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Jiang X, Luo C, Peng X, Zhang J, Yang L, Liu LZ, Cui YF, Liu MW, Miao L, Jiang JM, Ren JL, Yang XT, Li M, Zhang L. Incidence rate of occult lymph node metastasis in clinical T 1-2N 0M 0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research. Respir Res 2024; 25:226. [PMID: 38811960 PMCID: PMC11138070 DOI: 10.1186/s12931-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
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Affiliation(s)
- Xu Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Chao Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xin Peng
- Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, 610031, China
- Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Jing Zhang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Lin Yang
- Department of Pathology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Yan-Fen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Meng-Wen Liu
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lei Miao
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiu-Ming Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, 100176, China
| | - Xiao-Tang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China.
| | - Meng Li
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Li Zhang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Chung HS, Yoon HI, Hwangbo B, Park EY, Choi CM, Park YS, Lee K, Ji W, Park S, Lee GK, Kim TS, Kim HY, Kim MS, Lee JM. Prediction Models for Mediastinal Metastasis and Its Detection by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Potentially Operable Non-Small Cell Lung Cancer: A Prospective Study. Chest 2023; 164:770-784. [PMID: 37019355 DOI: 10.1016/j.chest.2023.03.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Prediction models for mediastinal metastasis and its detection by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) have not been developed using a prospective cohort of potentially operable patients with non-small cell lung cancer (NSCLC). RESEARCH QUESTION Can mediastinal metastasis and its detection by EBUS-TBNA be predicted with prediction models in NSCLC? STUDY DESIGN AND METHODS For the prospective development cohort, 589 potentially operable patients with NSCLC were evaluated (July 2016-June 2019) from five Korean teaching hospitals. Mediastinal staging was performed using EBUS-TBNA (with or without the transesophageal approach). Surgery was performed for patients without clinical N (cN) 2-3 disease by endoscopic staging. The prediction model for lung cancer staging-mediastinal metastasis (PLUS-M) and a model for mediastinal metastasis detection by EBUS-TBNA (PLUS-E) were developed using multivariable logistic regression analyses. Validation was performed using a retrospective cohort (n = 309) from a different period (June 2019-August 2021). RESULTS The prevalence of mediastinal metastasis diagnosed by EBUS-TBNA or surgery and the sensitivity of EBUS-TBNA in the development cohort were 35.3% and 87.0%, respectively. In PLUS-M, younger age (< 60 years and 60-70 years compared with ≥ 70 years), nonsquamous histology (adenocarcinoma and others), central tumor location, tumor size (> 3-5 cm), cN1 or cN2-3 stage by CT, and cN1 or cN2-3 stage by PET-CT were significant risk factors for N2-3 disease. Areas under the receiver operating characteristic curve (AUCs) for PLUS-M and PLUS-E were 0.876 (95% CI, 0.845-0.906) and 0.889 (95% CI, 0.859-0.918), respectively. Model fit was good (PLUS-M: Hosmer-Lemeshow P = .658, Brier score = 0.129; PLUS-E: Hosmer-Lemeshow P = .569, Brier score = 0.118). In the validation cohort, PLUS-M (AUC, 0.859 [95% CI, 0.817-0.902], Hosmer-Lemeshow P = .609, Brier score = 0.144) and PLUS-E (AUC, 0.900 [95% CI, 0.865-0.936], Hosmer-Lemeshow P = .361, Brier score = 0.112) showed good discrimination ability and calibration. INTERPRETATION PLUS-M and PLUS-E can be used effectively for decision-making for invasive mediastinal staging in NSCLC. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT02991924; URL: www. CLINICALTRIALS gov.
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Affiliation(s)
- Hyun Sung Chung
- Division of Pulmonology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Ho Il Yoon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Korea
| | - Bin Hwangbo
- Division of Pulmonology, National Cancer Center, Goyang, Gyeonggi, Korea.
| | - Eun Young Park
- Biostatistics Collaboration Team, Research Core Center, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Chang-Min Choi
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Sik Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Wonjun Ji
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sohee Park
- Department of Health Informatics and Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Geon Kook Lee
- Department of Pathology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Tae Sung Kim
- Department of Nuclear Medicine, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Hyae Young Kim
- Department of Radiology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Moon Soo Kim
- Department of Thoracic Surgery, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Jong Mog Lee
- Department of Thoracic Surgery, National Cancer Center, Goyang, Gyeonggi, Korea
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Sherminie LPG, Jayatilake ML, Hewavithana B, Weerakoon BS, Vijithananda SM. Morphometry-based radiomics for predicting therapeutic response in patients with gliomas following radiotherapy. Front Oncol 2023; 13:1139902. [PMID: 37664038 PMCID: PMC10470056 DOI: 10.3389/fonc.2023.1139902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy. Methods 105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated. Results Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features. Discussion Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy.
