Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics

Kuifei Chen; Liqiao Hou; Meng Chen; Shuling Li; Yangyang Shi; William Y Raynor; Haihua Yang

doi:10.3390/life13040884

Predicting the Efficacy of SBRT for Lung Cancer with ¹⁸F-FDG PET/CT Radiogenomics

Life (Basel). 2023 Mar 27;13(4):884. doi: 10.3390/life13040884.

Authors

Kuifei Chen^{1

2}, Liqiao Hou², Meng Chen^{1

2}, Shuling Li^{1

2}, Yangyang Shi³, William Y Raynor⁴, Haihua Yang^{1

2}

Affiliations

¹ Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China.
² Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China.
³ Department of Radiation Oncology, University of Arizona, Tucson, AZ 85724, USA.
⁴ Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA.

Abstract

Purpose: to develop a radiogenomic model on the basis of ¹⁸F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment.

Methods: A total of 123 patients with lung cancer who had undergone ¹⁸F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients' PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model.

Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849-0.851).

Conclusions: The radiogenomic model based on ¹⁸F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.

Keywords: 18F-fluorodeoxyglucose positron emission tomography/computed tomography; epidermal growth factor receptor; lung cancer; radiogenomics; stereotactic body radiation therapy.

Abstract

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