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Yang K, Song J, Liu M, Xue L, Liu S, Yin X, Liu K. TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases. Comput Biol Med 2024; 170:108002. [PMID: 38277921 DOI: 10.1016/j.compbiomed.2024.108002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/24/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024]
Abstract
The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.
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Affiliation(s)
- Kun Yang
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Jie Song
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Meng Liu
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Linyan Xue
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Shuang Liu
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China
| | - Xiaoping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China; Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Hebei University, Baoding, China; The Outstanding Young Scientific Research and Innovation Team of Hebei University, Baoding, China.
| | - Kun Liu
- College of Quality and Technical Supervision, Hebei University, Baoding, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding, China; Scientific Research and Innovation Team of Hebei University, Baoding, China.
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Xie T, Wang Z, Li H, Wu P, Huang H, Zhang H, Alsaadi FE, Zeng N. Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. Comput Biol Med 2023; 159:106947. [PMID: 37099976 PMCID: PMC10116157 DOI: 10.1016/j.compbiomed.2023.106947] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/30/2023] [Accepted: 04/15/2023] [Indexed: 04/28/2023]
Abstract
In this paper, a novel deep learning-based medical imaging analysis framework is developed, which aims to deal with the insufficient feature learning caused by the imperfect property of imaging data. Named as multi-scale efficient network (MEN), the proposed method integrates different attention mechanisms to realize sufficient extraction of both detailed features and semantic information in a progressive learning manner. In particular, a fused-attention block is designed to extract fine-grained details from the input, where the squeeze-excitation (SE) attention mechanism is applied to make the model focus on potential lesion areas. A multi-scale low information loss (MSLIL)-attention block is proposed to compensate for potential global information loss and enhance the semantic correlations among features, where the efficient channel attention (ECA) mechanism is adopted. The proposed MEN is comprehensively evaluated on two COVID-19 diagnostic tasks, and the results show that as compared with some other advanced deep learning models, the proposed method is competitive in accurate COVID-19 recognition, which yields the best accuracy of 98.68% and 98.85%, respectively, and exhibits satisfactory generalization ability as well.
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Affiliation(s)
- Tingyi Xie
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK.
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Peishu Wu
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China
| | - Huixiang Huang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Hongyi Zhang
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Fuad E Alsaadi
- Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China.
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Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph 2021; 87:101817. [PMID: 33278767 PMCID: PMC8477299 DOI: 10.1016/j.compmedimag.2020.101817] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 11/17/2022]
Abstract
Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing an automatic segmentation method with fewer steps, we propose a novel deep learning Generative Adversarial Network (GAN)-based lung segmentation schema, which we denote as LGAN. The proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images. We evaluated the proposed LGAN schema on datasets including Lung Image Database Consortium image collection (LIDC-IDRI) and Quantitative Imaging Network (QIN) collection with two metrics: segmentation quality and shape similarity. Also, we compared our work with current state-of-the-art methods. The experimental results demonstrated that the proposed LGAN schema can be used as a promising tool for automatic lung segmentation due to its simplified procedure as well as its improved performance and efficiency.
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Affiliation(s)
- Jiaxing Tan
- The City University of New York, New York 10016, USA
| | - Longlong Jing
- The City University of New York, New York 10016, USA
| | - Yumei Huo
- The City University of New York, New York 10016, USA
| | - Lihong Li
- The City University of New York, New York 10016, USA
| | - Oguz Akin
- Memorial Sloan Kettering Cancer Center, New York 10065, USA
| | - Yingli Tian
- The City University of New York, New York 10016, USA.
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Huang Z, Tan B, Ye H, Fu F, Wang R, Chen W. Dynamic evolution of osseous structure in osteonecrosis of the femoral head and dynamic collapse risks: a preliminary CT image study. J Orthop Surg Res 2020; 15:539. [PMID: 33203411 PMCID: PMC7672969 DOI: 10.1186/s13018-020-02069-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/04/2020] [Indexed: 12/22/2022] Open
Abstract
Background Collapse risk of osteonecrosis of the femoral head (ONFH) is estimated mainly based on static indicators, including lesion size and lesion location, but bone repairing is a dynamic process that lasts for years. The present study attempted to analyze the dynamic evolution of the osseous structure and its correlation with radiographic progression. Methods This retrospective study included 50 hips with ONFH from 50 patients. Participants were divided into the non-collapse group (n = 25) and the collapse group (n = 25). Original files of the initial computed tomography (CT) images were imported into imaging processing software for morphology analysis. The volume of sclerotic bone, the volume of soft tissue, and bone mineral density (BMD) were calculated. The linear correlations between the aforementioned indicators and the disease duration were estimated. The logistic regression analysis was conducted to evaluate the correlation of these indicators with the radiographic progression. Receiver operating characteristic (ROC) analysis was used to evaluate these indicators’ prediction performance. Results The volume of sclerotic bone and the BMD grew with disease duration, but the volume of soft tissue decrease. The logistic regression analysis found that the volume of sclerotic bone and the BMD were statistically associated with radiographic progression. The ROC analysis found that the regression model, which integrated the volume of sclerotic bone and the BMD, had satisfactory performance in predicting radiographic progression. Conclusion The present study suggested a dynamic evolution of the osseous structure and a dynamic variation trend of the collapse risk in ONFH. The volume of sclerotic bone and the BMD might serve as further prognostic indicators when estimating the collapse risk.
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Affiliation(s)
- Zeqing Huang
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, No. 51 Anwai Xiaoguanjie, Chaoyang District, Beijing, 100029, People's Republic of China.,Wangjing Hospital, China Academy of Chinese Medical Sciences, No. 6 Wangjing Zhonghuannan Road, Chaoyang District, Beijing, 100102, People's Republic of China
| | - Biao Tan
- Wangjing Hospital, China Academy of Chinese Medical Sciences, No. 6 Wangjing Zhonghuannan Road, Chaoyang District, Beijing, 100102, People's Republic of China
| | - Hengli Ye
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, No. 51 Anwai Xiaoguanjie, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Fanyu Fu
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, No. 51 Anwai Xiaoguanjie, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Rongtian Wang
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, No. 51 Anwai Xiaoguanjie, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Weiheng Chen
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, No. 51 Anwai Xiaoguanjie, Chaoyang District, Beijing, 100029, People's Republic of China.
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