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Affiliation(s)
- Lahanda Purage G. Sherminie
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L. Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Badra Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Bimali S. Weerakoon
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Sahan M. Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
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Tang W, Wen C, Pei Y, Wu Z, Zhong J, Peng J, Zhong J. Preoperative CT findings and prognosis of pulmonary sarcomatoid carcinoma: comparison with conventional NSCLC of similar tumor size. BMC Med Imaging 2023; 23:105. [PMID: 37580691 PMCID: PMC10424330 DOI: 10.1186/s12880-023-01065-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/26/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small cell lung cancer (NSCLC) but differs in terms of treatment strategies compared with conventional-NSCLC (c-NSCLC). However, preoperative CT differentiation between PSC and c-NSCLC remains a challenge. This study aimed to explore the CT findings and prognosis of PSC compared with c-NSCLC of similar tumor size. METHODS Clinical data and CT findings of 31 patients with PSC and 87 patients with c-NSCLC were retrospectively analyzed. Clinical data included sex, age, and smoking history. CT findings included tumor size, tumor location, calcification, vacuole/cavity, pleural invasion, mean CT value, and low-attenuation area (LAA) ratio. Kaplan‒Meier curves and log-rank tests were used for survival analysis. A Cox regression model was constructed to evaluate prognostic risk factors associated with overall survival (OS). The Spearman correlation among clinicoradiological outcomes were analyzed. RESULTS The mean tumor size of PSC and c-NSCLC were both 5.1 cm. The median survival times of PSC and c-NSCLC were 8 months and 34 months, respectively (P < 0.001). Calcification and vacuoles/cavities were rarely present in PSC. Pleural invasion occurred in both PSC and c-NSCLC (P = 0.285). The mean CT values of PSC and c-NSCLC on plain scan (PS), arterial phase (AP), and venous phase (VP) were 30.48 ± 1.59 vs. 36.25 ± 0.64 Hu (P = 0.002), 43.26 ± 2.96 vs. 58.71 ± 1.65 Hu (P < 0.001) and 50.26 ± 3.28 vs. 64.24 ± 1.86 Hu (P < 0.001), the AUCs were 0.685, 0.757 and 0.710, respectively. Compared to c-NSCLC, PSC had a larger LAA ratio, and the AUC was 0.802, with an optimal cutoff value of 20.6%, and the sensitivity and specificity were 0.645 and 0.862, respectively. Combined with the mean CT value and LAA ratio, AP + VP + LAA yielded the largest AUC of 0.826. The LAA ratio were not independent risk factors for PSC in this study. LAA ratio was negatively correlated with PS (r = -0.29), AP (r = -0.58), and VP (r = -0.66). LAA showed a weak positive correlation with tumor size(r = 0.27). CONCLUSIONS PSC has a poorer prognosis than c-NSCLC of similar tumor size. The mean CT value and LAA ratio contributes to preoperative CT differentiation of PSC and c-NSCLC.
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Affiliation(s)
- Wenjian Tang
- Department of Medical Imaging, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, 16th Meiguan Avenue, Ganzhou, 341000, P.R. China
| | - Chunju Wen
- Department of Medical Hematology, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China
| | - Yixiu Pei
- Department of Medical Imaging, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, 16th Meiguan Avenue, Ganzhou, 341000, P.R. China
| | - Zhen Wu
- Department of Pathology, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China
| | - Junyuan Zhong
- Department of Medical Imaging, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, 16th Meiguan Avenue, Ganzhou, 341000, P.R. China
| | - Jidong Peng
- Department of Medical Imaging, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, 16th Meiguan Avenue, Ganzhou, 341000, P.R. China
| | - Jianping Zhong
- Department of Medical Imaging, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, 16th Meiguan Avenue, Ganzhou, 341000, P.R. China.
