Abstract
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables—HPV status and tumor volume—were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
Highlights
The process of radiomics involves evaluating images on a voxel level to extract quantitative image features
When using the whole patient cohort, volume and human papillomavirus (HPV) status were selected from the forward selection of the clinical variables
Twelve radiomics features had a p-value < 0.01 when tumor volume and HPV status were held within the Cox proportional hazards model
Summary
The process of radiomics involves evaluating images on a voxel level to extract quantitative image features (i.e., texture) These radiomics features, combined with conventional prognostic factors (e.g., age), have improved patient outcome models, increasing the interest in radiomics studies [1,2,3,4,5]. Studies using computed tomography (CT) images from patients with head and neck cancer have found that radiomics features were significantly associated with local control, tumor failure, overall survival, and human papillomavirus (HPV) status [6,7,8,9,10,11,12]. Similar findings from positron-emission tomography (PET) images of head and neck cancer patients have shown that radiomics features were significantly associated with local control, tumor failure, overall survival, and freedom from distant metastases [10, 12,13,14]. For PET images, acquisition and reconstruction parameters have been shown to affect radiomics features; the number of iterations, matrix size, and smoothing filter have demonstrated variability [20,21,22,23,24]
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