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Wu WF, Lai KM, Chen CH, Wang BC, Chen YJ, Shen CW, Chen KY, Lin EC, Chen CC. Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset. Discov Oncol 2024; 15:447. [PMID: 39277568 PMCID: PMC11401825 DOI: 10.1007/s12672-024-01333-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 09/10/2024] [Indexed: 09/17/2024] Open
Abstract
BACKGROUND Early detection of T790M mutation in exon 20 of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) patients with brain metastasis is crucial for optimizing treatment strategies. In this study, we developed radiomics models to distinguish NSCLC patients with T790M-positive mutations from those with T790M-negative mutations using multisequence MR images of brain metastasis despite an imbalanced dataset. Various resampling techniques and classifiers were employed to identify the most effective strategy. METHODS Radiomic analyses were conducted on a dataset comprising 125 patients, consisting of 18 with EGFR T790M-positive mutations and 107 with T790M-negative mutations. Seventeen first- and second-order statistical features were selected from CET1WI, T2WI, T2FLAIR, and DWI images. Four classifiers (logistic regression, support vector machine, random forest [RF], and extreme gradient boosting [XGBoost]) were evaluated under 13 different resampling conditions. RESULTS The area under the curve (AUC) value achieved was 0.89, using the SVM-SMOTE oversampling method in combination with the XGBoost classifier. This performance was measured against the AUC reported in the literature, serving as an upper-bound reference. Additionally, comparable results were observed with other oversampling methods paired with RF or XGBoost classifiers. CONCLUSIONS Our study demonstrates that, even when dealing with an imbalanced EGFR T790M dataset, reasonable predictive outcomes can be achieved by employing an appropriate combination of resampling techniques and classifiers. This approach has significant potential for enhancing T790M mutation detection in NSCLC patients with brain metastasis.
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Affiliation(s)
- Wen-Feng Wu
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan
| | - Kuan-Ming Lai
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan
- Central Taiwan University of Science and Technology Institute of Radiological Science, Taichung, 406, Taiwan
| | - Chia-Hung Chen
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan
- Central Taiwan University of Science and Technology Institute of Radiological Science, Taichung, 406, Taiwan
| | - Bai-Chuan Wang
- Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan
| | - Yi-Jen Chen
- Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan
| | - Chia-Wei Shen
- Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan
| | - Kai-Yan Chen
- Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan
| | - Eugene C Lin
- Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan.
- Center for Nano Bio-Detection, National Chung Cheng University, Chiayi, 621, Taiwan.
| | - Chien-Chin Chen
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, No. 539, Zhongxiao Rd., East Dist., Chiayi City, 60002, Taiwan.
- Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, 402, Taiwan.
- Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, 701, Taiwan.
- Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan, 717, Taiwan.
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Zungsontiporn N, Ouwongprayoon P, Boonsirikamchai P, Leelayuwatanakul N, Vinayanuwattikun C, Moonai K, Khongkhaduead E, Thorner PS, Shuangshoti S, Teerapakpinyo C. Detection of EGFR T790M mutation using liquid biopsy for non-small cell lung cancer: Utility of droplet digital polymerase chain reaction vs. cobas real-time polymerase chain reaction. Pathol Res Pract 2024; 255:155213. [PMID: 38394807 DOI: 10.1016/j.prp.2024.155213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/03/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Digital platforms for mutation detection yield higher sensitivity than non-digital platforms but lack universal positive cut-off values that correlate with the outcome of osimertinib treatment. This study determined compared droplet digital polymerase chain reaction (ddPCR) to the standard cobas assay for epithelial growth factor receptor (EGFR) T790M mutation detection in patients with non-small cell lung cancer. METHODS Study patients had EGFR-mutant tumours with disease progression on first/second generation EGFR tyrosine kinase inhibitors, and osimertinib treatment after T790M mutation detection. T790M status was tested by cobas assay using liquid biopsy, and only by ddPCR if an EGFR mutation was identified but T790M was negative. Clinical efficacy of osimertinib was compared between patients with T790M detected by cobas vs. only by ddPCR. A positive cut-off value for ddPCR was determined by assessing efficacy with osimertinib. RESULTS 61 patients had tumors with an acquired T790M mutation, 38 detected by cobas and an additional 23 only by ddPCR. The median progression-free survival (PFS) for the cobas- and ddPCR-positive groups was 9.5 and 7.8 months, respectively (p=0.43). For ddPCR, a fractional abundance (FA) of 0.1% was used as a cut-off value. The median PFS of patients with FA ≥0.1% and <0.1% was 8.3 and 4.6 months, respectively (p=0.08). FA ≥0.1% was independently associated with a longer PFS. CONCLUSION Using ddPCR to follow up the cobas assay yielded more cases (38% of total) with a T790M mutation. A cut-off value of FA ≥0.1% identified patients who responded as well to osimertinib as those identified by cobas assay. This sequential approach should detect additional patients who might benefit from osimertinib treatment.
