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Shi L, Sheng M, Wei Z, Liu L, Zhao J. CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis. Acad Radiol 2023; 30:3064-3075. [PMID: 37385850 DOI: 10.1016/j.acra.2023.05.026] [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: 04/21/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023]
Abstract
RATIONALE AND OBJECTIVES More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies. MATERIALS AND METHODS PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity. RESULTS In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs. CONCLUSION CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People's Hospital, Nantong, China (M.S.)
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China (L.S., Z.W.)
| | - Lei Liu
- Institutes of Intelligence Medicine, Fudan University, Shanghai, China (L.L.)
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China (J.Z.).
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Ilić AŽ, de Luka SR, Popović TB, Debeljak-Martačić J, Kojadinović M, Ćirković S, Ristić-Djurović JL, Trbovich AM. Distinct fatty acid redistribution and textural changes in the brain tissue upon the static magnetic field exposure. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2022; 92:103853. [PMID: 35318121 DOI: 10.1016/j.etap.2022.103853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/24/2022] [Accepted: 03/16/2022] [Indexed: 06/14/2023]
Abstract
We observed different outcomes upon the subacute exposure to the 128 mT highly homogeneous static magnetic field (SMF) when its orientation was (i) aligned with the vertical component of the geomagnetic field; (ii) in the opposite direction. We employed the fatty acids (FA) composition and digital image analyses (DIA) to provide insights into the underlying processes and examine the possible weak SMF effects. Swiss-Webster male mice were whole-body exposed for 1 h/day over five days. Brain tissue's thin liquid chromatography resulted in brain FA composition, indicating a possible sequence of changes due to the SMF exposure. Quantitative DIA accurately assessed different image parameters. Delicate textural changes were revealed in the group where pathohistological or biochemical alterations have not been detected. DIA-based biological markers seem to be very promising for studying delicate tissue changes, which results from the high sensitivity and wide availability of DIA.
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Affiliation(s)
- Andjelija Ž Ilić
- Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Zemun, Belgrade, Serbia
| | - Silvio R de Luka
- Department of Pathological Physiology, Faculty of Medicine, University of Belgrade, Dr. Subotića 9, 11000 Belgrade, Serbia
| | - Tamara B Popović
- Institute for Medical Research, University of Belgrade, Centre of Excellence in Nutrition and Metabolism, Tadeuša Košćuška 1, Belgrade 11000, Serbia
| | - Jasmina Debeljak-Martačić
- Institute for Medical Research, University of Belgrade, Centre of Excellence in Nutrition and Metabolism, Tadeuša Košćuška 1, Belgrade 11000, Serbia
| | - Milica Kojadinović
- Institute for Medical Research, University of Belgrade, Centre of Excellence in Nutrition and Metabolism, Tadeuša Košćuška 1, Belgrade 11000, Serbia
| | - Saša Ćirković
- Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Zemun, Belgrade, Serbia
| | - Jasna L Ristić-Djurović
- Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Zemun, Belgrade, Serbia
| | - Alexander M Trbovich
- Department of Pathological Physiology, Faculty of Medicine, University of Belgrade, Dr. Subotića 9, 11000 Belgrade, Serbia.
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Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2021; 146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/09/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
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Affiliation(s)
| | | | | | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA and Knowledge Engineering Center, Global Biomedical Technologies, Inc, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy.
