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Yang C, Fan Y, Zhao D, Wang Z, Wang X, Wang H, Hu Y, He L, Zhang J, Wang Y, Liu Y, Sha X, Su J. Habitat-Based Radiomics for Predicting EGFR Mutations in Exon 19 and 21 From Brain Metastasis. Acad Radiol 2024:S1076-6332(24)00158-2. [PMID: 38599906 DOI: 10.1016/j.acra.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/12/2024]
Abstract
RATIONALE AND OBJECTIVES To explore and externally validate habitat-based radiomics for preoperative prediction of epidermal growth factor receptor (EGFR) mutations in exon 19 and 21 from MRI imaging of non-small cell lung cancer (NSCLC)-originated brain metastasis (BM). METHODS A total of 170, 62 and 61 patients from center 1, center 2 and center 3, respectively were included. All patients underwent contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans. Radiomics features were extracted from the tumor active (TA) and peritumoral edema (PE) regions in each MRI slice. The most important features were selected by the least absolute shrinkage and selection operator regression to develop radiomics signatures based on TA (RS-TA), PE (RS-PE) and their combination (RS-Com). Receiver operating characteristic (ROC) curve analysis was performed to access performance of radiomics models for both internal and external validation cohorts. RESULTS 10, four and six most predictive features were identified to be strongly associated with the EGFR mutation status, exon 19 and exon 21, respectively. The RSs derived from the PE region outperformed those from the TA region for predicting the EGFR mutation, exon 19 and exon 21. The RS-Coms generated the highest performance in the primary training (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.955 vs. 0.946 vs. 0.928), internal validation (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.879 vs. 0.819 vs. 0.882), external validation 1 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.830 vs. 0.825 vs. 0.822), and external validation 2 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.812 vs. 0.818 vs. 0.800) cohort. CONCLUSION The developed habitat-based radiomics model can be used to accurately predict the EGFR mutation subtypes, which may potentially guide personalized treatments for NSCLC patients with BM.
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Affiliation(s)
- Chunna Yang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Ying Fan
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Dan Zhao
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Zekun Wang
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Yanjun Hu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Lingzi He
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang 110122, PR China
| | - Jin Zhang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Yan Liu
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China.
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Akinci D'Antonoli T, Cavallo AU, Vernuccio F, Stanzione A, Klontzas ME, Cannella R, Ugga L, Baran A, Fanni SC, Petrash E, Ambrosini I, Cappellini LA, van Ooijen P, Kotter E, Pinto Dos Santos D, Cuocolo R. Reproducibility of radiomics quality score: an intra- and inter-rater reliability study. Eur Radiol 2024; 34:2791-2804. [PMID: 37733025 PMCID: PMC10957586 DOI: 10.1007/s00330-023-10217-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/03/2023] [Accepted: 07/30/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVES To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study. METHODS Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used. RESULTS The inter-rater reliability was poor to moderate for total RQS (ICC 0.30-055, p < 0.001) and very low to good for item's reproducibility (k - 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91-0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k - 0.40 to 1). CONCLUSIONS Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research. CLINICAL RELEVANCE STATEMENT There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation. KEY POINTS • Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research.
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Affiliation(s)
- Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland.
| | - Armando Ugo Cavallo
- Division of Radiology, Istituto Dermopatico dell'Immacolata (IDI) IRCCS, Rome, Italy
| | | | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Roberto Cannella
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Agah Baran
- MVZ Diagnostikum Berlin Gmbh, Diagnostisches Zentrum, Berlin, Germany
| | | | - Ekaterina Petrash
- Radiology Department, Research Institute of Children Oncology and Haematology of National Medical Research Center of Oncology n.a.N.N. Blokhin of Ministry of Health of RF, Moscow, Russia
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | | | - Peter van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Elmar Kotter
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
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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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Caloro E, Gnocchi G, Quarrella C, Ce M, Carrafiello G, Cellina M. Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review. Crit Rev Oncog 2024; 29:77-90. [PMID: 38505883 DOI: 10.1615/critrevoncog.2023050470] [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: 03/21/2024]
Abstract
The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.