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Affiliation(s)
- Nicha Zungsontiporn
- Division of Medical Oncology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Pongsakorn Ouwongprayoon
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Piyaporn Boonsirikamchai
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Nophol Leelayuwatanakul
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University and The King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Chanida Vinayanuwattikun
- Division of Medical Oncology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Kantika Moonai
- Chula GenePRO Center, Research Affairs, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Ekkachai Khongkhaduead
- Chula GenePRO Center, Research Affairs, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Paul Scott Thorner
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Department of Pathology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Shanop Shuangshoti
- Chula GenePRO Center, Research Affairs, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand; Department of Pathology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Chinachote Teerapakpinyo
- Chula GenePRO Center, Research Affairs, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand.
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Lv X, Li Y, Wang B, Wang Y, Pan Y, Li C, Hou D. Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases. Quant Imaging Med Surg 2023; 13:8599-8610. [PMID: 38106277 PMCID: PMC10722019 DOI: 10.21037/qims-23-822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 09/15/2023] [Indexed: 12/19/2023]
Abstract
Background Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. Methods This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. Results There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. Conclusions The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.
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Affiliation(s)
- Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Bing Wang
- Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yichuan Wang
- Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yanxi Pan
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
- Department of Radiology, The Fourth People’s Hospital of Nanning, Nanning, China
| | - Chenghai Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
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Li Y, Lv X, Wang Y, Xu Z, Lv Y, Hou D. CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy. Eur Radiol Exp 2023; 7:64. [PMID: 37914925 PMCID: PMC10620367 DOI: 10.1186/s41747-023-00380-7] [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: 07/18/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy. METHODS A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis. RESULTS The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001). CONCLUSIONS The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability. RELEVANCE STATEMENT Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients. KEY POINTS • Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.
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Affiliation(s)
- Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Yichuan Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Zexuan Xu
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Yan Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
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Léonce C, Guerriau C, Chalabreysse L, Duruisseaux M, Couraud S, Brevet M, Bringuier PP, Poncet DA. Comparison and Validation of Rapid Molecular Testing Methods for Theranostic Epidermal Growth Factor Receptor Alterations in Lung Cancer: Idylla versus Digital Droplet PCR. Int J Mol Sci 2023; 24:15684. [PMID: 37958668 PMCID: PMC10648419 DOI: 10.3390/ijms242115684] [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: 10/06/2023] [Revised: 10/20/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Targeting EGFR alterations, particularly the L858R (Exon 21) mutation and Exon 19 deletion (del19), has significantly improved the survival of lung cancer patients. From now on, the issue is to shorten the time to treatment. Here, we challenge two well-known rapid strategies for EGFR testing: the cartridge-based platform Idylla™ (Biocartis) and a digital droplet PCR (ddPCR) approach (ID_Solution). To thoroughly investigate each testing performance, we selected a highly comprehensive cohort of 39 unique del19 (in comparison, the cbioportal contains 40 unique del19), and 9 samples bearing unique polymorphisms in exon 19. Additional L858R (N = 24), L861Q (N = 1), del19 (N = 63), and WT samples (N = 34) were used to determine clear technical and biological cutoffs. A total of 122 DNA samples extracted from formaldehyde-fixed samples was used as input. No false positive results were reported for either of the technologies, as long as careful droplet selection (ddPCR) was ensured for two polymorphisms. ddPCR demonstrated higher sensitivity in detecting unique del19 (92.3%, 36/39) compared to Idylla (67.7%, 21/31). However, considering the prevalence of del19 and L858R in the lung cancer population, the adjusted theranostic values were similar (96.51% and 95.26%, respectively). ddPCR performs better for small specimens and low tumoral content, but in other situations, Idylla is an alternative (especially if a molecular platform is absent).
