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Hu Y, Geng Y, Wang H, Chen H, Wang Z, Fu L, Huang B, Jiang W. Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis. J Comput Assist Tomogr 2024; 48:780-788. [PMID: 38498926 DOI: 10.1097/rct.0000000000001591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVES This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status. METHODS A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM. RESULTS Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts. CONCLUSIONS This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.
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Affiliation(s)
- Yue Hu
- From the Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - Yikang Geng
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Liaoning
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang
| | - Zekun Wang
- Department of Medical Iconography, Liaoning Cancer Hospital & Institute, Liaoning
| | - Langyuan Fu
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Bo Huang
- Department of Pathology, Liaoning Cancer Hospital and Institute, Liaoning
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning, People's Republic of China
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Zheng Y, Zhou L, Huang W, Han N, Zhang J. Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy. Cancer Imaging 2024; 24:71. [PMID: 38863062 PMCID: PMC11167789 DOI: 10.1186/s40644-024-00713-8] [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: 01/02/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC. METHODS Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model. RESULTS ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively. CONCLUSIONS Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.
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Affiliation(s)
- Yu Zheng
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Liang Zhou
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Wenjing Huang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Na Han
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730030, China.
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, 730030, China.
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Hou S, Wang H, Wang X, Chen H, Zhou B, Meng R, Sha X, Chang S, Wang H, Jiang W. Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study. Med Phys 2024; 51:1083-1091. [PMID: 37408393 DOI: 10.1002/mp.16581] [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: 01/18/2023] [Revised: 04/19/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making. PURPOSE To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM. METHODS This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort. CONCLUSION Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.
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Affiliation(s)
- Shaoping Hou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, P.R. China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital, Shenyang, Liaoning, P.R. China
| | - Boyu Zhou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Ruiqing Meng
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, P.R. China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P.R. China
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Zheng Y, Huang WJ, Han N, Jiang YL, Ma LY, Zhang J. MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status. Clin Radiol 2023; 78:e243-e250. [PMID: 36577557 DOI: 10.1016/j.crad.2022.11.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/08/2022] [Accepted: 11/18/2022] [Indexed: 12/27/2022]
Abstract
AIM To explore the utility of magnetic resonance imaging (MRI) characteristics and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer (NSCLC) in the differentiation of epidermal growth factor receptor (EGFR) mutation status. MATERIALS AND METHODS Forty-eight patients with brain metastases from NSCLC were enrolled in this retrospective study. Patients were subtyped into EGFR mutation (23 cases) and wild-type (25 cases) groups. Whole-lesion histogram metrics were derived from the apparent diffusion coefficient (ADC) maps, and imaging features were evaluated according to conventional MRI. Student's t-test or Mann-Whitney U-test, chi-squared test, and receiver operating characteristic (ROC) curve analysis were performed to discriminate the two groups and to determine the diagnostic efficacy of ADC histogram parameters. RESULTS EGFR mutation group had more multiple brain metastases, less peritumoural brain oedema (PTBO), and lower peritumoural brain oedema index (PTBO-I) than EGFR wild-type group (all p<0.05). In addition, 90th and 75th percentiles of ADC and maximum ADC in the EGFR mutation group were significantly higher than in the EGFR wild-type group (all p<0.05). Ninetieth percentile of ADC had the highest area under the curve (AUC; 0.711), and it was found to outperform 75th percentile of ADC (AUC, 0.662; p=0.039) and maximum ADC (AUC, 0.681). CONCLUSIONS Whole-lesion ADC histogram analysis and MRI features of brain metastasis from NSCLC are expected to be potential biomarkers to non-invasively differentiate the EGFR mutation status.
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Affiliation(s)
- Y Zheng
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - W-J Huang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - N Han
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Y-L Jiang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - L-Y Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - J Zhang
- Second Clinical School, Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China.
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Deep learning for preoperative prediction of the EGFR mutation and subtypes based on the MRI image of spinal metastasis from primary NSCLC. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Shi J, Zhao Z, Jiang T, Ai H, Liu J, Chen X, Luo Y, Fan H, Jiang X. A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor. Front Neuroinform 2022; 16:973698. [PMID: 35991287 PMCID: PMC9382021 DOI: 10.3389/fninf.2022.973698] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis.MethodsWe retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2).ResultsThe brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set.ConclusionOur proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.
