{"title":"预处理18F-FDG PET/CT结构分析在预测非小细胞肺癌患者EGFR和ALK突变中的重要性","authors":"Nurşin Agüloğlu, Ayşegül Aksu, Murat Akyol, Nuran Katgı, Tuğçe Çiftçi Doksöz","doi":"10.1055/a-1868-4918","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features.</p><p><strong>Methods: </strong>Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model.</p><p><strong>Results: </strong>For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%).</p><p><strong>Conclusions: </strong>In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.</p>","PeriodicalId":19238,"journal":{"name":"Nuklearmedizin-nuclear Medicine","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER.\",\"authors\":\"Nurşin Agüloğlu, Ayşegül Aksu, Murat Akyol, Nuran Katgı, Tuğçe Çiftçi Doksöz\",\"doi\":\"10.1055/a-1868-4918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features.</p><p><strong>Methods: </strong>Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model.</p><p><strong>Results: </strong>For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%).</p><p><strong>Conclusions: </strong>In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.</p>\",\"PeriodicalId\":19238,\"journal\":{\"name\":\"Nuklearmedizin-nuclear Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nuklearmedizin-nuclear Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1055/a-1868-4918\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/8/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuklearmedizin-nuclear Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-1868-4918","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/8/17 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER.
Objective: Identification of anaplastic lymphoma kinase (ALK) and epidermal growth factor receptor (EGFR) mutation types is of great importance before treatment with tyrosine kinase inhibitors (TKIs). Radiomics is a new strategy for noninvasively predicting the genetic status of cancer. We aimed to evaluate the predictive power of 18F-FDG PET/CT-based radiomic features for mutational status before treatment in non-small cell lung cancer (NSCLC) and to develop a predictive model based on radiomic features.
Methods: Images of patients who underwent 18F-FDG PET/CT for initial staging with the diagnosis of NSCLC between January 2015 and July 2020 were evaluated using LIFEx software. The region of interest (ROI) of the primary tumor was established and volumetric and textural features were obtained. Clinical data and radiomic data were evaluated with machine learning (ML) algorithms to create a model.
Results: For EGFR mutation prediction, the most successful machine learning algorithm obtained with GLZLM_GLNU and clinical data was Naive Bayes (AUC: 0.751, MCC: 0.347, acc: 71.4%). For ALK rearrangement prediction, the most successful machine learning algorithm obtained with GLCM_correlation, GLZLM_LZHGE and clinical data was evaluated as Naive Bayes (AUC: 0.682, MCC: 0.221, acc: 77.4%).
Conclusions: In our study, we created prediction models based on radiomic analysis of 18F-FDG PET/CT images. Tissue analysis with ML algorithms are non-invasive methods for predicting ALK rearrangement and EGFR mutation status in NSCLC, which may be useful for targeted therapy selection in a clinical setting.
期刊介绍:
Als Standes- und Fachorgan (Organ von Deutscher Gesellschaft für Nuklearmedizin (DGN), Österreichischer Gesellschaft für Nuklearmedizin und Molekulare Bildgebung (ÖGN), Schweizerischer Gesellschaft für Nuklearmedizin (SGNM, SSNM)) von hohem wissenschaftlichen Anspruch befasst sich die CME-zertifizierte Nuklearmedizin/ NuclearMedicine mit Diagnostik und Therapie in der Nuklearmedizin und dem Strahlenschutz: Originalien, Übersichtsarbeiten, Referate und Kongressberichte stellen aktuelle Themen der Diagnose und Therapie dar.
Ausführliche Berichte aus den DGN-Arbeitskreisen, Nachrichten aus Forschung und Industrie sowie Beschreibungen innovativer technischer Geräte, Einrichtungen und Systeme runden das Konzept ab.
Die Abstracts der Jahrestagungen dreier europäischer Fachgesellschaften sind Bestandteil der Kongressausgaben.
Nuklearmedizin erscheint regelmäßig mit sechs Ausgaben pro Jahr und richtet sich vor allem an Nuklearmediziner, Radiologen, Strahlentherapeuten, Medizinphysiker und Radiopharmazeuten.