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Ping X, Jiang N, Meng Q, Hu C. Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images. Tomography 2024; 10:1042-1053. [PMID: 39058050 PMCID: PMC11280730 DOI: 10.3390/tomography10070078] [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: 05/21/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.
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Affiliation(s)
| | | | | | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188, Shizi Street, Suzhou 215006, China; (X.P.); (N.J.); (Q.M.)
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Woodworth CF, Frota Lima LM, Bartholmai BJ, Koo CW. Imaging of Solid Pulmonary Nodules. Clin Chest Med 2024; 45:249-261. [PMID: 38816086 DOI: 10.1016/j.ccm.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.
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Affiliation(s)
- Claire F Woodworth
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Livia Maria Frota Lima
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Brian J Bartholmai
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Chi Wan Koo
- Department of Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
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Barbosa-Martins J, Mendonça J, Carvalho N, Carvalho C, Soutinho G, Sarmento H, Coutinho C, Cotter J. Development of a predictive score to discriminate community acquired pneumonia with underlying lung cancer: A retrospective case - control study. Respir Med 2024; 229:107675. [PMID: 38782137 DOI: 10.1016/j.rmed.2024.107675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND A pneumonic infiltrate might hide an occult lung cancer (LC). This awareness depends on each clinician personal experience, turning definitive LC diagnosis challenging and possibly delayed. In this study we aimed to develop a clinical score to better identify those cases. MATERIALS AND METHODS We conducted a retrospective case-control study, including previously undiagnosed LC patients admitted in our institution, with a presumptive suspicious of community acquired pneumonia (CAP). Cases were compared with random CAP inpatient controls, using a matched 2:1 ratio. Demographic, clinical, and laboratorial variables were assessed for a possible association with the presence of a CAP with underlying LC (CAP-uLC). RESULTS Among 535 hospitalized LC patients, 43 cases had a presentation compatible with CAP and were compared with 86 CAP controls. A scoring system was built using 6 independent variables, which positively correlated with CAP-uLC: smoking history (OR: 8.3 [1.9-36.2]; p = 0.005); absence of fever (6.5 [2.0-21.5]; p = 0.002); sputum with blood (5.9 [1.2-29.9]; p = 0.033); platelet count ≥ 232x103/μL (5.8 [1.6-20.6]; p = 0.006); putative alternative diagnosis than CAP (4.6 [1.5-14.7]; p = 0.009); and duration of symptoms ≥ 10 days (3.7 [1.1-13.0]; p = 0.037). Our score presented an AUC of 0.910 (95 % CI, 0.852-0.967; p < 0.001), a sensitivity of 88.1 % and specificity of 84.7 %, in predicting the risk of presenting a CAP-uLC, when set to a cutoff of 18. CONCLUSION We propose a novel risk score aimed to aid clinicians identifying patients with CAP-uLC in the acute setting, possibly prompting early LC diagnosis.
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Affiliation(s)
- João Barbosa-Martins
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Joana Mendonça
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Nuno Carvalho
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Carolina Carvalho
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Gustavo Soutinho
- EPIUnit, Institute of Public Health of the University of Porto (ISPUP), Porto, Portugal.
| | - Helena Sarmento
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Camila Coutinho
- Medical Oncology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
| | - Jorge Cotter
- Internal Medicine Department, Hospital da Senhora da Oliveira, Guimarães, Portugal.