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Qin K, Fu X. [Research Progress in Imaging-based Diagnosis of Benign and Malignant
Enlarged Lymph Nodes in Non-small Cell Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2023; 26:31-37. [PMID: 36792078 PMCID: PMC9987091 DOI: 10.3779/j.issn.1009-3419.2023.101.01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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Affiliation(s)
- Kai Qin
- Department of Radiotherapy, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiaolong Fu
- Department of Radiotherapy, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China
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Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network. Int J Radiat Oncol Biol Phys 2023:S0360-3016(23)00002-0. [PMID: 36641040 DOI: 10.1016/j.ijrobp.2022.12.050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 12/26/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
PURPOSE This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. METHODS AND MATERIALS Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. RESULTS Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853). CONCLUSIONS Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Dai M, Wang N, Zhao X, Zhang J, Zhang Z, Zhang J, Wang J, Hu Y, Liu Y, Zhao X, Chen X. Value of Presurgical 18F-FDG PET/CT Radiomics for Predicting Mediastinal Lymph Node Metastasis in Patients with Lung Adenocarcinoma. Cancer Biother Radiopharm 2022. [PMID: 36342812 DOI: 10.1089/cbr.2022.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Objective: The aim of this study was to develop an F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic model for predicting mediastinal lymph node metastasis (LNM) in presurgical patients with lung adenocarcinoma. Methods: The study enrolled 320 patients with lung adenocarcinoma (288 internal and 32 external cases) and extracted 190 radiomic features using the LIFEx package. Optimal radiomic features to build a radiomic model were selected using the least absolute shrinkage and selection operator algorithm. Logistic regression was used to build the clinical and complex (combined radiomic and clinical variables) models. Results: Ten radiomic features were selected. In the training group, the area under the receiver operating characteristic curve of the complex model was significantly higher than that of the radiomic and clinical models [0.924 (95% CI: 0.887-0.961) vs. 0.863 (95% CI: 0.814-0.912; p = 0.001) and 0.838 (95% CI: 0.783-0.894; p = 0.000), respectively]. The sensitivity, specificity, accuracy, and positive and negative predictive values of the radiomic model were 0.857, 0.790, 0.811, and 0.651 and 0.924, respectively, which were better than that of visual evaluation (0.539, 0.724, 0.667, and 0.472 and 0.775, respectively) and PET semiquantitative analyses (0.619, 0.732, 0.697, and 0.513 and 0.808, respectively). Conclusions: 18F-FDG PET/CT radiomics showed good predictive performance for LNM and improved the N-stage accuracy of lung adenocarcinoma.
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Affiliation(s)
- Meng Dai
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Na Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Xinming Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China
| | - Jianyuan Zhang
- Department of Nuclear Medicine, Baoding No. 1 Central Hospital, Baoding, China
| | - Zhaoqi Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jingmian Zhang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jianfang Wang
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yujing Hu
- Department of Nuclear Medicine, Hebei General Hospital, Shijiazhuang, China
| | - Yunuan Liu
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiujuan Zhao
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaolin Chen
- Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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Smyczynska U, Grabia S, Nowicka Z, Papis-Ubych A, Bibik R, Latusek T, Rutkowski T, Fijuth J, Fendler W, Tomasik B. Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models. Cancers (Basel) 2021; 13:cancers13215584. [PMID: 34771747 PMCID: PMC8582656 DOI: 10.3390/cancers13215584] [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] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient’s condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy. Abstract State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.
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Affiliation(s)
- Urszula Smyczynska
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Szymon Grabia
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Zuzanna Nowicka
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
| | - Anna Papis-Ubych
- Department of Radiotherapy, N. Copernicus Memorial Regional Specialist Hospital, 93-513 Lodz, Poland; (A.P.-U.); (J.F.)
| | - Robert Bibik
- Department of Radiation Oncology, Oncology Center of Radom, 26-600 Radom, Poland;
| | - Tomasz Latusek
- Radiotherapy Department, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO)—Branch in Gliwice, 44-101 Gliwice, Poland;
| | - Tomasz Rutkowski
- I Radiation and Clinical Oncology Department, Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO)—Branch in Gliwice, 44-101 Gliwice, Poland;
| | - Jacek Fijuth
- Department of Radiotherapy, N. Copernicus Memorial Regional Specialist Hospital, 93-513 Lodz, Poland; (A.P.-U.); (J.F.)
- Department of Radiotherapy, Chair of Oncology, Medical University of Lodz, 93-509 Lodz, Poland
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Correspondence:
| | - Bartlomiej Tomasik
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; (U.S.); (S.G.); (Z.N.); (B.T.)
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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Liu Y, Fan H, Dong D, Liu P, He B, Meng L, Chen J, Chen C, Lang J, Tian J. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer. Transl Oncol 2021; 14:101113. [PMID: 33975178 PMCID: PMC8131712 DOI: 10.1016/j.tranon.2021.101113] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/14/2022] Open
Abstract
The metastatic status of lymph nodes in cervical cancer patients can be predicted. Computed tomography-based radiomic model can identify the status of the normal-sized lymph node singly. The model may help doctors to make staging and clinical decision, and realize individualized treatment.
