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Detection of Changes in CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and the Relationship with CT Signs. CONTRAST MEDIA & MOLECULAR IMAGING 2023; 2023:1421709. [PMID: 36851977 PMCID: PMC9966566 DOI: 10.1155/2023/1421709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 02/20/2023]
Abstract
Objective To investigate the relationship between the detection of changes in the levels of carcinoembryonic antigen (CEA) and progastrin-releasing peptide (ProGRP) in bronchoalveolar lavage fluid (BALF) and CT signs in patients with peripheral lung cancer. Methods Retrospective analysis of 108 patients with perihilar lung cancer who attended our hospital from January 2019 to January 2022, 54 cases were randomly selected as the observation group and 50 cases as the control group. Patients in both groups received CT examination and BALF test at the same time to observe and compare the differences in serum levels, the relationship between CT signs and serum indices, and the diagnostic value of peripheral lung cancer between the two groups. Results The serum levels of ProGrp, CEA, CA211, and NSE in the observation group were significantly higher than those in the control group, and the difference was statistically significant (P < 0.05). The morphology, density, mass enhancement pattern, bronchial morphology, obstructive signs, and lymph node fusion of CT signs were compared between the observation group and the control group, indicating that CT signs were more helpful for the localization, diagnosis, and staging of lung cancer. The results of ROC curve analysis showed that the AUC value of low-dose CT combined with serum ProGrp, CEA, CA211, and NSE was 0.892, sensitivity was 96.21%, and specificity of 90.05%, which were significantly higher than those of the single tests, respectively. The positive likelihood ratio was 84.41% and the negative likelihood ratio was 87.11%. Conclusion The combination of CT signs and serum tumour markers helps to improve the detection rate, sensitivity, and specificity of lung cancer, which has a high diagnostic rate for lung cancer and may provide evidence for the early diagnosis of lung cancer.
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Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
- *Correspondence: Meredith A. Jones,
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
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Random Walk Algorithm-Based Computer Tomography (CT) Image Segmentation Analysis Effect of Spiriva Combined with Symbicort on Immunologic Function of Non-Small-Cell Lung Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1986647. [PMID: 35693265 PMCID: PMC9187478 DOI: 10.1155/2022/1986647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 11/18/2022]
Abstract
The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (
). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (
,
), but there was no great difference from the group C and the group W (
). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (
,
), and there was no statistical difference between the group C and W (
). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.
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Jones MA, Faiz R, Qiu Y, Zheng B. Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features. Phys Med Biol 2022; 67:10.1088/1361-6560/ac5297. [PMID: 35130517 PMCID: PMC8935657 DOI: 10.1088/1361-6560/ac5297] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/07/2022] [Indexed: 12/20/2022]
Abstract
Objective.Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance.Approach.We retrospectively assembled a dataset involving 1535 lesions (740 malignant and 795 benign). Regions of interest (ROI) surrounding suspicious lesions are extracted and two types of features are computed from each ROI. The first one includes 40 radiomic features and the second one includes automated features computed from a VGG16 network using a transfer learning method. A single channel ROI image is converted to three channel pseudo-ROI images by stacking the original image, a bilateral filtered image, and a histogram equalized image. Two VGG16 models using pseudo-ROIs and 3 stacked original ROIs without pre-processing are used to extract automated features. Five linear support vector machines (SVM) are built using the optimally selected feature vectors from the handcrafted features, two sets of VGG16 model-generated automated features, and the fusion of handcrafted and each set of automated features, respectively.Main Results.Using a 10-fold cross-validation, the fusion SVM using pseudo-ROIs yields the highest lesion classification performance with area under ROC curve (AUC = 0.756 ± 0.042), which is significantly higher than those yielded by other SVMs trained using handcrafted or automated features only (p < 0.05).Significance.This study demonstrates that both handcrafted and automated futures contain useful information to classify breast lesions. Fusion of these two types of features can further increase CAD performance.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Rowzat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Danala G, Desai M, Ray B, Heidari M, Maryada SKR, Prodan CI, Zheng B. Applying Quantitative Radiographic Image Markers to Predict Clinical Complications After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study. Ann Biomed Eng 2022; 50:413-425. [PMID: 35112157 PMCID: PMC8918043 DOI: 10.1007/s10439-022-02926-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 01/24/2022] [Indexed: 12/14/2022]
Abstract
Accurately predicting clinical outcome of aneurysmal subarachnoid hemorrhage (aSAH) patients is difficult. The purpose of this study was to develop and test a new fully-automated computer-aided detection (CAD) scheme of brain computed tomography (CT) images to predict prognosis of aSAH patients. A retrospective dataset of 59 aSAH patients was assembled. Each patient had 2 sets of CT images acquired at admission and prior-to-discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and leaked extraparenchymal blood (EPB), respectively. CAD then detects sulci and computes 9 image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, and GM and 4 volumetrical ratios to sulci. Subsequently, applying a leave-one-case-out cross-validation method embedded with a principal component analysis (PCA) algorithm to generate optimal feature vector, 16 support vector machine (SVM) models were built using CT images acquired either at admission or prior-to-discharge to predict each of eight clinically relevant parameters commonly used to assess patients' prognosis. Finally, a receiver operating characteristics (ROC) method was used to evaluate SVM model performance. Areas under ROC curves of 16 SVM models range from 0.62 ± 0.07 to 0.86 ± 0.07. In general, SVM models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while SVM models trained using CT images acquired prior-to-discharge demonstrated higher accuracy in predicting long-term clinical outcomes. This study demonstrates feasibility to predict prognosis of aSAH patients using new quantitative image markers generated by SVM models.
