Abstract
Simple SummaryIn this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability such as noise, resolution and movement through breathing. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δAUCCSH) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δAUCCSH during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.
Highlights
In non-small cell lung cancer, [18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) is a valuable tool for tumor detection and staging [1,2,3,4]
Patients with histologically proven inoperable locally advanced nonsmall-cell lung cancer (NSCLC) suitable for chemoradiotherapy were randomly assigned (1:1) to target volume delineation performed on 18 F-FDG-PET and computed tomography (CT) plus elective nodal irradiation and tumor-associated atelectasis, if applicable, or to target volumes defined by PET alone (18 F-FDG PET-based target group)
We tested the implementation of the method in the prediction of treatment outcome in 37 lung cancer patients and preliminary validation results showed that the normalized relative deviation of the AUC of the cumulative histogram, δAUCCSH, differentiates patients with local recurrence from patients without local recurrence
Summary
In non-small cell lung cancer, [18 F]fluoro-2-deoxy-D-glucose (FDG) PET is a valuable tool for tumor detection and staging [1,2,3,4]. The use of FDG-PET/CT is the standard of care for the definition of the target volume in radiotherapy treatment planning as well as for treatment monitoring [5,6,7,8]. Relative changes of standardized uptake value (SUV) in lung cancer patients correlated with the treatment outcome [9,10]. Radiomics is the extraction and analysis of a large number of quantitative image features (IF) including first-order (histogram and shape parameters) and second- or higherorder statistics (texture features), which provide spatial and voxel intensity information. PET IF variability has been proved to be very sensitive to image reconstruction settings, tumor segmentation methods, SUV resampling methods and texture feature matrix definitions [16,17].
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