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Affiliation(s)
- Camille Léonce
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
- Cancer Research Center of Lyon, UMR INSERM 1052 CNRS 5286, 69008 Lyon, France
| | - Clémence Guerriau
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- CNRS UMR 5261, INSERM U 1315, LabEx DEVweCAN, Institut NeuroMyoGène (INMG), Pathophysiology and Genetics of the Neuron and Muscle (PGNM) Laboratory, Team Chromatin Dynamics, Nuclear Domains, Virus, 69008 Lyon, France
| | - Lara Chalabreysse
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
| | - Michaël Duruisseaux
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
- Cancer Research Center of Lyon, UMR INSERM 1052 CNRS 5286, 69008 Lyon, France
- Respiratory Department and Early Phase, Louis Pradel Hospital, Hospices Civils de Lyon Cancer Institute, 69100 Lyon, France
| | - Sébastien Couraud
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
- Department of Pulmonology and Thoracic Oncology, Lyon Sud Hospital, 69495 Pierre Bénite, France
| | - Marie Brevet
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
| | - Pierre-Paul Bringuier
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
| | - Delphine Aude Poncet
- Department of Pathology, Tumor Molecular Biology Unit, Groupement Hospitalier Est, Hospices Civils de Lyon, 69394 Bron, France; (C.L.); (C.G.); (L.C.); (M.B.); (P.-P.B.)
- University of Lyon, Université Claude Bernard Lyon 1, 69100 Lyon, France; (M.D.); (S.C.)
- CNRS UMR 5261, INSERM U 1315, LabEx DEVweCAN, Institut NeuroMyoGène (INMG), Pathophysiology and Genetics of the Neuron and Muscle (PGNM) Laboratory, Team Chromatin Dynamics, Nuclear Domains, Virus, 69008 Lyon, France
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Li X, Chen J, Zhang C, Han Z, Zheng X, Cao D. Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma. BMC Pulm Med 2023; 23:339. [PMID: 37697337 PMCID: PMC10494384 DOI: 10.1186/s12890-023-02609-y] [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: 06/04/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND The purpose of this study was to develop a radiomic nomogram to predict T790M mutation of lung adenocarcinoma base on non-enhanced CT lung images. METHODS This retrospective study reviewed demographic data and lung CT images of 215 lung adenocarcinoma patients with T790M gene test results. 215 patients (including 52 positive) were divided into a training set (n = 150, 36 positive) and an independent test set (n = 65, 16 positive). Multivariate logistic regression was used to select demographic data and CT semantic features to build clinical model. We extracted quantitative features from the volume of interest (VOI) of the lesion, and developed the radiomic model with different feature selection algorithms and classifiers. The models were trained by a 5-fold cross validation strategy on the training set and assessed on the test set. ROC was used to estimate the performance of the clinical model, radiomic model, and merged nomogram. RESULTS Three demographic features (gender, smoking, emphysema) and ten radiomic features (Kruskal-Wallis as selection algorithm, LASSO Logistic Regression as classifier) were determined to build the models. The AUC of the clinical model, radiomic model, and nomogram in the test set were 0.742(95%CI, 0.619-0.843), 0.810(95%CI, 0.696-0.907), 0.841(95%CI, 0.743-0.938), respectively. The predictive efficacy of the nomogram was better than the clinical model (p = 0.042). The nomogram predicted T790M mutation with cutoff value was 0.69 and the score was above 130. CONCLUSION The nomogram developed in this study is a non-invasive, convenient, and economical method for predicting T790M mutation of lung adenocarcinoma, which has a good prospect for clinical application.
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Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Jianwei Chen
- Department of Radiology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, 350014, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai200062, China
| | - Zewen Han
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Xiuying Zheng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350005, China.
- Department of Radiology, Binhai Campus of the First Affiliated Hospital, National Regional Medical Center, Fujian Medical University, Fuzhou, Fujian, 350212, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, 350005, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, the First Affiliated Hospital, Fujian Medical University, Shanghai200062, China.