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Yan Q, Zhao W, Kong H, Chi J, Dai Z, Yu D, Cui J. CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis. Exp Ther Med 2024; 27:112. [PMID: 38361522 PMCID: PMC10867735 DOI: 10.3892/etm.2024.12400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/02/2023] [Indexed: 02/17/2024] Open
Abstract
Global incidence rate of non-tuberculous mycobacteria (NTM) pulmonary disease has been increasing rapidly. In some countries and regions, its incidence rate is higher than that of tuberculosis. It is easily confused with tuberculosis. The topic of this study is to identify two diseases using CT radioomics. The aim in the present study was to investigate the value of CT-based radiomics to analyze consolidation features in differentiation of non-tuberculous mycobacteria (NTM) from pulmonary tuberculosis (TB). A total of 156 patients (75 with NTM pulmonary disease and 81 with TB) exhibiting consolidation characteristics in Shandong Public Health Clinical Center were retrospectively analyzed. Subsequently, 305 regions of interest of CT consolidation were outlined. Using a random number generated via a computer, 70 and 30% of consolidations were allocated to the training and the validation cohort, respectively. By means of variance threshold, when investigating the effective radiomics features, SelectKBest and the least absolute shrinkage and selection operator regression method were employed for feature selection and combined to calculate the radiomics score. K-nearest neighbor (KNN), support vector machine (SVM) and logistic regression (LR) were used to analyze effective radiomics features. A total of 18 patients with NTM pulmonary disease and 18 with TB possessing consolidation characteristics in Jinan Infectious Disease Hospital were collected for external validation of the model. A total of three methods was used in the selection of 52 optimal features. For KNN, the area under the curve (AUC; sensitivity, specificity) for the training and validation cohorts were 0.98 (0.93, 0.94) and 0.90 (0.88, 083), respectively; for SVM, AUC was 0.99 (0.96, 0.96) and 0.92 (0.86, 0.85) and for LR, AUC was 0.99 (0.97, 0.97) and 0.89 (0.88, 0.85). In the external validation cohort, AUC values of models were all >0.84 and LR classifier exhibited the most significant precision, recall and F1 score (0.87, 0.94 and 0.88, respectively). LR classifier possessed the best performance in differentiating diseases. Therefore, CT-based radiomics analysis of consolidation features may distinguish NTM pulmonary disease from TB.
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Affiliation(s)
- Qinghu Yan
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Wenlong Zhao
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Haili Kong
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Jingyu Chi
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
| | - Zhengjun Dai
- Huiying Medical Technology (Beijing) Co., Ltd., Beijing 100192, P.R. China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, P.R. China
| | - Jia Cui
- Department of Radiology, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong 250013, P.R. China
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Zhu F, Yang C, Zou J, Ma W, Wei Y, Zhao Z. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis. Acta Radiol 2023; 64:3074-3084. [PMID: 37817511 DOI: 10.1177/02841851231205737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.
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Affiliation(s)
- Fandong Zhu
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Chen Yang
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Jiajun Zou
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Weili Ma
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Hangzhou, Zhejiang, PR China
| | - Zhenhua Zhao
- Department of Radiology, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, Shaoxing People's Hospital, Shaoxing, PR China
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Yu X, Zhang S, Xu J, Huang Y, Luo H, Huang C, Nie P, Deng Y, Mao N, Zhang R, Gao L, Li S, Kang B, Wang X. Nomogram Using CT Radiomics Features for Differentiation of Pneumonia-Type Invasive Mucinous Adenocarcinoma and Pneumonia: Multicenter Development and External Validation Study. AJR Am J Roentgenol 2023; 220:224-234. [PMID: 36102726 DOI: 10.2214/ajr.22.28139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND. Pneumonia-type invasive mucinous adenocarcinoma (IMA) and pneumonia show overlapping chest CT features as well as overlapping clinical characteristics. OBJECTIVE. The purpose of our study was to develop and validate a nomogram combining clinical and CT-based radiomics features to differentiate pneumonia-type IMA and pneumonia. METHODS. This retrospective study included 314 patients (172 men, 142 women; mean age, 60.3 ± 14.5 [SD] years) from six hospitals who underwent noncontrast chest CT showing consolidation and were diagnosed with pneumonia-type IMA (n = 106) or pneumonia (n = 208). Patients from three hospitals formed a training set (n = 195) and a validation set (n = 50), and patients from the other three hospitals formed the external test set (n = 69). A model for predicting pneumonia-type IMA was built using clinical characteristics that were significant independent predictors of this diagnosis. Radiomics features were extracted from CT images by placing ROIs on areas of consolidation, and a radiomics signature of pneumonia-type IMA was constructed. A nomogram for predicting pneumonia-type IMA was constructed that combined features in the clinical model and the radiomics signature. Two cardiothoracic radiologists independently reviewed CT images in the external test set to diagnose pneumonia-type IMA. Diagnostic performance was compared among models and radiologists. Decision curve analysis (DCA) was performed. RESULTS. The clinical model included fever and family history of lung cancer. The radiomics signature included 15 radiomics features. DCA showed higher overall net benefit from the nomogram than from the clinical model. In the external test set, AUC was higher for the nomogram (0.85) than for the clinical model (0.71, p = .01), radiologist 1 (0.70, p = .04), and radiologist 2 (0.67, p = .01). In the external test set, the nomogram had sensitivity of 46.9%, specificity of 94.6%, and accuracy of 72.5%. CONCLUSION. The nomogram combining clinical variables and CT-based radiomics features outperformed the clinical model and two cardiothoracic radiologists in differentiating pneumonia-type IMA from pneumonia. CLINICAL IMPACT. The findings support potential clinical use of the nomogram for diagnosing pneumonia-type IMA in patients with consolidation on chest CT.