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Affiliation(s)
- Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Zilong Zhao
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Tao Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Hua Ai
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Jiani Liu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xinpu Chen
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Huijie Fan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- *Correspondence: Huijie Fan,
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
- Xiran Jiang,
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Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC. Acad Radiol 2022; 30:1039-1046. [PMID: 35907759 DOI: 10.1016/j.acra.2022.06.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 12/09/2022]
Abstract
RATIONALE AND OBJECTIVES The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis. MATERIALS AND METHODS A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs. RESULTS A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set. CONCLUSION The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.
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Fan Y, Dong Y, Wang H, Wang H, Sun X, Wang X, Zhao P, Luo Y, Jiang X. Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma. Eur Radiol 2022; 32:6739-6751. [PMID: 35729427 DOI: 10.1007/s00330-022-08955-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/20/2022] [Accepted: 06/08/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVES This study aims to explore values of multi-parametric MRI-based radiomics for detecting the epidermal growth factor receptor (EGFR) mutation and resistance (T790M) mutation in lung adenocarcinoma (LA) patients with spinal metastasis. METHODS This study enrolled a group of 160 LA patients from our hospital (between Jan. 2017 and Feb. 2021) to build a primary cohort. An external cohort was developed with 32 patients from another hospital (between Jan. 2017 and Jan. 2021). All patients underwent spinal MRI (including T1-weighted (T1W) and T2-weighted fat-suppressed (T2FS)) scans. Radiomics features were extracted from the metastasis for each patient and selected to develop radiomics signatures (RSs) for detecting the EGFR and T790M mutations. The clinical-radiomics nomogram models were constructed with RSs and important clinical parameters. The receiver operating characteristics (ROC) curve was used to evaluate the predication capabilities of each model. Calibration and decision curve analyses (DCA) were constructed to verify the performance of the models. RESULTS For detecting the EGFR and T790M mutation, the developed RSs comprised 9 and 4 most important features, respectively. The constructed nomogram models incorporating RSs and smoking status showed favorite prediction efficacy, with AUCs of 0.849 (Sen = 0.685, Spe = 0.885), 0.828 (Sen = 0.964, Spe = 0.692), and 0.778 (Sen = 0.611, Spe = 0.929) in the training, internal validation, and external validation sets for detecting the EGFR mutation, respectively, and with AUCs of 0.0.842 (Sen = 0.750, Spe = 0.867), 0.823 (Sen = 0.667, Spe = 0.938), and 0.800 (Sen = 0.875, Spe = 0.800) in the training, internal validation, and external validation sets for detecting the T790M mutation, respectively. CONCLUSIONS Radiomics features from the spinal metastasis were predictive on both EGFR and T790M mutations. The constructed nomogram models can be potentially considered as new markers to guild treatment management in LA patients with spinal metastasis. KEY POINTS • To our knowledge, this study was the first approach to detect the EGFR T790M mutation based on spinal metastasis in patients with lung adenocarcinoma. • We identified 13 MRI features that were strongly associated with the EGFR T790M mutation. • The proposed nomogram models can be considered as potential new markers for detecting EGFR and T790M mutations based on spinal metastasis.
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Affiliation(s)
- Ying Fan
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Xinyan Sun
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Peng Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, People's Republic of China.
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Cao R, Pang Z, Wang X, Du Z, Chen H, Liu J, Yue Z, Wang H, Luo Y, Jiang X. Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/19/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA). Approach. We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models. Main results. RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort. Significance. The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.