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Wan Q, Zhou J, Xia X, Hu J, Wang P, Peng Y, Zhang T, Sun J, Song Y, Yang G, Li X. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion. Front Oncol 2021; 11:683587. [PMID: 34868905 PMCID: PMC8637439 DOI: 10.3389/fonc.2021.683587] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To evaluate the performance of 2D and 3D radiomics features with different machine learning approaches to classify SPLs based on magnetic resonance(MR) T2 weighted imaging (T2WI). Material and Methods A total of 132 patients with pathologically confirmed SPLs were examined and randomly divided into training (n = 92) and test datasets (n = 40). A total of 1692 3D and 1231 2D radiomics features per patient were extracted. Both radiomics features and clinical data were evaluated. A total of 1260 classification models, comprising 3 normalization methods, 2 dimension reduction algorithms, 3 feature selection methods, and 10 classifiers with 7 different feature numbers (confined to 3–9), were compared. The ten-fold cross-validation on the training dataset was applied to choose the candidate final model. The area under the receiver operating characteristic curve (AUC), precision-recall plot, and Matthews Correlation Coefficient were used to evaluate the performance of machine learning approaches. Results The 3D features were significantly superior to 2D features, showing much more machine learning combinations with AUC greater than 0.7 in both validation and test groups (129 vs. 11). The feature selection method Analysis of Variance(ANOVA), Recursive Feature Elimination(RFE) and the classifier Logistic Regression(LR), Linear Discriminant Analysis(LDA), Support Vector Machine(SVM), Gaussian Process(GP) had relatively better performance. The best performance of 3D radiomics features in the test dataset (AUC = 0.824, AUC-PR = 0.927, MCC = 0.514) was higher than that of 2D features (AUC = 0.740, AUC-PR = 0.846, MCC = 0.404). The joint 3D and 2D features (AUC=0.813, AUC-PR = 0.926, MCC = 0.563) showed similar results as 3D features. Incorporating clinical features with 3D and 2D radiomics features slightly improved the AUC to 0.836 (AUC-PR = 0.918, MCC = 0.620) and 0.780 (AUC-PR = 0.900, MCC = 0.574), respectively. Conclusions After algorithm optimization, 2D feature-based radiomics models yield favorable results in differentiating malignant and benign SPLs, but 3D features are still preferred because of the availability of more machine learning algorithmic combinations with better performance. Feature selection methods ANOVA and RFE, and classifier LR, LDA, SVM and GP are more likely to demonstrate better diagnostic performance for 3D features in the current study.
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Affiliation(s)
- Qi Wan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jiaxuan Zhou
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoying Xia
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jianfeng Hu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Peng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | | | | | - Yang Song
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Naik A, Edla DR, Dharavath R. Prediction of Malignancy in Lung Nodules Using Combination of Deep, Fractal, and Gray-Level Co-Occurrence Matrix Features. BIG DATA 2021; 9:480-498. [PMID: 34191590 DOI: 10.1089/big.2020.0190] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Accurate detection of malignant tumor on lung computed tomography scans is crucial for early diagnosis of lung cancer and hence the faster recovery of patients. Several deep learning methodologies have been proposed for lung tumor detection, especially the convolution neural network (CNN). However, as CNN may lose some of the spatial relationships between features, we plan to combine texture features such as fractal features and gray-level co-occurrence matrix (GLCM) features along with the CNN features to improve the accuracy of tumor detection. Our framework has two advantages. First it fuses the advantage of CNN features with hand-crafted features such as fractal and GLCM features to gather the spatial information. Second, we reduce the overfitting effect by replacing the softmax layer with the support vector machine classifier. Experiments have shown that texture features such as fractal and GLCM when concatenated with deep features extracted from DenseNet architecture have a better accuracy of 95.42%, sensitivity of 97.49%, and specificity of 93.97%, and a positive predictive value of 95.96% with area under curve score of 0.95.
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Affiliation(s)
- Amrita Naik
- Department of Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India
| | - Damodar Reddy Edla
- Department of Computer Science and Engineering, National Institute of Technology, Ponda, Goa, India
| | - Ramesh Dharavath
- Department of Computer Science and Engineering, Indian Institute of Technology Dhanbad, Dhanbad, Jharkhand, India
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Abstract
PURPOSE OF REVIEW Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications. RECENT FINDINGS The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.
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Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon 2020; 6:e05194. [PMID: 33088959 PMCID: PMC7560585 DOI: 10.1016/j.heliyon.2020.e05194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/25/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Objective This study aimed to investigate the use of texture analysis for characterization of radicular cysts and periapical granulomas and to assess its efficacy to differentiate between both lesions with histological diagnosis. Methods Cone beam computed tomography (CBCT) images were obtained from 19 patients with 25 periapical lesions (14 radicular cysts and 11 periapical granulomas) confirmed by biopsy. Regions of interest were created in the lesions from which 11 texture parameters were calculated. Spearman's correlation analysis was performed and adjusted with Benjamini-Hochberg false discovery rate procedure (FDR <0.005). Results The texture parameters used to differentiate the lesions were assessed by using a receiver operating characteristic analysis. Five texture parameters were predictive of lesion differentiation for eight positions: angular second moment; sum of squares; sum of average; contrast; correlation. Conclusion Texture analysis of CBCT scans distinguishes radicular cysts from periapical granulomas and can be a promising diagnostic tool for periapical lesions. Clinical significance Texture analysis can be used in diagnostic and treatment monitoring to provide supplementary information.