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Affiliation(s)
- Elena Caloro
- Università degli studi di Milano, via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giulia Gnocchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Cettina Quarrella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Ce
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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Lv X, Li Y, Xu X, Zheng Z, Li F, Fang K, Wang Y, Wang B, Hou D. Multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in patients with non-small cell lung cancer brain metastases. Eur J Radiol Open 2023; 11:100521. [PMID: 37692549 PMCID: PMC10485591 DOI: 10.1016/j.ejro.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/09/2023] [Accepted: 08/31/2023] [Indexed: 09/12/2023] Open
Abstract
Background Osimertinib resistance is a major problem in the course of targeted therapy for non-small cell lung cancer (NSCLC) patients. To develop and validate a multisequence MRI-based radiomics nomogram for early prediction of osimertinib resistance in NSCLC with brain metastases (BM). Methods Pretreatment brain MRI of 251 NSCLC patients proven with BM were retrospectively enrolled from two centers (training cohort: 196 patients; testing cohort: 55 patients). According to the gene test result of osimertinib resistance, patients were labeled as resistance and non-resistance groups (training cohort: 65 versus 131 patients; testing cohort: 25 versus 30 patients). Radiomics features were extracted from T2WI, T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (T1-CE) sequences separately and radiomics score (rad-score) were built from the four sequences. Then a multisequence MRI-based nomogram was developed and the predictive ability was evaluated by ROC curves and calibration curves. Results The rad-scores of the four sequences has significant differences between resistance and non-resistance groups in both training and testing cohorts. The nomogram achieved the highest predictive ability with area under the curve (AUC) of 0.989 (95 % confidence interval, 0.976-1.000) and 0.923 (95 % confidence interval, 0.851-0.995) in the training and testing cohort respectively. The calibration curves showed excellent concordance between the predicted and actual probability of osimertinib resistance using the radiomics nomogram. Conclusions The multisequence MRI-based radiomics nomogram can be used as a noninvasive auxiliary tool to identify candidates who were resistant to osimertinib, which could guide clinical therapy for NSCLC patients with BM.
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Affiliation(s)
- Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Xiaoyue Xu
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Ziwei Zheng
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Fang Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Kun Fang
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Yue Wang
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Bing Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing 101149, 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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Lin Z, Gu W, Guo Q, Xiao M, Li R, Deng L, Li Y, Cui Y, Li H, Qiang J. Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer. Br J Radiol 2023; 96:20221063. [PMID: 37660398 PMCID: PMC10607390 DOI: 10.1259/bjr.20221063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.
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Affiliation(s)
| | - Weiyong Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | | | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | | | - Lin Deng
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | | | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Tang X, Li Y, Shen LT, Yan WF, Qian WL, Yang ZG. CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI. Acad Radiol 2023; 30:2574-2587. [PMID: 36941156 DOI: 10.1016/j.acra.2023.01.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 03/23/2023]
Abstract
RATIONALE AND OBJECTIVES We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI). MATERIALS AND METHODS A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760). CONCLUSION This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.
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Affiliation(s)
- Xin Tang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuan Li
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li-Ting Shen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei-Feng Yan
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wen-Lei Qian
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhi-Gang Yang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 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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Cao R, Chen H, Wang H, Wang Y, Cui EN, Jiang W. Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma. Front Oncol 2023; 13:1154327. [PMID: 37143947 PMCID: PMC10151709 DOI: 10.3389/fonc.2023.1154327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/30/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose To investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma. Methods A primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors. Results The RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models. Conclusions This study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.
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Affiliation(s)
- Ran Cao
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning, Shenyang, China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, Shenyang, China
| | - Yan Wang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning, Shenyang, China
| | - E-Nuo Cui
- School of Computer Science and Engineering, Shenyang University, Shenyang, China
- *Correspondence: E-Nuo Cui, ; Wenyan Jiang,
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, Shenyang, China
- *Correspondence: E-Nuo Cui, ; Wenyan Jiang,
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