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Affiliation(s)
- Xinxin Yu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Yong Huang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hao Luo
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
| | - Pei Nie
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yan Deng
- Department of Radiology, Qilu Hospital, Shandong University, Jinan, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Ran Zhang
- Huiying Medical Technology Co., Ltd., Beijing, China
| | - Lin Gao
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Sha Li
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, No. 324, Jingwu Rd, Jinan, 250021, China
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Shen J, Du H, Wang Y, Du L, Yang D, Wang L, Zhu R, Zhang X, Wu J. A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule. Front Oncol 2022; 12:1035307. [PMID: 36591441 PMCID: PMC9798090 DOI: 10.3389/fonc.2022.1035307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Objective To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index. Methods A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity. Results The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit. Conclusion This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.
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Affiliation(s)
- Jing Shen
- Graduate School, Tianjin Medical University, Tianjin, China,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Hai Du
- Graduate School, Tianjin Medical University, Tianjin, China,Department of Radiology, Ordos Central Hospital, Ordos Inner Mongolia, China
| | - Yadong Wang
- School of Medicine, Dalian University, Dalian, China,Department of Research, Dalian Detecsen Biomedical Co., LTD, Dalian, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,Graduate School, Dalian Medical University, Dalian, China
| | - Dong Yang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,Graduate School, Dalian University, Dalian, China
| | - Lingwei Wang
- Department of Cardio-Thoracic Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ruiping Zhu
- Department of Pathology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Xiaohui Zhang
- College of Environment and Chemical Engineering, Dalian University, Dalian, China,*Correspondence: Jianlin Wu, ; Xiaohui Zhang,
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,*Correspondence: Jianlin Wu, ; Xiaohui Zhang,
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Zhang R, Wei Y, Shi F, Ren J, Zhou Q, Li W, Chen B. The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images. BMC Cancer 2022; 22:1118. [PMID: 36319968 PMCID: PMC9628173 DOI: 10.1186/s12885-022-10224-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/17/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules. METHODS Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas. RESULTS Totally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence. CONCLUSIONS The radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.
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Affiliation(s)
- Rui Zhang
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jing Ren
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weimin Li
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
| | - Bojiang Chen
- grid.13291.380000 0001 0807 1581Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province 610041 People’s Republic of China
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Luo C, Song Y, Liu Y, Wang R, Gao J, Yue S, Ding C. Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study. BMC Med Imaging 2022; 22:16. [PMID: 35105314 PMCID: PMC8808962 DOI: 10.1186/s12880-022-00745-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 01/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As a rare benign lung tumour, pulmonary sclerosing pneumocytoma (PSP) is often misdiagnosed as atypical peripheral lung cancer (APLC) on routine imaging examinations. This study explored the value of enhanced CT combined with texture analysis to differentiate between PSP and APLC. METHODS Forty-eight patients with PSP and fifty patients with APLC were retrospectively enrolled. The CT image features of the two groups of lesions were analysed, and MaZda software was used to evaluate the texture of CT venous phase thin-layer images. Independent sample t-test, Mann-Whitney U tests or χ2 tests were used to compare between groups. The intra-class correlation coefficient (ICC) was used to analyse the consistency of the selected texture parameters. Spearman correlation analysis was used to evaluate the differences in texture parameters between the two groups. Based on the statistically significant CT image features and CT texture parameters, the independent influencing factors between PSP and APLC were analysed by multivariate logistic regression. Extremely randomized trees (ERT) was used as the classifier to build models, and the models were evaluated by the five-fold cross-validation method. RESULTS Logistic regression analysis based on CT image features showed that calcification and arterial phase CT values were independent factors for distinguishing PSP from APLC. The results of logistic regression analysis based on CT texture parameters showed that WavEnHL_s-1 and Perc.01% were independent influencing factors to distinguish the two. Compared with the single-factor model (models A and B), the classification accuracy of the model based on image features combined with texture parameters was 0.84 ± 0.04, the AUC was 0.84 ± 0.03, and the sensitivity and specificity were 0.82 ± 0.13 and 0.87 ± 0.12, respectively. CONCLUSION Enhanced CT combined with texture analysis showed good diagnostic value for distinguishing PSP and APLC, which may contribute to clinical decision-making and prognosis evaluation.
