TY - JOUR
T1 - Enhancing the prediction of KRAS mutation status in Asian lung adenocarcinoma
T2 - a comprehensive approach combining clinical, dual-energy spectral computed tomography, and radiomics features
AU - Ma, Jing Wen
AU - Yuan, Cai Xing
AU - Muhammad, Shan
AU - Wang, Yan Mei
AU - Qi, Lin Lin
AU - Jiang, Jiu Ming
AU - Jiang, Xu
AU - Miao, Lei
AU - Liu, Meng Wen
AU - Liang, Xin
AU - Qiu, Tian
AU - Zhang, Li
AU - Li, Meng
N1 - Publisher Copyright:
© AME Publishing Company.
PY - 2024/12/31
Y1 - 2024/12/31
N2 - Background: Lung adenocarcinoma (LUAD) is a sub-type of non-small cell lung cancer (NSCLC) that is often associated with genetic alterations, including the Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation. The KRAS mutation is particularly challenging to treat due to resistance to targeted therapies. This study aims to develop a predictive model for the KRAS mutation in patients with LUAD by integrating clinical, dual-energy spectral computed tomography (DESCT), and radiomics features. Methods: A total of 172 patients with LUAD were retrospectively enrolled and divided into a developing cohort (n=120) and a validation cohort (n=52). Clinical, DESCT and radiomics features were extracted and analyzed. Four predictive models were constructed: clinical, DESCT, radiomics, and combined clinical-DESCT-radiomics (C-S-R) model. The performance of these models was evaluated by the receiver operating characteristic curves. A nomogram incorporating clinical, DESCT, radiomics features with R-score was developed in the validation cohort. Results: In this study, 8.7% (15/172) of the patients showed KRAS mutation. The C-S-R model demonstrated the best performance, with an area under the curve (AUC) of 0.92 in the developing cohort and 0.87 in the validation cohort. The C-S-R model was not superior to radiomics model (P=0.28), but it was significantly better than DESCT model (P=0.01). Conclusions: This study suggests that integrating clinical, DESCT, and radiomics features can enhance the prediction of KRAS mutation in patients with LUAD.
AB - Background: Lung adenocarcinoma (LUAD) is a sub-type of non-small cell lung cancer (NSCLC) that is often associated with genetic alterations, including the Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation. The KRAS mutation is particularly challenging to treat due to resistance to targeted therapies. This study aims to develop a predictive model for the KRAS mutation in patients with LUAD by integrating clinical, dual-energy spectral computed tomography (DESCT), and radiomics features. Methods: A total of 172 patients with LUAD were retrospectively enrolled and divided into a developing cohort (n=120) and a validation cohort (n=52). Clinical, DESCT and radiomics features were extracted and analyzed. Four predictive models were constructed: clinical, DESCT, radiomics, and combined clinical-DESCT-radiomics (C-S-R) model. The performance of these models was evaluated by the receiver operating characteristic curves. A nomogram incorporating clinical, DESCT, radiomics features with R-score was developed in the validation cohort. Results: In this study, 8.7% (15/172) of the patients showed KRAS mutation. The C-S-R model demonstrated the best performance, with an area under the curve (AUC) of 0.92 in the developing cohort and 0.87 in the validation cohort. The C-S-R model was not superior to radiomics model (P=0.28), but it was significantly better than DESCT model (P=0.01). Conclusions: This study suggests that integrating clinical, DESCT, and radiomics features can enhance the prediction of KRAS mutation in patients with LUAD.
KW - duel-energy spectral computed tomography (DESCT)
KW - Kirsten rat sarcoma viral oncogene homolog mutation (KRAS mutation)
KW - lung adenocarcinoma (LUAD)
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85213846674&partnerID=8YFLogxK
U2 - 10.21037/tlcr-24-694
DO - 10.21037/tlcr-24-694
M3 - Article
AN - SCOPUS:85213846674
SN - 2226-4477
VL - 13
SP - 3566
EP - 3578
JO - Translational Lung Cancer Research
JF - Translational Lung Cancer Research
IS - 12
ER -