Tian-Ying Jia1, Jun-Feng Xiong2,3, Xiao-Yang Li1, Wen Yu1, Zhi-Yong Xu1, Xu-Wei Cai1, Jing-Chen Ma2, Ya-Cheng Ren2, Rasmus Larsson2, Jie Zhang4, Jun Zhao5,6,7, Xiao-Long Fu8. 1. Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, No.241 Huaihai Road, Shanghai, 200030, China. 2. School of Biomedical Engineering, Shanghai Jiao Tong University, No.800, Dong Chuan Road, Shanghai, 200030, China. 3. Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10032, USA. 4. Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China. 5. School of Biomedical Engineering, Shanghai Jiao Tong University, No.800, Dong Chuan Road, Shanghai, 200030, China. junzhao@sjtu.edu.cn. 6. SJTU-UIH Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China. junzhao@sjtu.edu.cn. 7. MED-X Research Institute, Shanghai Jiao Tong University, Shanghai, China. junzhao@sjtu.edu.cn. 8. Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, No.241 Huaihai Road, Shanghai, 200030, China. xlfu1964@hotmail.com.
Abstract
OBJECTIVES: The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models. METHODS: Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point. CONCLUSION: Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool. KEY POINTS: • Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status. • In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions. • The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.
OBJECTIVES: The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models. METHODS: Five hundred three lung adenocarcinomapatients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point. CONCLUSION: Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool. KEY POINTS: • Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status. • In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions. • The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.
Entities:
Keywords:
Epidermal growth factor receptor (EGFR); Non-small cell lung cancer (NSCLC); Radiomics; Random forest
Authors: J Guillermo Paez; Pasi A Jänne; Jeffrey C Lee; Sean Tracy; Heidi Greulich; Stacey Gabriel; Paula Herman; Frederic J Kaye; Neal Lindeman; Titus J Boggon; Katsuhiko Naoki; Hidefumi Sasaki; Yoshitaka Fujii; Michael J Eck; William R Sellers; Bruce E Johnson; Matthew Meyerson Journal: Science Date: 2004-04-29 Impact factor: 47.728
Authors: Thomas J Lynch; Daphne W Bell; Raffaella Sordella; Sarada Gurubhagavatula; Ross A Okimoto; Brian W Brannigan; Patricia L Harris; Sara M Haserlat; Jeffrey G Supko; Frank G Haluska; David N Louis; David C Christiani; Jeff Settleman; Daniel A Haber Journal: N Engl J Med Date: 2004-04-29 Impact factor: 91.245
Authors: Mark G Kris; Ronald B Natale; Roy S Herbst; Thomas J Lynch; Diane Prager; Chandra P Belani; Joan H Schiller; Karen Kelly; Harris Spiridonidis; Alan Sandler; Kathy S Albain; David Cella; Michael K Wolf; Steven D Averbuch; Judith J Ochs; Andrea C Kay Journal: JAMA Date: 2003-10-22 Impact factor: 56.272
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919
Authors: Erica L Carpenter; Despina Kontos; Bardia Yousefi; Michael J LaRiviere; Eric A Cohen; Thomas H Buckingham; Stephanie S Yee; Taylor A Black; Austin L Chien; Peter Noël; Wei-Ting Hwang; Sharyn I Katz; Charu Aggarwal; Jeffrey C Thompson Journal: Sci Rep Date: 2021-05-11 Impact factor: 4.379