Browsing by Subject "radiomics"
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Item Open Access Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.(Physics in medicine and biology, 2019-01-08) Lafata, Kyle J; Hong, Julian C; Geng, Ruiqi; Ackerson, Bradley G; Liu, Jian-Guo; Zhou, Zhennan; Torok, Jordan; Kelsey, Chris R; Yin, Fang-FangThe purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p = 0.022) and Long-Run-High-Gray-Level-Emphasis (p = 0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum [Formula: see text] values of [Formula: see text], and [Formula: see text], for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses.Item Embargo CT-Based Thyroid Cancer Diagnosis using Deep Learning and Radiomics Fusion Method(2024) Dong, YunfeiPurposeThe aim of this study was to address the limitations observed in past research, particularly the limited accuracy of individual deep learning or radiomics methods in small datasets. By developing a fusion approach that integrates the two techniques, we hypothesized that the performance in CT-based thyroid cancer diagnosis could be improved. Materials and Methods Eighty-five patients with thyroid tumors (58 malignant, 27 benign) who underwent CT scans were included in this study. The dataset was divided into training (70%) and testing (30%). A shallow CNN model, including five convolutional layers and two fully connected layers, was developed for tumor classification. Radiomics features were extracted and selected using the pyradiomics package and statistical tests (T-test, etc.). These features were then utilized to develop a Multiple Logistic Regression (MLR) model for tumor classification. The CNN and MLR models were combined using a fusion method that calculates the weighted sum of each diagnostic output for classification. The accuracy of the diagnostic methods was evaluated for both the individual and combined fusion models. The statistical significance of the weighted combination model was examined using the Wilcoxon-Test. Results The CNN model achieved an accuracy of 82.713%, and the MLR model achieved an accuracy of 76.596%. The accuracy of the fusion model reached 85.372%, suggested the improvement of performance of the fusion approach over the individual models. The Wilcoxon-Test yielded a W-Statistic of 19410.0 and a p-value of 〖2.96×10〗^(-14), which is below the threshold of 0.05. Conclusion A fusion model combining deep learning and radiomics methods was developed and showed improved accuracy in thyroid tumor diagnosis in a small dataset. The results showed a statistically significant difference between the fusion model and the individual models.
Item Open Access Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.(Physics in medicine and biology, 2018-11-08) Lafata, Kyle; Cai, Jing; Wang, Chunhao; Hong, Julian; Kelsey, Chris R; Yin, Fang-FangThe purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (NSCLC) histology. Forty-three radiomic features were considered and classified into one of four categories: Morphological, Intensity, Fine Texture, and Coarse Texture. First, a series of simulations were used to study feature-sensitivity to changes in spatial-temporal resolution. A dynamic digital phantom was used to generate images with different breathing amplitudes and SNR, from which features were extracted and characterized relative to initial simulation conditions. Stage I NSCLC patients were then retrospectively identified, from which three different acquisition-specific feature-spaces were generated based on free-breathing (FB), average-intensity-projection (AIP), and end-of-exhalation (EOE) CT images. These feature-spaces were derived to cover a wide range of spatial-temporal tradeoff. Normalized percent differences and concordance correlation coefficients (CCC) were used to assess the variability between the 3D and 4D acquisition techniques. Subsequently, three corresponding acquisition-specific logistic regression models were developed to classify lung tumor histology. Classification performance was compared between the different data-dependent models. Simulation results demonstrated strong linear dependences (p > 0.95) between respiratory motion and morphological features, as well as between SNR and texture features. The feature Short Run Emphasis was found to be particularly stable to both motion blurring and changes in SNR. When comparing FB-to-EOE, 37% of features demonstrated high CCC agreement (CCC > 0.8), compared to only 30% for FB-to-AIP. In classifying tumor histology, EoE images achieved an average AUC, Accuracy, Sensitivity, and Specificity of [Formula: see text], respectively. FB images achieved respective values of [Formula: see text], and AIP images achieved respective values of [Formula: see text]. Several radiomic features have been identified as being relatively robust to spatial-temporal variations based on both simulation data and patient data. In general, features that were sensitive to motion blurring were not necessarily the same features that were sensitive to changes in SNR. Our modeling results suggest that the EoE phase of a 4DCT acquisition may provide useful radiomic information, particularly for features that are highly sensitive to respiratory motion.