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Ahn SJ, Kwon H, Yang JJ, Park M, Cha YJ, Suh SH, Lee JM. Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer. Sci Rep 2020; 10:8905. [PMID: 32483122 PMCID: PMC7264319 DOI: 10.1038/s41598-020-65470-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/30/2020] [Indexed: 01/01/2023] Open
Abstract
Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.
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Affiliation(s)
- Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Hyeokjin Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
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9
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Effects of simulated dose variation on contrast-enhanced CT-based radiomic analysis for Non-Small Cell Lung Cancer. Eur J Radiol 2020; 124:108804. [PMID: 31926387 DOI: 10.1016/j.ejrad.2019.108804] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/05/2019] [Accepted: 12/16/2019] [Indexed: 01/18/2023]
Abstract
PURPOSE To examine the potential effect of CT dose variation on radiomic features in vivo using simulated contrast-enhanced CT dose reduction in patients with non-small lung cell cancer (NSCLC). METHODS In this retrospective study, we included 69 patients (25 females, 44 males, median age 66 years) with histologically proven NSCLC who underwent a whole contrast-enhanced body FDG-PET/CT for primary staging. To simulate different CT dose levels, we used an algorithm to simulate low-dose CT images based on a noise model derived from phantom experiments. The tumor lesions and reference regions in the paraspinal muscle were manually segmented to obtain three-dimensional regions of interest. Radiomic feature extraction was performed using the PyRadiomics toolbox. The median relative feature value deviation was assessed for each feature and each dose level. RESULTS The mean segmented tumor volume was 340 ml. T-stages of the primary tumors were primarily T3/4. For NSCLCs, the median relative feature value deviation in the lowest dose images varied for the calculated features from 52.2% to -49.5%. In general, dose-dependent deviations of feature values showed a monotonous increase or decrease with decreasing dose levels. Statistical analyses revealed significant differences between the dose levels in 91% of features. CONCLUSIONS We examined the effects of simulated CT dose reduction on the values of radiomic features in primary NSCLC and showed significant deviations of varying degrees in numerous feature classes. This is a theoretical indicator of potential influence under real conditions, which should be taken into account in clinical use.
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Shim J, Yoon M, Lee Y. Feasibility of fast non local means filter in pediatric chest x-ray for increasing of pulmonary nodule detectability with 3D printed lung nodule phantom. JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2019; 39:872-890. [PMID: 31167171 DOI: 10.1088/1361-6498/ab2755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
General x-ray images have a lower probability of nodule detection than other modalities. Especially in children, the probability of nodule detection can likely drop due to poor image quality from using low radiation dose. To demonstrate the effectiveness of fast non-local means (FNLM) filter to increase the probability of nodule detection in pediatric chest x-ray images and reduce radiation dose while maintaining image quality. Quantitative assessment of normalised noise power spectrum (NNPS), coefficient of variation (COV) and contrast to noise ratio (CNR) were performed after applying four filters (median, Wiener, total variation and FNLM) on a 1-year-old child phantom. A 3D-printed patient nodule phantom was inserted into the phantom. Assessment was performed on AP and LAT view images acquired with the tube voltage reduced to 38 and 27%, and tube current reduced to 84 and 61%, respectively. The results showed the lowest NNPS and COV values and the highest CNR value when the FNLM filter applied. Moreover, the AP view results showed 37% decrease in COV and 30% increase in CNR in images with the FNLM filter applied (images exposed with the tube voltage and current reduced to 29% and 50%, respectively). The LAT view results showed 5% decrease in COV and 36% increase in CNR in images with the FNLM filter applied (images exposed with the tube current reduced by 27%). By applying the FNLM filter, the probability of nodule detection could be increased by denoising and contrast enhancement. Moreover, using the FNLM filter could reduce cancer risk in pediatric patients by reducing radiation dose about 30% to 44%.
