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Zhang X, Yang D, Li L, Wang J, Liang S, Li P, Han Z, Wang X, Zhang K. Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy. Front Oncol 2024; 14:1280075. [PMID: 38525423 PMCID: PMC10957557 DOI: 10.3389/fonc.2024.1280075] [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: 08/19/2023] [Accepted: 02/15/2024] [Indexed: 03/26/2024] Open
Abstract
Background Due to the widespread use of imaging techniques, the detection rate of early-stage lung cancer has increased. Video-assisted thoracoscopic surgery (VATS) sublobectomy has emerged as a prominent alternative to lobectomy, offering advantages like reduced resection range, better preservation of lung function, and enhanced postoperative quality of life. However, sublobectomy is more intricate than lobectomy, necessitating a higher level of surgical proficiency and anatomical understanding. Methods Three electronic databases were searched to capture relevant studies from January 2016 to March 2023, which related to the application of three-dimensional(3D) technology in VATS sublobectomy. Results Currently, clinical departments such as orthopedics, hepatobiliary surgery, and urology have started using 3D technology. This technology is expected to be widely used in thoracic surgery in future. Now 3D technology assists in preoperative planning, intraoperative navigation and doctor-patient communication. Conclusion 3D technologies, instrumental in locating pulmonary nodules and identifying variations in target lung segmental vessels and bronchi, play pivotal roles in VATS sublobectomy, especially in preoperative planning, intraoperative navigation, and doctor-patient communication. The limitations of 3D technology in clinical application are analyzed, and the future direction of existing 3D technology development is prospected.
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Affiliation(s)
- Xinyu Zhang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University Cardiothoracic Surgical Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Di Yang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University Cardiothoracic Surgical Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Linqian Li
- Surgical Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
| | - Jianing Wang
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- Imaging Department of Hebei University Affiliated Hospital, Baoding, China
| | - Si Liang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Peng Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Zhe Han
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Xiaodong Wang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Ke Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University Cardiothoracic Surgical Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
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P E, S K, Sagayam KM, J A. An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches. Technol Health Care 2024; 32:2193-2209. [PMID: 38251073 DOI: 10.3233/thc-230926] [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] [Indexed: 01/23/2024]
Abstract
BACKGROUND Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates. OBJECTIVE This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images. METHODS The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them. RESULTS The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%. CONCLUSION The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.
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Affiliation(s)
- Elayaraja P
- Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy, India
| | - Kumarganesh S
- Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem, India
| | - K Martin Sagayam
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Andrew J
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
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An P, Liu J, Yu M, Wang J, Wang Z. Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study. Technol Health Care 2024; 32:1569-1582. [PMID: 37694325 DOI: 10.3233/thc-230619] [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] [Indexed: 09/12/2023]
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world. OBJECTIVE To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making. METHODS A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People's Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram. RESULTS Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858-0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701-0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818-0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients. CONCLUSION SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.
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Affiliation(s)
- Peng An
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Junjie Liu
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Mengxing Yu
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jinsong Wang
- Department of Internal Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Zhongqiu Wang
- Department of Radiology, Xiangyang First People's Hospital, Hubei University of Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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Lu X, Gu Y, Yang L, Zhang B, Zhao Y, Yu D, Zhao J, Gao L, Zhou T, Liu Y, Zhang W. Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography. Curr Med Imaging 2020; 16:1004-1021. [PMID: 33081662 DOI: 10.2174/1573405615666191113122840] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 10/11/2019] [Accepted: 10/19/2019] [Indexed: 12/31/2022]
Abstract
OBJECTIVE False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. METHODS Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. RESULTS The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. CONCLUSION The result shows that the proposed scheme can be significant for false-positive nodule reduction task.
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Affiliation(s)
- Xiaoqi Lu
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China,Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Yu Gu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Lidong Yang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Baohua Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Ying Zhao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Jianfeng Zhao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Lixin Gao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Foreign Languages, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Tao Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Yang Liu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Wei Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
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Shaukat F, Raja G, Frangi AF. Computer-aided detection of lung nodules: a review. J Med Imaging (Bellingham) 2019. [DOI: 10.1117/1.jmi.6.2.020901] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Furqan Shaukat
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Gulistan Raja
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Alejandro F. Frangi
- University of Leeds Woodhouse Lane, School of Computing and School of Medicine, Leeds
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