cancer-related death. Imaging (MRI, CT, and X-ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k-nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi-layer perceptron (MLP) classify cancer types; MLP excels in accuracy.
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Affiliation(s)
- K Priyadarshini
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
| | - S Ahamed Ali
- Department of Computer Science and Engineering, Easwari Engineering College, Chennai, Tamilnadu, India
| | - K Sivanandam
- Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamilnadu, India
| | - Manjunathan Alagarsamy
- Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Trichy, Tamilnadu, India
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Tran TO, Vo TH, Le NQK. Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. Brief Funct Genomics 2024; 23:181-192. [PMID: 37519050 DOI: 10.1093/bfgp/elad031] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/04/2023] [Accepted: 07/13/2023] [Indexed: 08/01/2023] Open
Abstract
Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.
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Affiliation(s)
- Thi-Oanh Tran
- International Ph.D. Program in Cell Therapy and Regenerative Medicine, College of Medicine, Taipei Medical University, No 250 Wuxing Street, 110, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, No 250 Wuxing Street, 110, Taipei, Taiwan
- Hematology and Blood Transfusion Center, Bach Mai Hospital, No 78 Giai Phong Street, Hanoi, Viet Nam
| | - Thanh Hoa Vo
- Department of Science, School of Science and Computing, South East Technological University, Waterford X91 K0EK, Ireland
- Pharmaceutical and Molecular Biotechnology Research Center (PMBRC), South East Technological University, Waterford X91 K0EK, Ireland
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, No 250 Wuxing Street, 110, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing Street, 110, Taipei, Taiwan
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胡 伦, 夏 威, 李 琼, 高 欣. [Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:205-212. [PMID: 38686399 PMCID: PMC11058493 DOI: 10.7507/1001-5515.202309060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/08/2024] [Indexed: 05/02/2024]
Abstract
Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as "image transformation to image restoration" to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network's feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.
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Affiliation(s)
- 伦瑜 胡
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - 威 夏
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
| | - 琼 李
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
| | - 欣 高
- 中国科学技术大学 生命科学与医学部 生物医学工程学院(苏州)(合肥 230026)School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, P. R. China
- 中国科学院苏州生物医学工程技术研究所(江苏苏州 215163)Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
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Yao S, Dai F, Sun P, Zhang W, Qian B, Lu H. Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population. Nat Commun 2024; 15:1958. [PMID: 38438371 PMCID: PMC10912763 DOI: 10.1038/s41467-024-44906-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.
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Affiliation(s)
- Siqiong Yao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Fang Dai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Peng Sun
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Weituo Zhang
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Biyun Qian
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, 200020, PR China.
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Ortiz S, Rojas-Valenzuela I, Rojas F, Valenzuela O, Herrera LJ, Rojas I. Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches. Comput Biol Med 2024; 168:107713. [PMID: 38000243 DOI: 10.1016/j.compbiomed.2023.107713] [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: 10/25/2022] [Revised: 11/07/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.
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Affiliation(s)
- Sergio Ortiz
- Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain.
| | - Ignacio Rojas-Valenzuela
- Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain
| | - Fernando Rojas
- Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain
| | - Olga Valenzuela
- Department of Applied Mathematics, University of Granada, Facultad de Ciencias, Avenida de la Fuente Nueva S/N CP:18071 Granada, Spain
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain
| | - Ignacio Rojas
- Department of Computer Architecture and Technology, University of Granada, E.T.S. de Ingenierías Informática y de Telecomunicación, C/ Periodista Daniel Saucedo Aranda S/N CP:18071 Granada, Spain.
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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.
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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.
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Subashchandrabose U, John R, Anbazhagu UV, Venkatesan VK, Thyluru Ramakrishna M. Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer. Diagnostics (Basel) 2023; 13:3053. [PMID: 37835796 PMCID: PMC10572651 DOI: 10.3390/diagnostics13193053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
The early detection and classification of lung cancer is crucial for improving a patient's outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.