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Affiliation(s)
- Chenglong Luo
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Yiman Song
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Yiyang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Songwei Yue
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China
| | - Changmao Ding
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
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Zhu H, Song Y, Huang Z, Zhang L, Chen Y, Tao G, She Y, Sun X, Yu H. Accurate prediction of epidermal growth factor receptor mutation status in early-stage lung adenocarcinoma, using radiomics and clinical features. Asia Pac J Clin Oncol 2022; 18:586-594. [PMID: 35098682 DOI: 10.1111/ajco.13641] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 07/01/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVES To develop a nomogram based on CT radiomics and clinical features to predict the epidermal growth factor receptor (EGFR) mutations in early-stage lung adenocarcinomas. METHODS A retrospective analysis of postoperative patients with pathologically confirmed lung adenocarcinoma, which had been tested for EGFR mutations was performed from January 2015 to December 2015. Patients were randomly assigned to training and validation cohorts. A total of 1,078 radiomics features were extracted. least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select clinical and radiomics features, and to establish predictive models. The radiomics score (rad-score) of each patient was calculated. The discrimination of the model was evaluated with area under the curve. RESULTS 1092 patients (444 men and 648 women; mean age: 59.59±9.6) were enrolled. The radiomics signature consisted of 28 radiomics features and emphysema. The mean validation cohort result of the rad-score for patients with EGFR mutations (0.814±0.988) was significantly higher than those with EGFR wild-type (0.315±1.237; p = 0.001). When combined with clinical features, LASSO regression analysis revealed four radiomics features, emphysema, and three clinical features including sex, age, and histologic subtype as associated with to EGFR mutation status. The nomogram that combined radiomics and clinical features significantly improved the predictive discrimination (AUC: 0.723), which is better than that of the radiomics signature alone (AUC: 0.646). CONCLUSION A relationship between selected radiomics features and EGFR mutant lung adenocarcinomas is demonstrated. A nomogram, combining radiomics features and clinical features for EGFR prediction in early-stage lung adenocarcinomas, has shown a moderate discriminatory efficiency and high sensitivity, providing additional information for clinicians.
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Affiliation(s)
- Huiyuan Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Department of Radiology, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China
| | - Yueqiang Song
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, P.R. China
| | - Zike Huang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lian Zhang
- Department of Radiology, Jiading Hospital of Traditional Chinese Medicine, Shanghai, China
| | - Yanqing Chen
- Department of Radiology, People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yunlang She
- Department of Thoracic surgery, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, China
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
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11
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Peng S, Pan L, Guo Y, Gong B, Huang X, Liu S, Huang J, Pu H, Zeng J. Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non- COVID-19 (highly suspected) pneumonia patients during the epidemic period. PLoS One 2022; 17:e0256194. [PMID: 35025878 PMCID: PMC8758079 DOI: 10.1371/journal.pone.0256194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 07/31/2021] [Indexed: 12/21/2022] Open
Abstract
Objectives
COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia.
Materials and methods
145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance.
Results
Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs.
Conclusions
The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.
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Affiliation(s)
- Shengkun Peng
- Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lingai Pan
- Department of Critical Care Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Guo
- Department of Critical Care Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Gong
- The Key Laboratory for Human Disease Gene Study of Sichuan Province, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Huang
- Department of Critical Care Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyun Liu
- GE Healthcare (China), Beijing, China
| | - Jianxin Huang
- Department of Anesthesiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- * E-mail:
| | - Jie Zeng
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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12
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Yan Q, Wang W, Zhao W, Zuo L, Wang D, Chai X, Cui J. Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics. BMC Pulm Med 2022; 22:4. [PMID: 34991543 PMCID: PMC8740493 DOI: 10.1186/s12890-021-01766-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 11/24/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity. METHODS 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features. RESULTS 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93. CONCLUSION The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.