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Li Y, Lv X, Wang B, Xu Z, Wang Y, Sun M, Hou D. Predicting EGFR T790M Mutation in Brain Metastases Using Multisequence MRI-Based Radiomics Signature. Acad Radiol 2023; 30:1887-1895. [PMID: 36586758 DOI: 10.1016/j.acra.2022.12.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022]
Abstract
RATIONALE AND OBJECTIVES Timely identifying T790M mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) is essential to adjust targeted treatment strategies. To develop and validate radiomics models based on multisequence MRI for differentiating patients with T790M resistance from no T790M mutation in BM and explore the optimal sequence for prediction. MATERIALS AND METHODS This retrospective study enrolled 233 patients with proven of BM in NSCLC which included 95 with T790M and 138 without T790M from two hospitals as the training cohort and testing cohort separately. Radiomics features extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequence respectively. The most predictable features were selected based on the maximal information coefficient and Boruta method. Then four radiomics models were built to characterize T790M mutation by random forest classifier. ROC curves, F1 score and DCA curves were constructed to validate the capability and verify the performance of four models. RESULTS The DWI model showed best performance with AUC and F1 score of 0.886 and 0.789 in the training cohort, 0.850 and 0.743 in the testing cohort. DCA curves also showed higher overall net benefit from the DWI model than from the remaining three models in the testing cohort. Other three models also had some classification power whether in the training or testing cohort, especially T2-FLAIR model. CONCLUSION Multisequence MRI-based radiomics has potential to predict the emergence of EGFR T790M resistance mutations especially the radiomics signature based on DWI sequence.
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Affiliation(s)
- Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Bing Wang
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Zexuan Xu
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Yichuan Wang
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Mengyan Sun
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.)
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China (Y.L., X.L., Z.X., M.S.); Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China (B.W., Y,W.).
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Chiang CL, Ho HL, Yeh YC, Lee CC, Huang HC, Shen CI, Luo YH, Chen YM, Chiu CH, Chou TY. Efficacy of different platforms in detecting EGFR mutations using cerebrospinal fluid cell-free DNA from non-small-cell lung cancer patients with leptomeningeal metastases. Thorac Cancer 2023; 14:1251-1259. [PMID: 36977550 PMCID: PMC10175033 DOI: 10.1111/1759-7714.14866] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Cell-free tumor DNA (ctDNA) obtained through liquid biopsy is useful for the molecular analysis of advanced non-small-cell lung cancer (NSCLC). Few studies have directly compared analysis platforms in terms of their diagnostic performance in analyzing ctDNA obtained from the cerebrospinal fluid (CSF) of patients with leptomeningeal metastasis (LM). METHODS We prospectively analyzed patients with epidermal growth factor receptor (EGFR)-mutant NSCLC who were subjected to CSF analysis for suspected LM. To detect EGFR mutations, CSF ctDNA was analyzed using the cobas EGFR Mutation Test and droplet digital polymerase chain reaction (ddPCR). CSF samples from osimertinib-refractory patients with LM were also subjected to next-generation sequencing (NGS). RESULTS Significantly higher rates of valid results (95.1% vs. 78%, respectively, p = 0.04) and EGFR common mutation detection (94.3% vs. 77.1%, respectively, p = 0.047) were obtained through ddPCR than through the cobas EGFR Mutation Test. The sensitivities of ddPCR and cobas were 94.3% and 75.6%, respectively. The concordance rate for EGFR mutation detection through ddPCR and the cobas EGFR Mutation Test was 75.6% and that for EGFR mutation detection in CSF and plasma ctDNA was 28.1%. In osimertinib-resistant CSF samples, all original EGFR mutations were detected through NGS. MET amplification and CCDC6-RET fusion were demonstrated in one patient each (9.1%). CONCLUSIONS The cobas EGFR Mutation Test, ddPCR, and NGS appear to be feasible methods for analyzing CSF ctDNA in patients with NSCLC and LM. In addition, NGS may provide comprehensive information regarding the mechanisms underlying osimertinib resistance.
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Affiliation(s)
- Chi-Lu Chiang
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsiang-Ling Ho
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Chen Yeh
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsu-Ching Huang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-I Shen
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yung-Hung Luo
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chao-Hua Chiu
- Taipei Cancer Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Teh-Ying Chou
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Pathology, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
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