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Affiliation(s)
| | - Rajan John
- Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia;
| | - Usha Veerasamy Anbazhagu
- Department of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai 603203, India;
| | - Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bangalore 560066, India
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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Menon T, Gopal S, Rastogi Verma S. Targeted therapies in non-small cell lung cancer and the potential role of AI interventions in cancer treatment. Biotechnol Appl Biochem 2023; 70:344-356. [PMID: 35609005 DOI: 10.1002/bab.2356] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 04/17/2022] [Indexed: 11/12/2022]
Abstract
Non-small cell lung cancer is the most prevalent lung cancer, and almost three-fourths of patients are diagnosed in the advanced stage directly. In this stage, chemotherapy gives only a 15% 5-year survival rate. As people have varied symptoms and reactions to a specific cancer type, treatment for the tumor is likely to fall short, complicating cancer therapy. Immunotherapy is a breakthrough treatment involving drugs targeting novel immune checkpoint inhibitors like CTLA-4 and PD-1/PD-L1, along with combination therapies. In addition, the utility of engineered CAR-T and CAR-NK cells can be an effective strategy to promote the immune response against tumors. The concept of personalized cancer vaccines with the discovery of neoantigens loaded on dendritic cell vectors can also be an effective approach to cure cancer. Advances in genetic engineering tools like CRISPR/Cas9-mediated gene editing of T cells to enhance their effector function is another ray of hope. This review aims to provide an overview of recent developments in cancer immunotherapy, which can be used in first- and second-line treatments in the clinical space. Further, the intervention of artificial intelligence to detect cancer tumors at an initial stage with the help of machine learning techniques is also explored.
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Affiliation(s)
- Tarunya Menon
- Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shubhang Gopal
- Department of Information Technology, Delhi Technological University, Delhi, India
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Lin C, Huang Y, Wang W, Feng S, Feng S. Lesion detection of chest X-Ray based on scalable attention residual CNN. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1730-1749. [PMID: 36899506 DOI: 10.3934/mbe.2023079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Most of the research on disease recognition in chest X-rays is limited to segmentation and classification, but the problem of inaccurate recognition in edges and small parts makes doctors spend more time making judgments. In this paper, we propose a lesion detection method based on a scalable attention residual CNN (SAR-CNN), which uses target detection to identify and locate diseases in chest X-rays and greatly improves work efficiency. We designed a multi-convolution feature fusion block (MFFB), tree-structured aggregation module (TSAM), and scalable channel and spatial attention (SCSA), which can effectively alleviate the difficulties in chest X-ray recognition caused by single resolution, weak communication of features of different layers, and lack of attention fusion, respectively. These three modules are embeddable and can be easily combined with other networks. Through a large number of experiments on the largest public lung chest radiograph detection dataset, VinDr-CXR, the mean average precision (mAP) of the proposed method was improved from 12.83% to 15.75% in the case of the PASCAL VOC 2010 standard, with IoU > 0.4, which exceeds the existing mainstream deep learning model. In addition, the proposed model has a lower complexity and faster reasoning speed, which is conducive to the implementation of computer-aided systems and provides referential solutions for relevant communities.