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Affiliation(s)
- Qinghu Yan
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Wuzhang Wang
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Wenlong Zhao
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China
| | - Liping Zuo
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Dongdong Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Xiangfei Chai
- Huiying Medical Technology (Beijing) Co., Ltd, Beijing, 100192, China
| | - Jia Cui
- Department of Radiology, Shandong Public Health Clinical Center, Jinan, 250013, China.
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13
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Ji H, Liu Q, Chen Y, Gu M, Chen Q, Guo S, Ning S, Zhang J, Li WH. Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study. Front Endocrinol (Lausanne) 2022; 13:997921. [PMID: 36726465 PMCID: PMC9884819 DOI: 10.3389/fendo.2022.997921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/17/2023] Open
Abstract
PURPOSE The purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters. METHODS A total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA). RESULTS The radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83-0.96) in the training cohort and 0.88 (95% CI, 0.79-0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90-0.98) and 0.91 (95% CI, 0.84-0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer-Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model. CONCLUSIONS The nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult.
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Affiliation(s)
- Huijun Ji
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qianqian Liu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Yingxiu Chen
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Mengyao Gu
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Qi Chen
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shaolan Guo
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Shangkun Ning
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Juntao Zhang
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wan-Hu Li
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
- *Correspondence: Wan-Hu Li,
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14
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Luo Y, Li Y, Zhang Y, Zhang J, Liang M, Jiang L, Guo L. Parameter tuning in machine learning based on radiomics biomarkers of lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:477-490. [PMID: 35342074 DOI: 10.3233/xst-211096] [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: 06/14/2023]
Abstract
BACKGROUND Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate. OBJECTIVE To improve predictive performance of radiomics features for lung cancer by tuning the machine learning model parameters. METHODS Using a dataset involving 263 cases (125 benign and 138 malignant) acquired from our hospital, each classifier model is trained and tested using 237 and 26 cases, respectively. We initially extract 867 radiomics features of CT images for model development and then test 10 feature selections and 7 models to determine the best method. We further tune the parameter of the final model to reach the best performance. The adjusted final model is then validated using 224 cases acquired from Lung Image Database Consortium (LIDC) dataset (64 benign and 160 malignant) with the same set of selected radiomics features. RESULTS During model development, the feature selection via concave minimization method show the best performance of area under ROC curve (AUC = 0.765), followed by l0-norm regularization (AUC = 0.741) and Fisher discrimination criterion (AUC = 0.734). Support vector machine (SVM) and random forest (RF) are the top two machine learning algorithms showing the best performance (AUC = 0.765 and 0.734, respectively), using by the default parameter. After parameter tuning, SVM with linear kernel achieves the best performance (AUC = 0.837), whereas the best tuned RF with the number of trees is 510 and yields a slightly lower performance (AUC = 0.775) in 26 test samples data. During model validation, the SVM and RF models yield AUC = 0.78 and 0.77, respectively. CONCLUSION Appropriate quantitative radiomics features and accurate parameters can improve the model's performance to predict lung cancer.