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Affiliation(s)
- Jina Shim
- Department of Bio-Convergence Engineering, Korea University, Seoul, Republic of Korea. Department of Diagnostic Radiology, Severance Hospital, Seoul, Republic of Korea
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11
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Zhang G, Yang Z, Gong L, Jiang S, Wang L, Cao X, Wei L, Zhang H, Liu Z. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J Med Syst 2019; 43:181. [PMID: 31093830 DOI: 10.1007/s10916-019-1327-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 05/08/2019] [Indexed: 12/17/2022]
Abstract
As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Gong
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. .,Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
| | - Lu Wang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Xi Cao
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Lin Wei
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Hongyun Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Ziqi Liu
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
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12
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Ma Y, Feng W, Wu Z, Liu M, Zhang F, Liang Z, Cui C, Huang J, Li X, Guo X. Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes. Phys Med Biol 2018; 63:165018. [PMID: 30051884 DOI: 10.1088/1361-6560/aad648] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P < 0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P < 0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78-1.00), colour features showed an AUC of 0.85 (95% CI, 0.71-0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48-0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P = 0.010), but not significantly different from that for colour features only (P = 0.328). HSV colour features showed a similar performance to RGB colour features (P = 0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance.
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Affiliation(s)
- Yuan Ma
- School of Public Health, Capital Medical University, Beijing, People's Republic of China. Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, People's Republic of China
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13
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Ferreira JR, Oliveira MC, de Azevedo-Marques PM. Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture. J Digit Imaging 2018; 31:451-463. [PMID: 29047033 PMCID: PMC6113151 DOI: 10.1007/s10278-017-0029-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.
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Affiliation(s)
- José Raniery Ferreira
- Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil.
| | - Marcelo Costa Oliveira
- Institute of Computing, Federal University of Alagoas, Av. Lourival Melo Mota, Cidade Universitária, Maceió, Alagoas, 57072-900, Brazil
| | - Paulo Mazzoncini de Azevedo-Marques
- Center of Imaging Sciences and Medical Physics, Ribeirão Preto Medical School, University of São Paulo, Av. dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil
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Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research. J Digit Imaging 2018; 29:716-729. [PMID: 27440183 DOI: 10.1007/s10278-016-9894-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.
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Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer 2018; 115:34-41. [DOI: 10.1016/j.lungcan.2017.10.015] [Citation(s) in RCA: 216] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/14/2017] [Accepted: 10/29/2017] [Indexed: 10/18/2022]
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16
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Learning Lung Nodule Malignancy Likelihood from Radiologist Annotations or Diagnosis Data. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0317-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Eilaghi A, Baig S, Zhang Y, Zhang J, Karanicolas P, Gallinger S, Khalvati F, Haider MA. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC Med Imaging 2017. [PMID: 28629416 PMCID: PMC5477257 DOI: 10.1186/s12880-017-0209-5] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Background To assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC). Methods Thirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann–Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS). Results Uniformity (p < 0.001), entropy (p = 0.009), correlation (p < 0.001), and mean intensity (p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity (p = 0.045) and inverse difference normalized (p = 0.046) were associated with OS whereas tumor intensity (p = 0.366), tumor size (p = 0.611) and other textural features including uniformity (p = 0.334), entropy (p = 0.330) and correlation (p = 0.068) were not associated with OS. Conclusion CT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.
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Affiliation(s)
- Armin Eilaghi
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada.,Mechanical Engineering Department, Australian College of Kuwait, Kuwait City, Kuwait
| | - Sameer Baig
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada
| | - Yucheng Zhang
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada
| | - Junjie Zhang
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada
| | - Paul Karanicolas
- Department of Surgery, Sunnybrook Health Sciences Center, University of Toronto, Toronto, ON, Canada
| | - Steven Gallinger
- PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.,Hepatobiliary/pancreatic Surgical Oncology Program, University Health Network, Toronto, ON, Canada
| | - Farzad Khalvati
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada
| | - Masoom A Haider
- Department of Medical Imaging and Sunnybrook Research Institute, Sunnybrook Health Sciences Center, University of Toronto, 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5, ON, Canada.