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Affiliation(s)
- Cong Lin
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
- College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
| | - Yiquan Huang
- College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
| | - Wenling Wang
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Siling Feng
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Siling Feng
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
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Saad HM, Tourky GF, Al-kuraishy HM, Al-Gareeb AI, Khattab AM, Elmasry SA, Alsayegh AA, Hakami ZH, Alsulimani A, Sabatier JM, Eid MW, Shaheen HM, Mohammed AA, Batiha GES, De Waard M. The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity. Diagnostics (Basel) 2022; 12:2985. [PMID: 36552994 PMCID: PMC9777200 DOI: 10.3390/diagnostics12122985] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/24/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Lung cancer is the second most commonly diagnosed cancer in the world. In terms of the diagnosis of lung cancer, combination carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125) detection had higher sensitivity, specificity, and diagnostic odds ratios than CEA detection alone. Most individuals with elevated serum CA125 levels had lung cancer that was either in stage 3 or stage 4. Serum CA125 levels were similarly elevated in lung cancer patients who also had pleural effusions or ascites. Furthermore, there is strong evidence that human lung cancer produces CA125 in vitro, which suggests that other clinical illnesses outside of ovarian cancer could also be responsible for the rise of CA125. MUC16 (CA125) is a natural killer cell inhibitor. As a screening test for lung and ovarian cancer diagnosis and prognosis in the early stages, CA125 has been widely used as a marker in three different clinical settings. MUC16 mRNA levels in lung cancer are increased regardless of gender. As well, increased expression of mutated MUC16 enhances lung cancer cells proliferation and growth. Additionally, the CA125 serum level is thought to be a key indicator for lung cancer metastasis to the liver. Further, CA125 could be a useful biomarker in other cancer types diagnoses like ovarian, breast, and pancreatic cancers. One of the important limitations of CA125 as a first step in such a screening technique is that up to 20% of ovarian tumors lack antigen expression. Each of the 10 possible serum markers was expressed in 29-100% of ovarian tumors with minimal or no CA125 expression. Therefore, there is a controversy regarding CA125 in the diagnosis and prognosis of lung cancer and other cancer types. In this state, preclinical and clinical studies are warranted to elucidate the clinical benefit of CA125 in the diagnosis and prognosis of lung cancer.
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Affiliation(s)
- Hebatallah M. Saad
- Department of Pathology, Faculty of Veterinary Medicine, Matrouh University, Marsa Matruh 51744, Matrouh, Egypt
| | - Ghada F. Tourky
- Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Hayder M. Al-kuraishy
- Department of Clinical Pharmacology, Internal Medicine, College of Medicine, Al-Mustansiriyiah University, Baghdad P.O. Box 14132, Iraq
| | - Ali I. Al-Gareeb
- Department of Clinical Pharmacology, Internal Medicine, College of Medicine, Al-Mustansiriyiah University, Baghdad P.O. Box 14132, Iraq
| | - Ahmed M. Khattab
- Pharmacy College, Al-Azhar University, Cairo 11884, Cairo, Egypt
| | - Sohaila A. Elmasry
- Faculty of Science, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Abdulrahman A. Alsayegh
- Clinical Nutrition Department, Applied Medical Sciences College, Jazan University, Jazan 82817, Saudi Arabia
| | - Zaki H. Hakami
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, MS, CT (ASCP), PhD, Jazan 45142, Saudi Arabia
| | - Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, MS, CT (ASCP), PhD, Jazan 45142, Saudi Arabia
| | - Jean-Marc Sabatier
- Aix-Marseille Université, Institut de Neurophysiopathologie (INP), CNRS UMR 7051, Faculté des Sciences Médicales et Paramédicales, 27 Bd Jean Moulin, 13005 Marseille, France
| | - Marwa W. Eid
- Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Hazem M. Shaheen
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Ali A. Mohammed
- Consultant Respiratory & General Physician, The Chest Clinic, Barts Health NHS Trust Whipps Cross University Hospital, London E11 1NR, UK
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt
| | - Michel De Waard
- Smartox Biotechnology, 6 rue des Platanes, 38120 Saint-Egrève, France
- L’institut du Thorax, INSERM, CNRS, UNIV NANTES, 44007 Nantes, France
- Université de Nice Sophia-Antipolis, LabEx «Ion Channels, Science & Therapeutics», 06560 Valbonne, France
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12
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Rashed BM, Popescu N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:7065. [PMID: 36146414 PMCID: PMC9501859 DOI: 10.3390/s22187065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017-2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.
