Abstract
ObjectivesTo investigate the development and validation of a radiomics nomogram based on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma mutation(s) in the EGFR gene.MethodsA cohort of 109 (77/32 in training/validation cohort) consecutive lung adenocarcinoma patients with an EGFR mutation was enrolled in this study. A total of 1672 radiomic features were extracted from PET and CT images, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the radiomic features and construct the radiomics nomogram for the estimation of overall survival (OS), which was then assessed with respect to calibration and clinical usefulness. Patients with an EGFR mutation were divided into high- and low- risk groups according to their nomogram score. The treatment strategy for high- and low-risk groups was analyzed using Kaplan–Meier analysis and a log-rank test.ResultsThe C-index of the radiomics nomogram for the prediction of OS in lung adenocarcinoma in patients with an EGFR mutation was 0.840 and 0.803 in the training and validation cohorts, respectively. Distant metastasis [(Hazard ratio, HR),1.80], metabolic tumor volume (MTV, HR, 1.62), and rad score (HR, 17.23) were the independent risk factors for patients with an EGFR mutation. The calibration curve showed that the predicted survival time was remarkably close to the actual time. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Targeted therapy for patients with high-risk EGFR mutations attained a greater benefit than other therapies (p < 0.0001), whereas the prognoses of the two therapies were similar in the low-risk group (p = 0.85).ConclusionsDevelopment and validation of a radiomics nomogram based on PET/CT radiomic features combined with clinicopathological factors may guide targeted therapy for patients with lung adenocarcinoma with EGFR mutations. This is conducive to the advancement of precision medicine.
Highlights
Lung cancer is the leading cause of cancer deaths in the world and has the highest morbidity and mortality rates among all malignant tumors [1, 2]
The following inclusion criteria were applied to select patients from the medical database: a) an 18F-FDG PET/ CT examination within 1 month prior to surgery or biopsy, b) no anti-tumor treatment received before the 18F-FDG positron emission tomography/computed tomography (PET/CT) examination, c) with surgical or biopsy specimens confirmed by pathology, and d) with epidermal growth factor receptor (EGFR) mutation detection results
We developed a radiomics nomogram based on 18FFDG PET/CT radiomics features combined with clinicopathological factors to predict survival outcomes in patients with lung adenocarcinoma of EGFR mutations, with the aim of providing guidance for personalized targeted treatment of patients with lung adenocarcinoma with EGFR mutations
Summary
Lung cancer is the leading cause of cancer deaths in the world and has the highest morbidity and mortality rates among all malignant tumors [1, 2]. The prognosis of lung cancer has improved significantly with improvements in treatment methods, the 5-year survival rate for lung cancer patients remains at 17– 18% [7, 8]. The tumor, node, and metastasis (TNM) staging system is currently the most valuable and commonly used tumor staging system for assessing the prognosis of malignant tumors [9,10,11,12]. In clinical practice, it is found that the TNM staging system continues to have many shortcomings in the prognostic evaluation of lung cancer. A nomogram is regarded as a tool for quantifying risks and has become the focus of cancer research [16,17,18]
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