Establishment of a Prediction Model for Overall Survival after Stereotactic Body Radiation Therapy for Primary Non-Small Cell Lung Cancer Using Radiomics Analysis

Abstract

Stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) leads to recurrence in approximately 18% of patients. We aimed to extract the radiomic features, with which we predicted clinical outcomes and to establish predictive models. Patients with primary non-metastatic NSCLC who were treated with SBRT between 2002 and 2022 were retrospectively reviewed. The 358 primary tumors were randomly divided into a training cohort of 250 tumors and a validation cohort of 108 tumors. Clinical features and 744 radiomic features derived from primary tumor delineation on pre-treatment computed tomography were examined as prognostic factors of survival outcomes by univariate and multivariate analyses in the training cohort. Predictive models of survival outcomes were established from the results of the multivariate analysis in the training cohort. The selected radiomic features and prediction models were tested in a validation cohort. We found that one radiomic feature showed a significant difference in overall survival (OS) in the validation cohort (p = 0.044) and one predicting model could estimate OS time (mean: 37.8 months) similar to the real OS time (33.7 months). In this study, we identified one radiomic factor and one prediction model that can be widely used.

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy

“…Franceschini et al successfully predicted disease-specific survival in patients treated with SBRT using four radiomic features (Franceschini et al 2020 ). Sawayanagi et al developed an OS prediction model for primary NSCLC after SBRT through radiomics analysis (Sawayanagi et al 2022 ). Jiao et al also developed a radiomic model to integrate risk of death estimates based on pre- and post-treatment CT scans in patients receiving SBRT (Jiao et al 2021 ).…”

“…Radiomics, a non-invasive technology that converts medical images into a high-dimensional mineable feature space via high-throughput quantitative feature extraction (Bera et al 2022 ; Gillies et al 2016 ; Lambin et al 2012 , 2017 ; Reuze et al 2018 ), has been introduced for the prediction of treatment responses, patient stratification, and prognosis for lung cancer patients in recent years (Chen et al 2017 , 2023 ; Constanzo et al 2017 ; Coroller et al 2015 ; Huang et al 2016 ; Lee et al 2017 ; Li et al 2018 ; Mattonen et al 2016 ). In particular, radiomic features extracted from CT images have shown promising performance in predicting OS in NSCLC patients treated with SBRT (Jiao et al 2021 ; Li et al 2018 ; Sawayanagi et al 2022 ; Somasundaram et al 2023 ; Starkov et al 2019 ). However, most studies to date have employed OS as endpoint and there is a scarcity of studies that have integrated clinical and radiomic features to predict cancer-specific survival (CSS) in lung cancer patients undergoing SBRT.…”

Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

“…These potential biases may have an impact on the validity and reproducibility of the model. In another study, Sawayanagi et al demonstrated the prognostic value of GTV-derived GLSZM features on the pretherapy CT image on OS in patients with localized NSCLC treated with curative SBRT [39]. In contrast to these studies, the present study was designed with a much larger sample size and, more importantly, with external testing sets treated by SBRT.…”

Recent Advances and Challenges in Stereotactic Body Radiotherapy

“…These radiomic features, obtained prior to treatment initiation, offer invaluable insights into treatment outcomes and the likelihood of adverse events. Sawayanagi et al 6 investigated SBRT for early-stage NSCLC, identifying a radiomic feature and predictive model for overall survival, as well as assessing recurrence rates. Shen et al 7 established a predictive model employing radiomic features for radiation-induced liver disease in patients with hepatocellular carcinoma undergoing SBRT.…”