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Affiliation(s)
| | - Nirvana Popescu
- Computer Science Department, University Politehnica of Bucharest, 060042 Bucharest, Romania
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13
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Melo IO, Angelo Mendes Tenorio FDC, da Silva Gomes JA, da Silva Junior VA, de Albuquerque Nogueira R, Tenorio BM. Fractal methods applied to the seminiferous lumen images can quantify testicular changes induced by heat stress. Acta Histochem 2022; 124:151949. [PMID: 36007436 DOI: 10.1016/j.acthis.2022.151949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022]
Abstract
Male infertility affects many couples around the world and can be related to environmental factors such as exposure to high temperatures. Even so, automated methods evaluating the seminiferous tubules to detect testicular damage are still scarce. In search of new approaches to automation in the microscopic analysis of the testis; the present study used the fractal dimension, lacunarity, multifractality and quantitative morphometry to quantify changes in microphotographs of the seminiferous lumen in testicles reversibly damaged by heat stress (43 °C, 12 min). The parameters fractal dimension, lacunarity, multifractality (Dq and α), perimeter, feret and circularity were able to detect changes in the seminiferous lumen at 7, 15 and 30 days after the testicular damage. These methods also detected the recovery of spermatogenesis at 60 days after heat stress. Area, f(α), centroid X and Y, roundness, rectangle height and width were unable to detect changes caused by heat stress. In conclusion, computer assisted methods applied to the seminiferous lumen images can be a useful new viewpoint to analyze microscopic changes in the testicles, a fast low-cost tool to assist in the automated quantification of testicular damage.
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Affiliation(s)
- Isabel Oliveira Melo
- Health Sciences Center, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
| | | | - José Anderson da Silva Gomes
- Department of Histology and Embryology, Bioscience Center, Federal University of Pernambuco, Recife, Pernambuco, Brazil
| | | | | | - Bruno Mendes Tenorio
- Department of Histology and Embryology, Bioscience Center, Federal University of Pernambuco, Recife, Pernambuco, Brazil.
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14
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Viswanathan VS, Toro P, Corredor G, Mukhopadhyay S, Madabhushi A. The state of the art for artificial intelligence in lung digital pathology. J Pathol 2022; 257:413-429. [PMID: 35579955 PMCID: PMC9254900 DOI: 10.1002/path.5966] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/26/2022] [Accepted: 05/15/2022] [Indexed: 12/03/2022]
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | - Paula Toro
- Department of PathologyCleveland ClinicClevelandOHUSA
| | - Germán Corredor
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
| | | | - Anant Madabhushi
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Louis Stokes Cleveland VA Medical CenterClevelandOHUSA
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15
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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: 16] [Impact Index Per Article: 8.0] [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.
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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.
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16
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Chen C, Cao Y, Li W, Liu Z, Liu P, Tian X, Sun C, Wang W, Gao H, Kang S, Wang S, Jiang J, Chen C, Tian J. The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer. Cancer Med 2022; 12:1051-1063. [PMID: 35762423 PMCID: PMC9883425 DOI: 10.1002/cam4.4953] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 05/26/2022] [Accepted: 06/06/2022] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis. METHODS A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1-IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis-associate RS, high-dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X-tile, Kaplan-Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease-free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups. RESULTS For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan-Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification. CONCLUSION In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.