Purpose Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. Methods A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). Results Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. Conclusion We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.
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Affiliation(s)
- Yujia Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Huijian Fan
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Di Dong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai 519000, China.
| | - Ping Liu
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Bingxi He
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Lingwei Meng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Jiaming Chen
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Chunlin Chen
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
| | - Jinghe Lang
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan Wangfujing Dongcheng District, Beijing 100730, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai 519000, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China.
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11
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Trebeschi S, Bodalal Z, Boellaard TN, Tareco Bucho TM, Drago SG, Kurilova I, Calin-Vainak AM, Delli Pizzi A, Muller M, Hummelink K, Hartemink KJ, Nguyen-Kim TDL, Smit EF, Aerts HJWL, Beets-Tan RGH. Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy. Front Oncol 2021; 11:609054. [PMID: 33738253 PMCID: PMC7962549 DOI: 10.3389/fonc.2021.609054] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/04/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Checkpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria. METHODS A cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival. RESULTS Our results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations. CONCLUSIONS Our results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.
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Affiliation(s)
- Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Zuhir Bodalal
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Thierry N. Boellaard
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
| | - Teresa M. Tareco Bucho
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Silvia G. Drago
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
| | - Ieva Kurilova
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Adriana M. Calin-Vainak
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- Affidea, Cluj-Napoca, Romania
| | - Andrea Delli Pizzi
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D’Annunzio University of Chieti, Chieti, Italy
| | - Mirte Muller
- Department of Thoracic Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Karlijn Hummelink
- Department of Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Koen J. Hartemink
- Department of Surgery, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Thi Dan Linh Nguyen-Kim
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, Switzerland
| | | | - Hugo J. W. L. Aerts
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Radiology and Nuclear Medicine, University of Maastricht, Maastricht, Netherlandsa
- CARIM School for Cardiovascular Diseases, University of Maastricht, Maastricht, Netherlands
| | - Regina G. H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute - Antoni vanLeeuwenhoek Hospital, Amsterdam, Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht, Netherlands
- Department of Radiology, University of Southern Denmark, Odense, Denmark
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12
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Dong M, Hou G, Li S, Li N, Zhang L, Xu K. Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging. Front Oncol 2021; 10:558428. [PMID: 33489871 PMCID: PMC7821835 DOI: 10.3389/fonc.2020.558428] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 11/18/2020] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To establish and validate a radiomics model to estimate the malignancy of mediastinal lymph nodes (LNs) based on contrast-enhanced CT imaging. METHOD In total, 201 pathologically confirmed mediastinal LNs from 129 patients were enrolled and assigned to training and test sets. Radiomics features were extracted from the region of interest (ROI) delineated on venous-phase CT imaging of LN. Feature selection was performed with least absolute shrinkage and selection operator (LASSO) binary logistic regression. Multivariate logistic regression was performed with the backward stepwise elimination. A model was fitted to associate mediastinal LN malignancy with selected features. The performance of the model was assessed and compared to that of five other machine learning algorithms (support vector machine, naive Bayes, random forest, decision tree, K-nearest neighbor) using receiver operating characteristic (ROC) curves. Calibration curves and Hosmer-Lemeshow tests were used to assess the calibration degree. Decision curve analysis (DCA) was used to assess the clinical usefulness of the logistic regression model in both the training and test sets. Stratified analysis was performed for different scanners and slice thicknesses. RESULT Among the six machine learning methods, the logistic regression model with the eight strongest features showed a significant association with mediastinal LN status and the satisfactory diagnostic performance for distinguishing malignant LNs from benign LNs. The accuracy, sensitivity, specificity and area under the ROC curve (AUC) were 0.850/0.803, 0.821/0.806, 0.893/0.800, and 0.922/0.850 in the training/test sets, respectively. The Hosmer-Lemeshow test showed that the P value was > 0.05, indicating good calibration, and the calibration curves showed good agreement between the classifications and actual observations. DCA showed that the model would obtain more benefit when the threshold probability was between 30% and 90% in the test set. Stratified analysis showed that the performance was not affected by different scanners or slice thicknesses. There was no significant difference (DeLong test, P > 0.05) between any two subgroups, which showed the generalization of the radiomics score across different factors. CONCLUSION The model we built could help assist the preoperative estimation of mediastinal LN malignancy based on contrast-enhanced CT imaging, with stability for different scanners and slice thicknesses.
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Affiliation(s)
- Mengshi Dong
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Gang Hou
- Institute of Respiratory Disease, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Shu Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Nan Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
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