Cheng Chang1,2, Xiaoyan Sun1,2, Gang Wang3, Hong Yu4, Wenlu Zhao5, Yaqiong Ge6, Shaofeng Duan6, Xiaohua Qian7, Rui Wang8, Bei Lei1, Lihua Wang1, Liu Liu1,2, Maomei Ruan1, Hui Yan1, Ciyi Liu1, Jie Chen9, Wenhui Xie1,2. 1. Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 2. Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China. 3. Statistical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 4. Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 5. Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China. 6. Pharmaceutical Diagnostic Department, GE Healthcare China, Shanghai, China. 7. Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. 8. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 9. Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Abstract
OBJECTIVES: Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics. METHODS: Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test. RESULTS: A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model. CONCLUSIONS: This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.
OBJECTIVES: Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics. METHODS: Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test. RESULTS: A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model. CONCLUSIONS: This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.
Authors: Clayton P Smith; Marcin Czarniecki; Sherif Mehralivand; Radka Stoyanova; Peter L Choyke; Stephanie Harmon; Baris Turkbey Journal: Abdom Radiol (NY) Date: 2019-06
Authors: S Rastegar; M Vaziri; Y Qasempour; M R Akhash; N Abdalvand; I Shiri; H Abdollahi; H Zaidi Journal: Diagn Interv Imaging Date: 2020-02-04 Impact factor: 4.026
Authors: Stefan Leger; Alex Zwanenburg; Karoline Pilz; Fabian Lohaus; Annett Linge; Klaus Zöphel; Jörg Kotzerke; Andreas Schreiber; Inge Tinhofer; Volker Budach; Ali Sak; Martin Stuschke; Panagiotis Balermpas; Claus Rödel; Ute Ganswindt; Claus Belka; Steffi Pigorsch; Stephanie E Combs; David Mönnich; Daniel Zips; Mechthild Krause; Michael Baumann; Esther G C Troost; Steffen Löck; Christian Richter Journal: Sci Rep Date: 2017-10-16 Impact factor: 4.379