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Affiliation(s)
- Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA.
| | - Masoom Desai
- Department of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA
| | - Bappaditya Ray
- Division of Neurocritical Care, Department of Neurology and Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA
| | | | - Calin I Prodan
- Department of Neurology, University of Oklahoma Medical Center, Oklahoma City, OK, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA
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Tong X, Li J. Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics. Eur J Radiol Open 2022; 9:100424. [PMID: 35600083 PMCID: PMC9120240 DOI: 10.1016/j.ejro.2022.100424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 11/01/2022] Open
Abstract
Objectives Methods Results Conclusion
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Zhao K, Zheng Q, Che T, Dyrba M, Li Q, Ding Y, Zheng Y, Liu Y, Li S. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis. Netw Neurosci 2021; 5:783-797. [PMID: 34746627 PMCID: PMC8567836 DOI: 10.1162/netn_a_00200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/08/2021] [Indexed: 12/13/2022] Open
Abstract
A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.
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Affiliation(s)
- Kun Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, China
| | - Tongtong Che
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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8
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Mirniaharikandehei S, Heidari M, Danala G, Lakshmivarahan S, Zheng B. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105937. [PMID: 33486339 PMCID: PMC7920928 DOI: 10.1016/j.cmpb.2021.105937] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/06/2021] [Indexed: 05/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric cancer patients using a small and imbalanced computed tomography (CT) image dataset. METHODS A retrospective dataset involving CT images acquired from 159 patients is assembled, including 121 and 38 cases with and without peritoneal metastasis, respectively. A computer-aided detection scheme is first applied to segment primary gastric tumor volumes and initially compute 315 image features. Then, five gradients boosting machine (GBM) models embedded with five feature selection methods (including random projection algorithm, principal component analysis, least absolute shrinkage, and selection operator, maximum relevance and minimum redundancy, and recursive feature elimination) along with a synthetic minority oversampling technique, are built to predict the risk of peritoneal metastasis. All GBM models are trained and tested using a leave-one-case-out cross-validation method. RESULTS Results show that the GBM model embedded with a random projection algorithm yields a significantly higher prediction accuracy (71.2%) than the other four GBM models (p<0.05). The precision, sensitivity, and specificity of this optimal GBM model are 65.78%, 43.10%, and 87.12%, respectively. CONCLUSIONS This study demonstrates that CT images of the primary gastric tumors contain discriminatory information to predict the risk of peritoneal metastasis, and a random projection algorithm is a promising method to generate optimal feature vector, improving the performance of machine learning based prediction models.
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Affiliation(s)
| | - Morteza Heidari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Gopichandh Danala
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | | | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
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Heidari M, Lakshmivarahan S, Mirniaharikandehei S, Danala G, Maryada SKR, Liu H, Zheng B. Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification. IEEE Trans Biomed Eng 2021; 68:2764-2775. [PMID: 33493108 DOI: 10.1109/tbme.2021.3054248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. METHODS We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. RESULTS Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). CONCLUSION The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. SIGNIFICANCE This study presents a new method to develop CAD schemes with significantly higher and robust performance.