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Affiliation(s)
- Chi Chen
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of Medicine and EngineeringBeihang UniversityBeijingChina,CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Yuye Cao
- Department of Obstetrics and GynecologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Weili Li
- Department of Obstetrics and GynecologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina,School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Ping Liu
- Department of Obstetrics and GynecologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Xin Tian
- Department of Obstetrics and GynecologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Caixia Sun
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of Medicine and EngineeringBeihang UniversityBeijingChina,CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Wuliang Wang
- Department of Obstetrics and GynecologyThe Second Affiliated Hospital of He' nan Medical UniversityZhengzhouChina
| | - Han Gao
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of Medicine and EngineeringBeihang UniversityBeijingChina,CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Shan Kang
- Department of GynecologyFourth Hospital Hebei Medical UniversityShijiazhuangChina
| | - Shaoguang Wang
- Department of GynecologyYantai Yuhuangding HospitalYantaiChina
| | - Jingying Jiang
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of Medicine and EngineeringBeihang UniversityBeijingChina,Key Laboratory of Big Data‐Based Precision Medicine (Beihang University)Ministry of Industry and Information TechnologyBeijingChina
| | - Chunlin Chen
- Department of Obstetrics and GynecologyNanfang Hospital, Southern Medical UniversityGuangzhouChina
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine, School of Medicine and EngineeringBeihang UniversityBeijingChina,CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
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17
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Han J, Xiao N, Yang W, Luo S, Zhao J, Qiang Y, Chaudhary S, Zhao J. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg 2022; 17:1049-1057. [PMID: 35445285 PMCID: PMC9020752 DOI: 10.1007/s11548-022-02625-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/24/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Medical imaging data of lung cancer in different stages contain a large amount of time information related to its evolution (emergence, development, or extinction). We try to explore the evolution process of lung images in time dimension to improve the prediction of lung cancer survival by using longitudinal CT images and clinical data jointly. METHODS In this paper, we propose an innovative multi-branch spatiotemporal residual network (MS-ResNet) for disease-specific survival (DSS) prediction by integrating the longitudinal computed tomography (CT) images at different times and clinical data. Specifically, we first extract the deep features from the multi-period CT images by an improved residual network. Then, the feature selection algorithm is used to select the most relevant feature subset from the clinical data. Finally, we integrate the deep features and feature subsets to take full advantage of the complementarity between the two types of data to generate the final prediction results. RESULTS The experimental results demonstrate that our MS-ResNet model is superior to other methods, achieving a promising 86.78% accuracy in the classification of short-survivor, med-survivor, and long-survivor. CONCLUSION In computer-aided prognostic analysis of cancer, the time dimension features of the course of disease and the integration of patient clinical data and CT data can effectively improve the prediction accuracy.
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Affiliation(s)
- Jiahao Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ning Xiao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wanting Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shichao Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jun Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
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18
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Chen S. Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3972298. [PMID: 35378943 PMCID: PMC8976635 DOI: 10.1155/2022/3972298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/19/2022] [Accepted: 02/18/2022] [Indexed: 11/17/2022]
Abstract
In this article, in order to explore the application of a diagnosis system for lung cancer, we use an auxiliary diagnostic system to predict and diagnose the good and evil attributes of chest CT pulmonary nodules. This research improves the new diagnosis method based on the convolutional neural network (CNN) and the recurrent neural network (RNN) and combines the dual effects of the two algorithms to process the classification of benign and malignant nodules. By collecting H-E-stained pathological slices of 652 patients' lung lesions from two hospitals between January 2018 and January 2019, the output results of the improved 3D U-net system and the consistent results of two-person reading were compared. This article analyzes the sensitivity, specificity, positive flammability rate, and negative flammability rate of different lung nodule detection methods. In addition, the artificial intelligence system's and the radiologist's judgment results of benign and malignant pulmonary nodules are used to draw ROC curves for further analysis. The improved model has an accuracy rate of 92.3% for predicting malignant lung nodules and an accuracy rate of 82.8% for benign lung nodules. The new diagnostic method using the convolutional neural network and the recurrent neural network can be very effective for improving the accuracy of predicting lung cancer diagnosis. It can play a very effective role in the disease prediction of lung cancer patients, thereby improving the treatment effect.
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Affiliation(s)
- Su Chen
- The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510030, Guangdong, China
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19
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Becker L, Janssen N, Layland SL, Mürdter TE, Nies AT, Schenke-Layland K, Marzi J. Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring. Cancers (Basel) 2021; 13:cancers13225682. [PMID: 34830837 PMCID: PMC8616063 DOI: 10.3390/cancers13225682] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/05/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.