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Affiliation(s)
- Yuan Luo
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yifan Li
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Yuwei Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Jianwei Zhang
- Department of Radiology, Tianjin Baodi Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Lin Jiang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
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15
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Chen X, Li W, Wang F, He L, Liu E. Early recognition of necrotizing pneumonia in children based on non-contrast-enhanced computed tomography radiomics signatures. Transl Pediatr 2021; 10:1542-1551. [PMID: 34295769 PMCID: PMC8261593 DOI: 10.21037/tp-20-241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 04/16/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Necrotizing pneumonia (NP) is an infrequent but severe complication of pneumonia in children. In the early stages of NP, CT imaging shows lung consolidation, which cannot be detected in time. This study aimed to explore the ability of non-contrast-enhanced CT radiomics features to recognize NP in early stage. METHODS This was a retrospective study, and 250 patients who presented with lung consolidation on initial CT images were included in this study. After a follow-up period of 1-3 weeks, 116 patients developed NP, whose CT or X-ray shows cavitation or liquefied necrosis. Manual segmentation of lesion sites in the initial non-contrast-enhanced CT scans was performed with RadCloud (Huiying Medical Technology Co., Ltd., China), and 1,409 radiomics features were extracted. We used Variance threshold (0.8), SelectKBest, and the least absolute shrinkage and selection operator (LASSO) methods for feature dimension reduction. Three machine learning algorithms, k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models, were established to recognize NP early. To assess the recognition performance, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and other indicators were used in the validation cohort. RESULTS Radiomics features helped to recognize NP in early stage in both the training and validation cohorts. The AUC (sensitivity, specificity) for the training and validation cohorts were 0.81 (0.73, 0.68) and 0.71 (0.61, 0.65) for KNN, respectively; 0.81 (0.72, 0.70) and 0.77 (0.66, 0.65) for SVM, respectively; and 0.82 (0.73, 0.73) and 0.76 (0.63, 0.70) for LR, respectively. Recall and F1-scores determined that LR performed better at diagnosing early NP, with the values of the above two indexes being 0.70 and 0.67, respectively. CONCLUSIONS Non-contrast-enhanced CT-based radiomics models may be helpful for recognizing NP in early stage.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Weiguo Li
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China.,Department of Respiratory, Medicine Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Fang Wang
- Huiying Medical Technology Co. Ltd., Beijing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Enmei Liu
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China.,Department of Respiratory, Medicine Children's Hospital of Chongqing Medical University, Chongqing, China
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16
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CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias. Radiol Med 2021; 126:1037-1043. [PMID: 34043146 PMCID: PMC8155795 DOI: 10.1007/s11547-021-01370-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/05/2021] [Indexed: 01/08/2023]
Abstract
Purpose To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
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El Ayachy R, Giraud N, Giraud P, Durdux C, Giraud P, Burgun A, Bibault JE. The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up. Front Oncol 2021; 11:603595. [PMID: 34026602 PMCID: PMC8131863 DOI: 10.3389/fonc.2021.603595] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/06/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care. METHODS A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020. RESULTS We identified several studies at each point of patient's care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications. CONCLUSION Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
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Affiliation(s)
- Radouane El Ayachy
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Nicolas Giraud
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
- Radiation Oncology Department, Haut-Lévêque Hospital, CHU de Bordeaux, Pessac, France
| | - Paul Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Catherine Durdux
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Anita Burgun
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
| | - Jean Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
- INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Cordeliers Research Centre, Paris Descartes University, Paris, France
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Gülbay M, Özbay BO, Mendi BAR, Baştuğ A, Bodur H. A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias? PLoS One 2021; 16:e0246582. [PMID: 33690730 PMCID: PMC7946299 DOI: 10.1371/journal.pone.0246582] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/21/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To evaluate the discrimination of parenchymal lesions between COVID-19 and other atypical pneumonia (AP) by using only radiomics features. METHODS In this retrospective study, 301 pneumonic lesions (150 ground-glass opacity [GGO], 52 crazy paving [CP], 99 consolidation) obtained from nonenhanced thorax CT scans of 74 AP (46 male and 28 female; 48.25±13.67 years) and 60 COVID-19 (39 male and 21 female; 48.01±20.38 years) patients were segmented manually by two independent radiologists, and Location, Size, Shape, and First- and Second-order radiomics features were calculated. RESULTS Multiple parameters showed significant differences between AP and COVID-19-related GGOs and consolidations, although only the Range parameter was significantly different for CPs. Models developed by using the Bayesian information criterion (BIC) for the whole group of GGO and consolidation lesions predicted COVID-19 consolidation and AP GGO lesions with low accuracy (46.1% and 60.8%, respectively). Thus, instead of subjective classification, lesions were reclassified according to their skewness into positive skewness group (PSG, 78 AP and 71 COVID-19 lesions) and negative skewness group (NSG, 56 AP and 44 COVID-19 lesions), and group-specific models were created. The best AUC, accuracy, sensitivity, and specificity were respectively 0.774, 75.8%, 74.6%, and 76.9% among the PSG models and 0.907, 83%, 79.5%, and 85.7% for the NSG models. The best PSG model was also better at predicting NSG lesions smaller than 3 mL. Using an algorithm, 80% of COVID-19 and 81.1% of AP patients were correctly predicted. CONCLUSION During periods of increasing AP, radiomics parameters may provide valuable data for the differential diagnosis of COVID-19.