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He J, Zhang H, Wang X, Sun Z, Ge Y, Wang K, Yu C, Deng Z, Feng J, Xu X, Hu S. A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:171-183. [PMID: 33325448 DOI: 10.3233/xst-200760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35 had ETE and 25 did not. Pre-contrast T2WI and T2WI-FS images depicting the largest section of tumor were selected. Tumor regions were manually segmented using ITK-SNAP software and 107 radiomics features were computed from the segmented regions using the open Pyradiomics package. Then, a random forest model was built to do classification in which the datasets were partitioned randomly 10 times to do training and testing with ratio of 1:1. Furthermore, forward greedy feature selection based on feature importance was adopted to reduce model overfitting. Classification accuracy was estimated on the test set using area under ROC curve (AUC). RESULTS The model using T2WI-FS image features yields much higher performance than the model using T2WI features (AUC = 0.906 vs. 0.760 using 107 features). Among the top 10 important features of T2WI and T2WI-FS, there are 5 common features. After feature selection, the models trained using top 2 features of T2WI and the top 6 features of T2WI-FS achieve AUC 0.845 and 0.928, respectively. Combining features computed from T2WI and T2WI-FS, model performance decreases slightly (AUC = 0.882 based on all features and AUC = 0.913 based on top features after feature selection). Adjusting hyper parameters of the random forest model have negligible influence on the model performance with mean AUC = 0.907 for T2WI-FS images. CONCLUSIONS Radiomics features based on pre-contrast T2WI and T2WI-FS is helpful to predict aggressive ETE in PTC. Particularly, the model trained using the optimally selected T2WI-FS image features yields the best classification performance. The most important features relate to lesion size and the texture heterogeneity of the tumor region.
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Affiliation(s)
- Junlin He
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital, Jiangnan University, Huihe Road, Wuxi, Jiangsu, China
| | - Xian Wang
- Department of Radiology, Affiliated Renmin Hospital, Jiangsu University, Dianli Road, Zhenjiang, Jiangsu, China
| | - Zongqiong Sun
- Department of Radiology, Affiliated Hospital, Jiangnan University, Huihe Road, Wuxi, Jiangsu, China
| | - Yuxi Ge
- Department of Radiology, Affiliated Hospital, Jiangnan University, Huihe Road, Wuxi, Jiangsu, China
| | - Kang Wang
- Department of Radiology, Affiliated Hospital, Jiangnan University, Huihe Road, Wuxi, Jiangsu, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhaohong Deng
- School of Digital Media, Jiangnan University and Jiangsu Key Laboratory of Digital Design and Software Technology, Digital Media Academy, Jiangnan University, China
| | | | - Xin Xu
- Haohua Technology Co., Ltd, Shanghai, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital, Jiangnan University, Huihe Road, Wuxi, Jiangsu, China
- Department of Radiology, Affiliated Renmin Hospital, Jiangsu University, Dianli Road, Zhenjiang, Jiangsu, China
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Chen B, Yang L, Zhang R, Luo W, Li W. Radiomics: an overview in lung cancer management-a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1191. [PMID: 33241040 PMCID: PMC7576016 DOI: 10.21037/atm-20-4589] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.
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Affiliation(s)
- Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Wenxin Luo
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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12
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Yin J, Qiu JJ, Qian W, Ji L, Yang D, Jiang JW, Wang JR, Lan L. A radiomics signature to identify malignant and benign liver tumors on plain CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:683-694. [PMID: 32568166 DOI: 10.3233/xst-200675] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signature was developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than Rα = 0.3703 (the critical value of R at significant level α = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians' diagnostic abilities and play an important role in RCADs.
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Affiliation(s)
- Jin Yin
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jia-Jun Qiu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Qian
- Department of Electric and Computer Engineering, University of Texas El Paso, El Paso, TX, USA
| | - Lin Ji
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing-Wen Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jun-Ren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Lan Lan
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
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Ge YX, Li J, Zhang JQ, Duan SF, Liu YK, Hu SD. Radiomics analysis of multicenter CT images for discriminating mucinous adenocarcinoma from nomucinous adenocarcinoma in rectal cancer and comparison with conventional CT values. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:285-297. [PMID: 32116286 DOI: 10.3233/xst-190614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.
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Affiliation(s)
- Yu-Xi Ge
- Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Jie Li
- Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Jun-Qin Zhang
- The First People's Hospital of Yuhang District, Hangzhou, China
| | | | - Yan-Kui Liu
- Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Shu-Dong Hu
- Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
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