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Identifying final pathology of FNA-indeterminant nodules before surgical resection could decrease the number of unnecessary surgeries and total cost to patients. This project explores how radiomics (RM) and deep learning (DTLM) models may be combined to improve the potential for clinical interpretability of machine learning models in the task of classifying indeterminant thyroid nodules on ultrasound. Two radiomic and deep learning combination models were created: a simple classifier combination model (SCM) and an interpretability-driven combination model (ICM). SCM provided a nodule malignancy score. ICM merged radiomic and deep learning features through correlation and provided echogenicity-related, composition-related, and shape/margin-related malignancy scores which were averaged to yield an overall nodule malignancy score. Models were trained and tested on a de-identified dataset of 476 grayscale ultrasound images collected under IRB approval containing 222 images from 69 indeterminant nodules with a final pathology of malignant and 254 images from 82 indeterminant nodules with a final pathology of benign. Models were tested using 5- fold cross-validation by nodule over 100 iterations. Receiver-operating characteristic (ROC) analysis was conducted with area under the ROC curve (AUC) serving as the statistic of merit for model performance. Models yielded mean AUC [95%CI] of 0.75 [.67,.83], 0.70 [.62,.78], 0.77 [0.70,0.84], 0.76 [.69,.84] for RM, DTLM, SCM, and ICM respectively. This work failed to demonstrate a statistically significant difference in model performances. However, the ICM presents a novel method for combining radiomics and deep learning features focused on improving interpretability for clinical implementation in the task of indeterminant thyroid nodule classification.
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