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Affiliation(s)
- Mutlu Gülbay
- Department of Radiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Bahadır Orkun Özbay
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Bökebatur Ahmet Raşit Mendi
- Department of Radiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Aliye Baştuğ
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
| | - Hürrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, Ankara Numune Education and Research Hospital, Ankara City Hospital, Universiteler Mahallesi, Ankara, Çankaya, Turkey
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Huang Y, Zhang Z, Liu S, Li X, Yang Y, Ma J, Li Z, Zhou J, Jiang Y, He B. CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia. BMC Med Imaging 2021; 21:31. [PMID: 33596844 PMCID: PMC7887546 DOI: 10.1186/s12880-021-00564-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 12/28/2020] [Indexed: 01/08/2023] Open
Abstract
Background In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. Methods A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. Results The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). Conclusions CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00564-w.
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Affiliation(s)
- Yilong Huang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Zhenguang Zhang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Siyun Liu
- Precision Health Institution, PDx, GE Healthcare (China), Beijing, 100176, China
| | - Xiang Li
- Department of Radiology, The 3rd Peoples' Hospital of Kunming, Kunming, 650000, China
| | - Yunhui Yang
- Department of Medical Imaging, People's Hospital of Xishuangbanna Dai Autonomous Prefecture, Xishuangbanna, 666100, China
| | - Jiyao Ma
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Zhipeng Li
- Medical Imaging Department, Yunnan Provincial Infectious Disease Hospital, Kunming, 650000, China
| | - Jialong Zhou
- MRI Department, The First People's Hospital of Yunnan Province, Kunming, 650000, China
| | - Yuanming Jiang
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Bo He
- Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
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20
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Xie C, Ng MY, Ding J, Leung ST, Lo CSY, Wong HYF, Vardhanabhuti V. Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis. Eur J Radiol Open 2020; 7:100271. [PMID: 32959017 PMCID: PMC7494331 DOI: 10.1016/j.ejro.2020.100271] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 08/16/2020] [Accepted: 08/24/2020] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs. METHODS We retrospectively included COVID-19 patients between 24/01/2020 and 31/03/2020 as case group and patients with pulmonary GGOs between 04/02/2012 and 31/03/2020 as a control group. Radiomics features were extracted from contoured GGOs by PyRadiomics. The least absolute shrinkage and selection operator method was used to establish the radiomics model. We assessed the performance using the area under the curve of the receiver operating characteristic curve (AUC). RESULTS A total of 301 patients (age mean ± SD: 64 ± 15 years; male: 52.8 %) from three hospitals were enrolled, including 33 COVID-19 patients in the case group and 268 patients with malignancies or pneumonia in the control group. Thirteen radiomics features out of 474 were selected to build the model. This model achieved an AUC of 0.905, accuracy of 89.5 %, sensitivity of 83.3 %, specificity of 90.0 % in the testing set. CONCLUSION We developed a noninvasive radiomics model based on CT imaging for the diagnosis of COVID-19 based on GGO lesions, which could be a promising supplementary tool for improving specificity for COVID-19 in a population confounded by ground glass opacity changes from other etiologies.
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Affiliation(s)
- Chenyi Xie
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Ming-Yen Ng
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
- Department of Medical Imaging, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Jie Ding
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Siu Ting Leung
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | | | | | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
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21
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Zhang R, Tian P, Chen B, Zhou Y, Li W. Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes. Cancer Manag Res 2020; 12:8057-8066. [PMID: 32943938 PMCID: PMC7481308 DOI: 10.2147/cmar.s256719] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/13/2020] [Indexed: 02/05/2023] Open
Abstract
Objective Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size. Materials and Methods This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models. Results The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size. Conclusion We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Panwen Tian
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.,Department of Lung Cancer Treatment Center, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yongzhao Zhou
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
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22
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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