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Meng N, Fu F, Feng P, Li Z, Gao H, Wu Y, Zhang J, Wei W, Yuan J, Yang Y, Liu H, Cheng J, Wang M. Evaluation of Amide Proton Transfer‐Weighted Imaging for Lung Cancer Subtype and Epidermal Growth Factor Receptor: A Comparative Study With Diffusion and Metabolic Parameters. J Magn Reson Imaging 2022; 56:1118-1129. [PMID: 35258145 DOI: 10.1002/jmri.28135] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 11/05/2022] Open
Affiliation(s)
- Nan Meng
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
- Academy of Medical Sciences Zhengzhou University Zhengzhou China
| | - Fangfang Fu
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Pengyang Feng
- Department of Medical Imaging Henan University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Ziqiang Li
- Department of Medical Imaging Xinxiang Medical University, Henan Provincial People's Hospital Zhengzhou China
| | - Haiyan Gao
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Yaping Wu
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Jiawen Zhang
- Department of Pediatrics, Zhengzhou Central Hospital Zhengzhou University Zhengzhou China
| | - Wei Wei
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Jianmin Yuan
- Central Research Institute UIH Group Shanghai China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging UIH Group Beijing China
| | - Hui Liu
- UIH America, Inc Houston Texas USA
| | - Jianjian Cheng
- Department of Respiratory and Critical Care Medicine Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
| | - Meiyun Wang
- Department of Medical Imaging Zhengzhou University People's Hospital & Henan Provincial People's Hospital Zhengzhou China
- Academy of Medical Sciences Zhengzhou University Zhengzhou China
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Fan Y, Dong Y, Yang H, Chen H, Yu Y, Wang X, Wang X, Yu T, Luo Y, Jiang X. Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer. Phys Med Biol 2021; 66. [PMID: 34633298 DOI: 10.1088/1361-6560/ac2ea7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/11/2021] [Indexed: 01/20/2023]
Abstract
The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.
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Affiliation(s)
- Ying Fan
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Huazhe Yang
- Department of Biophysics, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Yalian Yu
- Department of Otorhinolaryngology, the First Affiliated Hospital of China Medical University, Shenyang, 110122, People's Republic of China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xinling Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
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12
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Yang F, Wan Y, Xu L, Wu Y, Shen X, Wang J, Lu D, Shao C, Zheng S, Niu T, Xu X. MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2021; 11:672126. [PMID: 34476208 PMCID: PMC8406635 DOI: 10.3389/fonc.2021.672126] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has poor prognosis. Cytokeratin (CK)19-positive (CK19+) HCC is especially aggressive; early identification of this subtype and timely intervention can potentially improve clinical outcomes. In the present study, we developed a preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI)-based radiomics model for noninvasive and accurate classification of CK19+ HCC. A multicenter and time-independent cohort of 257 patients were retrospectively enrolled (training cohort, n = 143; validation cohort A, n = 75; validation cohort B, n = 39). A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. Multiple logistic regression, support vector machine, random forest, and artificial neural network (ANN) algorithms were used to construct the radiomics model, and the area under the receiver operating characteristic (AUROC) curve was used to evaluate the diagnostic performance of corresponding classifiers. The incidence of CK19+ HCC was significantly higher in male patients. The ANN-derived combined classifier comprising 12 optimal radiomics features showed the best diagnostic performance, with AUROCs of 0.857, 0.726, and 0.790 in the training cohort and validation cohorts A and B, respectively. The combined model based on multisequence MRI radiomics features can be used for preoperative noninvasive and accurate classification of CK19+ HCC, so that personalized management strategies can be developed.
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Affiliation(s)
- Fan Yang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lei Xu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yichao Wu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianguo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Di Lu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chuxiao Shao
- Department of General Surgery, Lishui Central Hospital, Lishui, China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, Shulan Health Hangzhou Hospital, Hangzhou, China
| | - Tianye Niu
- Nucelar & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang University Cancer Center, Hangzhou, China.,NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China.,Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
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13
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Ren M, Yang H, Lai Q, Shi D, Liu G, Shuang X, Su J, Xie L, Dong Y, Jiang X. MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients. Med Phys 2021; 48:5142-5151. [PMID: 34318502 DOI: 10.1002/mp.15137] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/08/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma. METHODS A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance. RESULTS The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram. CONCLUSION Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.
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Affiliation(s)
- Meihong Ren
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China
| | - Huazhe Yang
- Department of Biophysics, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China
| | - Qingyuan Lai
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Dabao Shi
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Guanyu Liu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Xue Shuang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China
| | - Juan Su
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China
| | - Liping Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, P.R. China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, P.R. China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, P.R. China
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Fung DLX, Liu Q, Zammit J, Leung CKS, Hu P. Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. J Transl Med 2021; 19:318. [PMID: 34311742 PMCID: PMC8312213 DOI: 10.1186/s12967-021-02992-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/17/2021] [Indexed: 12/28/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases). Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02992-2.