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Affiliation(s)
- Lucas Becker
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
| | - Nicole Janssen
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Shannon L Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
| | - Thomas E Mürdter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Anne T Nies
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tübingen, 72076 Tübingen, Germany
| | - Katja Schenke-Layland
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
- Cardiovascular Research Laboratories, Department of Medicine/Cardiology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90073, USA
| | - Julia Marzi
- Department for Medical Technologies and Regenerative Medicine, Institute of Biomedical Engineering, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, 72076 Tübingen, Germany
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
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20
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Terra WDS, Bull ÉS, Morcelli SR, Moreira RR, Maciel LLF, Almeida JCDA, Kanashiro MM, Fernandes C, Horn A. Antitumor activity via apoptotic cell death pathway of water soluble copper(II) complexes: effect of the diamino unit on selectivity against lung cancer NCI-H460 cell line. Biometals 2021; 34:661-674. [PMID: 33813688 DOI: 10.1007/s10534-021-00302-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 03/16/2021] [Indexed: 12/23/2022]
Abstract
The cytotoxicity against five human tumor cell lines (THP-1, U937, Molt-4, Colo-205 and NCI-H460) of three water soluble copper(II) coordination compounds containing the ligands 3,3'-(ethane-1,2-diylbis(azanediyl))dipropanamide (BCEN), 3,3'-(piperazine-1,4-diyl)dipropanamide (BPAP) or 3,3'-and (1,4-diazepane-1,4-diyl)dipropanamide (BPAH) are reported in this work. The ligands contain different diamine units (ethylenediamine, piperazine or homopiperazine) and two propanamide units attached to the diamine centers, resulting in N2O2 donor sets. The complex containing homopiperazine unit presented the best antiproliferative effect and selectivity against lung cancer cell line NCI-H460, showing inhibitory concentration (IC50) of 58 μmol dm-3 and Selectivity Index (SI) > 3.4. The mechanism of cell death promoted by the complex was investigated by Sub-G1 cell population analysis and annexin V and propidium iodide (PI) labeling techniques, suggesting that the complex promotes death by apoptosis. Transmission electron microscopy investigations are in agreement with the results presented by mitochondrial membrane potential analysis and also show the impairment of other organelles, including endoplasmic reticulum.
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Affiliation(s)
- Wagner da S Terra
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
- Instituto Federal Fluminense, Campos dos Goytacazes, RJ, 28030-130, Brazil
| | - Érika S Bull
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
- Instituto Federal Fluminense, Campos dos Goytacazes, RJ, 28030-130, Brazil
| | - Samila R Morcelli
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
- Secretaria de Educação do Espírito Santo, Mimoso Do Sul, ES, 29400-000, Brazil
| | - Rafaela R Moreira
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
- Centro Federal de Educação Tecnológica, Nova Friburgo, RJ, 28635-080, Brazil
| | - Leide Laura F Maciel
- Laboratório de Fisiologia e Bioquímica de Microrganismos, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
| | - João Carlos de A Almeida
- Laboratório de Fisiologia e Bioquímica de Microrganismos, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
| | - Milton M Kanashiro
- Laboratório de Biologia do Reconhecer, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
| | - Christiane Fernandes
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil
- Departamento de Química, Universidade Federal de Santa Catarina, Florianópolis, SC, 88040-900, Brazil
| | - Adolfo Horn
- Laboratório de Ciências Químicas, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, 28013-602, Brazil.
- Departamento de Química, Universidade Federal de Santa Catarina, Florianópolis, SC, 88040-900, Brazil.
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21
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Cui M, Zhang DY. Artificial intelligence and computational pathology. J Transl Med 2021; 101:412-422. [PMID: 33454724 PMCID: PMC7811340 DOI: 10.1038/s41374-020-00514-0] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 02/07/2023] Open
Abstract
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.
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Affiliation(s)
- Miao Cui
- St. Luke's Roosevelt Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10025, USA
| | - David Y Zhang
- Pathology and Laboratory Services, VA Medical Center, New York, NY, 10010, USA.
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22
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AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features. SENSORS 2020; 21:s21010122. [PMID: 33375508 PMCID: PMC7795214 DOI: 10.3390/s21010122] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/03/2020] [Accepted: 12/24/2020] [Indexed: 11/26/2022]
Abstract
Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features.
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23
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Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020; 18:2312-2325. [PMID: 32994890 PMCID: PMC7494605 DOI: 10.1016/j.csbj.2020.08.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
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Affiliation(s)
- Erik Meijering
- School of Computer Science and Engineering & Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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