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Xie Y, Zhang J, Liu S, Cai W, Xia Y. Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features. MEDICAL COMPUTER VISION AND BAYESIAN AND GRAPHICAL MODELS FOR BIOMEDICAL IMAGING 2017. [DOI: 10.1007/978-3-319-61188-4_11] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Ferreira de Lucena DJ, Ferreira Junior JR, Machado AP, Oliveira MC. Automatic weighing attribute to retrieve similar lung cancer nodules. BMC Med Inform Decis Mak 2016; 16 Suppl 2:79. [PMID: 27460071 PMCID: PMC4965736 DOI: 10.1186/s12911-016-0313-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules. METHODS In this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes. RESULTS The results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved. CONCLUSIONS Based on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.
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Affiliation(s)
- David Jones Ferreira de Lucena
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil.
| | - José Raniery Ferreira Junior
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
| | - Aydano Pamponet Machado
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
| | - Marcelo Costa Oliveira
- Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil
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Reeves AP, Xie Y, Jirapatnakul A. Automated pulmonary nodule CT image characterization in lung cancer screening. Int J Comput Assist Radiol Surg 2015; 11:73-88. [DOI: 10.1007/s11548-015-1245-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 06/08/2015] [Indexed: 02/06/2023]
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Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 2015; 14:9. [PMID: 25888834 PMCID: PMC4329222 DOI: 10.1186/s12938-015-0003-y] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 01/23/2015] [Indexed: 11/10/2022] Open
Abstract
Background Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. Methods The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. Results The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. Conclusions The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.
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Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 2014; 9:e110300. [PMID: 25330171 PMCID: PMC4203782 DOI: 10.1371/journal.pone.0110300] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 09/15/2014] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice. METHODOLOGY The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared. PRINCIPAL FINDINGS Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description. CONCLUSIONS In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.
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Affiliation(s)
- Lejla Alic
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Intelligent Imaging, Netherlands Organization for Applied Scientific Research (TNO), The Hague, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Jifke F. Veenland
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
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Wang J, Sun T, Gao N, Menon DD, Luo Y, Gao Q, Li X, Wang W, Zhu H, Lv P, Liang Z, Tao L, Liu X, Guo X. Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images. PLoS One 2014; 9:e108465. [PMID: 25250576 PMCID: PMC4177406 DOI: 10.1371/journal.pone.0108465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2013] [Accepted: 08/29/2014] [Indexed: 01/04/2023] Open
Abstract
Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.
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Affiliation(s)
- Jingjing Wang
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Tao Sun
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ni Gao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Desmond Dev Menon
- School of Medical Sciences, Edith Cowan University, Perth, Australia
- School of Exercise and Health Sciences, Edith Cowan University, Perth, Australia
| | - Yanxia Luo
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Qi Gao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xia Li
- School of Public Health, Capital Medical University, Beijing, China
- Department of Epidemiology & Public Health, University College Cork, Cork, Ireland
| | - Wei Wang
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
- School of Medical Sciences, Edith Cowan University, Perth, Australia
| | - Huiping Zhu
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Pingxin Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Zhigang Liang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiangtong Liu
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, Beijing, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
- * E-mail:
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Zhang Q, Li C, Han H, Yang L, Wang Y, Wang W. Computer-aided quantification of contrast agent spatial distribution within atherosclerotic plaque in contrast-enhanced ultrasound image sequences. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.03.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Wang JJ, Wu HF, Sun T, Li X, Wang W, Tao LX, Huo D, Lv PX, He W, Guo XH. Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters. Asian Pac J Cancer Prev 2014; 14:6019-23. [PMID: 24289618 DOI: 10.7314/apjcp.2013.14.10.6019] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.