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Affiliation(s)
- Daryl L X Fung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Qian Liu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada.,Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J9, Canada
| | - Judah Zammit
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Carson Kai-Sang Leung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Pingzhao Hu
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. .,Department of Biochemistry and Medical Genetics, University of Manitoba, 745 Bannatyne Avenue, Winnipeg, MB, R3E 0J9, Canada. .,CancerCare Manitoba Research Institute, CancerCare Manitoba, Winnipeg, MB, R3E 0W3, Canada.
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15
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Wang Y, Wan Q, Xia X, Hu J, Liao Y, Wang P, Peng Y, Liu H, Li X. Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. J Thorac Dis 2021; 13:3497-3508. [PMID: 34277045 PMCID: PMC8264682 DOI: 10.21037/jtd-20-3358] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 04/02/2021] [Indexed: 11/25/2022]
Abstract
Background The epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma. Methods A total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction. Results Among the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUCtest =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively. Conclusions The radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.
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Affiliation(s)
- Yuze Wang
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qi Wan
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoying Xia
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jianfeng Hu
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | | | - Peng Wang
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Peng
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hongyan Liu
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
| | - Xinchun Li
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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16
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Jiang X, Ren M, Shuang X, Yang H, Shi D, Lai Q, Dong Y. Multiparametric MRI-Based Radiomics Approaches for Preoperative Prediction of EGFR Mutation Status in Spinal Bone Metastases in Patients with Lung Adenocarcinoma. J Magn Reson Imaging 2021; 54:497-507. [PMID: 33638577 DOI: 10.1002/jmri.27579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions. PURPOSE To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM. STUDY TYPE Retrospective. POPULATION A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set). FIELD STRENGTH/SEQUENCE T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T. ASSESSMENT Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features. STATISTICAL TESTS The Mann-Whitney U test and χ2 test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results. RESULTS The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models. DATA CONCLUSION Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Xiran Jiang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Meihong Ren
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Xue Shuang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Huazhe Yang
- Department of Biophysics, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Dabao Shi
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Qingyuan Lai
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
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Wu W, Zhou S, Hippe DS, Liu H, Wang Y, Mayr NA, Yuh WT, Xia L, Bowen SR. Whole-Lesion DCE-MRI Intensity Histogram Analysis for Diagnosis in Patients with Suspected Lung Cancer. Acad Radiol 2021; 28:e27-e34. [PMID: 32102748 DOI: 10.1016/j.acra.2020.01.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/17/2020] [Accepted: 01/18/2020] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer. MATERIALS AND METHODS This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [Tmax] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs). RESULTS There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower Tmax (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either Tmax (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011). CONCLUSION This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.
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Ai Z, Han Q, Huang Z, Wu J, Xiang Z. The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1128. [PMID: 33240977 PMCID: PMC7576072 DOI: 10.21037/atm-20-5109] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background The present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions. Methods In this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves. Results There were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29. Conclusions A multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.
