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Zhang Y, Qu L, Zhang H, Wang Y, Gao G, Wang X, Zhang T. Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT. Front Med (Lausanne) 2024; 11:1403020. [PMID: 38975053 PMCID: PMC11224554 DOI: 10.3389/fmed.2024.1403020] [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: 03/18/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Background The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures. Methods We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established. Results (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0. Conclusion We analyzed the risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.
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Affiliation(s)
- Yifan Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Lin Qu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Haihua Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Ying Wang
- Department of Respiratory Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Guizhou Gao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Xiaodong Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Tao Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
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Logullo P, MacCarthy A, Dhiman P, Kirtley S, Ma J, Bullock G, Collins GS. Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018-2019. BJR Open 2023; 5:20220033. [PMID: 37389003 PMCID: PMC10301715 DOI: 10.1259/bjro.20220033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 07/01/2023] Open
Abstract
Objective This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant. Methods In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively. Results The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches. Conclusion The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications. Advances in knowledge We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines.
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Affiliation(s)
| | | | | | | | | | - Garrett Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
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Ma H, Guo H, Zhao M, Qi S, Li H, Tian Y, Li Z, Zhang G, Yao Y, Qian W. Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network. Med Phys 2022; 49:2555-2569. [PMID: 35092608 DOI: 10.1002/mp.15501] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantly essential. METHODS In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pre-trained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask RCNN to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracy further. RESULTS The experiments were conducted based on five-fold cross-validation with the imbalanced dataset. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the Competition Performance Metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposed method. CONCLUSIONS We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- He Ma
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, China
| | - Huimin Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
| | - Mingfang Zhao
- Department of Medical Oncology, the First Hospital of China Medical University, No.155 Nanjingbei Road, Shenyang, 110001, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, China
| | - Heming Li
- Department of Medical Oncology, the First Hospital of China Medical University, No.155 Nanjingbei Road, Shenyang, 110001, China
| | - Yumeng Tian
- Department of Medical Oncology, the First Hospital of China Medical University, No.155 Nanjingbei Road, Shenyang, 110001, China
| | - Zhi Li
- Department of Medical Oncology, the First Hospital of China Medical University, No.155 Nanjingbei Road, Shenyang, 110001, China
| | | | - Yudong Yao
- Deparment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Wei Qian
- Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, 79968, USA
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Abstract
AbstractModern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way. One of the most important applications is evaluation of CT brain scans, where the most precise results come from deep learning approaches. In this article, we propose a novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture. The support neural network helps CNN to find the most adequate filers for pooling and convolution layers. As a result, the main neural classifier learns faster and reaches higher efficiency. Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall. We have described our proposed mechanism and discussed numerical results to draw conclusions and show future works.
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Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Med Biol Eng Comput 2020; 58:1239-1250. [PMID: 32221796 DOI: 10.1007/s11517-020-02159-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 03/12/2020] [Indexed: 12/19/2022]
Abstract
High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis face the problem of insufficient annotated data. In this paper, we present a semi-supervised learning method for chronic gastritis classification using gastric X-ray images. The proposed semi-supervised learning method based on tri-training can leverage unannotated data to boost the performance that is achieved with a small amount of annotated data. We utilize a novel learning method named Between-Class learning (BC learning) that can considerably enhance the performance of our semi-supervised learning method. As a result, our method can effectively learn from unannotated data and achieve high diagnostic accuracy for chronic gastritis. Graphical Abstract Gastritis classification using gastric X-ray images with semi-supervised learning.
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Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, Shasky C, Zhang YD, Tavares JMR, Raja NSM. Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.013] [Citation(s) in RCA: 158] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review. Diagnostics (Basel) 2019; 9:E29. [PMID: 30866425 PMCID: PMC6468920 DOI: 10.3390/diagnostics9010029] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/29/2019] [Accepted: 02/19/2019] [Indexed: 12/27/2022] Open
Abstract
The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%⁻97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.
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Affiliation(s)
- Lea Marie Pehrson
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark.
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
| | - Carsten Ammitzbøl Lauridsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark.
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