A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer

Abstract

Background The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. Methods A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. Results The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. Conclusions The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.

Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model

“…Deep learning has been heavily applied to medical image research for constructing appealing high-accuracy diagnostic and prediction models in individual studies in recent years [ 39 – 45 ]. In ENE detection, Kann and his colleagues developed the first deep learning 3D convolutional neural network model with impressive diagnostic performance and comprehensive external validation [ 15 , 46 , 47 ].…”

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence‐Free Survival in Hepatocellular Carcinoma

“…The DeepSurv model 22 in this study was implemented using Python version 3.7 within the PyTorch framework. Our DeepSurv model employed a grid search approach to determine the optimal hyperparameters through cross − validation.…”

“…However, DeepSurv model is often used as deep learning model for survival analysis in clinical studies, 18–20 based on nonlinear cox regression analysis 21 . DeepSurv is a deep feedforward neural network to analyze the influence of patient covariates on their risk rate, which and has been utilized as the deep learning modality for non‐small cell lung cancer in previous radiological studies 22,23 . There have been several studies evaluating HCC recurrence using radiomics.…”

“…21 DeepSurv is a deep feedforward neural network to analyze the influence of patient covariates on their risk rate, which and has been utilized as the deep learning modality for non-small cell lung cancer in previous radiological studies. 22,23 There have been several studies evaluating HCC recurrence using radiomics. However, most of the previous studies were based on MVI status.…”

Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis

“…Subsequently, 31 full-text articles were thoroughly examined for eligibility, with 19 articles being excluded for reasons detailed in Figure 1. Ultimately, 12 studies were included in this systematic review and meta-analysis [30][31][32][33][34][35][36][37][38][39][40][41].…”