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Affiliation(s)
- Zhu Ai
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Zhiwei Huang
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Jiayan Wu
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China
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19
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Chen J, Wu Z, Xia C, Jiang H, Liu X, Duan T, Cao L, Ye Z, Zhang Z, Ma L, Song B, Shi Y. Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI. Eur Radiol 2020; 30:1232-1242. [PMID: 31529254 DOI: 10.1007/s00330-019-06414-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/26/2019] [Accepted: 08/07/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To explore the noninvasive prediction of hepatocellular carcinoma (HCC) with progenitor phenotype based on gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS This retrospective study included 115 surgery-proven HCCs with preoperative gadoxetic acid-enhanced MRI from August 2015 to September 2018. Image features were reviewed. Quantitative image analysis was performed using histogram analysis. HCC with progenitor phenotype was defined as positive for either cytokeratin 19 (CK19) or epithelial cell adhesion molecule (EpCAM) expression. Statistically significant variables for identifying HCCs with progenitor phenotype were determined at multivariate analyses. ROC analyses were used to determined cutoff values and the diagnostic performance of significant variables and combinations. Prediction nomogram was constructed based on multivariate analysis. RESULTS At multivariate regression analyses, AFP ≥ 155.25 ng/mL (p < 0.001), skewness on T2WI ≤ 1.10 (p = 0.024), uniformity on pre-T1WI ≤ 0.91 (p = 0.024), irregular tumor margin (p = 0.006), targetoid appearance (p = 0.001), and the absence of mosaic architecture (p = 0.014) were significant predictors of HCCs expressing progenitor cell markers. Combing any three of those significant variables, it provides a diagnostic accuracy of 0.86 (95% CI 0.78-0.92) with sensitivity of 0.97 (95% CI 0.86-1.00), and specificity of 0.74 (95% CI 0.63-0.83). The C-index of the regression coefficient-based nomogram was 0.94 (95% CI 0.91-0.98). CONCLUSIONS Noninvasive prediction of HCCs with progenitor phenotype can be achieved with high accuracy by integrated interpretation of biochemical and radiological information, representing a handy tool for precise patient management and the prediction of prognosis. KEY POINTS • Qualitative image features of irregular tumor margin, targetoid appearance, and the absence of mosaic architecture are significant predictors of hepatocellular carcinoma with progenitor phenotype. • Quantitative analyses using whole-lesion histogram analysis provides additional information for the prediction of hepatocellular carcinoma with progenitor phenotype. • Noninvasive prediction of hepatocellular carcinoma with progenitor phenotype can be achieved with high accuracy by integrated interpretation of clinical information and qualitative and quantitative imaging analyses.
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Affiliation(s)
- Jie Chen
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zhenru Wu
- Laboratory of Pathology, West China Hospital, Sichuan University, B2 Building, No. 88, South Ke Yuan Road, Chengdu, 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Hanyu Jiang
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Xijiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Likun Cao
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zheng Ye
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Zhen Zhang
- West China School of Medicine, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China
| | - Ling Ma
- Application Advanced Team, GE Healthcare, Shanghai, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Guoxue Xiang, No. 37, Chengdu, 610041, China.
| | - Yujun Shi
- Laboratory of Pathology, West China Hospital, Sichuan University, B2 Building, No. 88, South Ke Yuan Road, Chengdu, 610041, China.
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Cui Y, Cui X, Yang X, Zhuo Z, Du X, Xin L, Yang Z, Cheng X. Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings. J Magn Reson Imaging 2019; 50:930-939. [PMID: 30637861 DOI: 10.1002/jmri.26653] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 12/30/2018] [Accepted: 12/31/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies. PURPOSE To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma. STUDY TYPE Retrospective. SUBJECTS In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution. SEQUENCE DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm2 . ASSESSMENT D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status. STATISTICAL TESTS Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis. RESULTS All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively. DATA CONCLUSION DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xue'e Cui
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Zhizheng Zhuo
- MR Clinical Sciences, Philips Healthcare Greater China, Beijing, China
| | - Xiaosong Du
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Zhao Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xintao Cheng
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
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Abstract
Precision medicine is increasingly pushed forward, also with respect to upcoming new targeted therapies. Individual characterization of diseases on the basis of biomarkers is a prerequisite for this development. So far, biomarkers are characterized clinically, histologically or on a molecular level. The implementation of broad screening methods (“Omics”) and the analysis of big data – in addition to single markers – allow to define biomarker signatures. Next to “Genomics”, “Proteomics”, and “Metabolicis”, “Radiomics” gained increasing interest during the last years. Based on radiologic imaging, multiple radiomic markers are extracted with the help of specific algorithms. These are correlated with clinical, (immuno-) histopathological, or genomic data. Underlying structural differences are based on the imaging metadata and are often not visible and therefore not detectable without specific software. Radiomics are depicted numerically or by graphs. The fact that radiomic information can be extracted from routinely performed imaging adds a specific appeal to this method. Radiomics could potentially replace biopsies and additional investigations. Alternatively, radiomics could complement other biomarkers and thus lead to a more precise, multimodal prediction. Until now, radiomics are primarily used to investigate solid tumors. Some promising studies in head and neck cancer have already been published.
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