Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma
| Autor: | Shenglin Li, Jing Zhang, Guojin Zhang, Junlin Zhou, Liangna Deng, Zhiyong Zhao, Yuntai Cao |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Oncology Original article Cancer Research medicine.medical_specialty genetic structures medicine.medical_treatment Adenocarcinoma urologic and male genital diseases lcsh:RC254-282 Targeted therapy AUC area under the curve 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Epidermal growth factor receptor Lung cancer Computed tomography Lung biology business.industry Medical record TKIs tyrosine kinase inhibitors Nomogram lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease ARMA amplified refractory mutation system CT computed tomography EGFR epidermal growth factor receptor GGO ground-glass opacity 030104 developmental biology medicine.anatomical_structure 030220 oncology & carcinogenesis Cohort biology.protein EGFR mutation CEA carcinoembryonic antigen ROC receiver operating characteristic curve business |
| Zdroj: | Translational Oncology Translational Oncology, Vol 14, Iss 1, Pp 100954-(2021) |
| ISSN: | 1936-5233 |
| DOI: | 10.1016/j.tranon.2020.100954 |
| Popis: | Highlights • Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. • Non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. • The personalized nomogram model of CT features and clinical risk factors can easily and noninvasively predict the EGFR mutation status before surgery. Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma. The medical records of 403 patients with lung adenocarcinoma confirmed by histology from January 2016 to June 2020 were retrospectively collected. We combined CT features and clinical risk factors and used them to build a prediction nomogram. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. The nomogram was further validated in an independent external cohort. Finally, a nomogram that contained CT features and clinical risk factors, which could conveniently and non-invasively predict EGFR mutation status in patients with lung adenocarcinoma before surgery. |
| Databáze: | OpenAIRE |
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