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Affiliation(s)
- Jing-Jing Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China E-mail :
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Ha S, Choi H, Cheon GJ, Kang KW, Chung JK, Kim EE, Lee DS. Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result. Nucl Med Mol Imaging 2014; 48:278-86. [PMID: 26396632 DOI: 10.1007/s13139-014-0283-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 05/20/2014] [Accepted: 05/22/2014] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Texture analysis on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) scan is a relatively new imaging analysis tool to evaluate metabolic heterogeneity. We analyzed the difference in textural characteristics between non-small cell lung carcinoma (NSCLC) subtypes, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). METHODS Diagnostic (18)F-FDG PET/computed tomography (CT) scans of 30y patients (median age, 67; range, 42-88) with NSCLC (17 ADC and 13 SqCC) were retrospectively analyzed. Regions of interest were manually determined on selected transverse image containing the highest SUV value in tumors. Texture parameters were extracted by histogram-based algorithms, absolute gradient-based algorithms, run-length matrix-based algorithms, co-occurrence matrix-based algorithms, and autoregressive model-based algorithms. Twenty-four out of hundreds of texture features were selected by three algorithms: Fisher coefficient, minimization of both classification error probability and average correlation, and mutual information. Automated clustering of tumors was based on the most discriminating feature calculated by linear discriminant analysis (LDA). Each tumor subtype was determined by histopathologic examination after biopsy and surgery. RESULTS Fifteen texture features had significant different values between ADC and SqCC. LDA with 24 automate-selected texture features accurately clustered between ADC and SqCC with 0.90 linear separability. There was no high correlation between SUVmax and texture parameters (|r| ≤ 0.62). CONCLUSION Each subtype of NSCLC tumor has different metabolic heterogeneity. The results of this study support the potential of textural parameters on FDG PET as an imaging biomarker.
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Affiliation(s)
- Seunggyun Ha
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
| | - Gi Jeong Cheon
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea ; Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu Seoul, 110-744 Korea
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea
| | - June-Key Chung
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Euishin Edmund Kim
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea ; Department of Radiological Science, University of California at Irvine, Irvine, CA USA
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Korea ; Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Korea
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Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images. Artif Intell Med 2014; 61:105-18. [DOI: 10.1016/j.artmed.2014.05.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 05/14/2014] [Accepted: 05/16/2014] [Indexed: 11/20/2022]
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Cai H, Peng Y, Ou C, Chen M, Li L. Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach. PLoS One 2014; 9:e87387. [PMID: 24498092 PMCID: PMC3909149 DOI: 10.1371/journal.pone.0087387] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Accepted: 12/24/2013] [Indexed: 11/18/2022] Open
Abstract
Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported. Materials and Methods The study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination. Results Seven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively. Conclusion Multi-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.
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Affiliation(s)
- Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yanxia Peng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Caiwen Ou
- Key Laboratory of Construction and Detection of Guangdong Province, Southern Medical University, Guangzhou, People’s Republic of China
| | - Minsheng Chen
- Key Laboratory of Construction and Detection of Guangdong Province, Southern Medical University, Guangzhou, People’s Republic of China
- * E-mail: (LL); (MC)
| | - Li Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China
- * E-mail: (LL); (MC)
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Sun T, Wang J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X. Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:519-524. [PMID: 23727300 DOI: 10.1016/j.cmpb.2013.04.016] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2012] [Revised: 04/24/2013] [Accepted: 04/24/2013] [Indexed: 06/02/2023]
Abstract
Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In this paper, the usage of support vector machine (SVM) classification for lung cancer is investigated, presenting a systematic quantitative evaluation against Boosting, Decision trees, k-nearest neighbor, LASSO regressions, neural networks and random forests. A large database of 5984 regions of interest (ROIs) and 488 input features (including textural features, patient characteristics, and morphological features) were used to train the classifiers and evaluate for their performance. The evaluation for classifiers' performance was based on a tenfold cross validation framework, receiver operating characteristic curve (ROC), and Matthews correlation coefficient. Area under curve (AUC) of SVM, Boosting, Decision trees, k-nearest neighbor, LASSO, neural networks, random forests were 0.94, 0.86, 0.73, 0.72, 0.91, 0.92, and 0.85, respectively. It was proved that SVM classification offered significantly increased classification performance compared to the reference methods. This scheme may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future.
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Affiliation(s)
- Tao Sun
- School of Public Health, Capital Medical University, Beijing 100069, China.
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