Mr. Julian Conn Busch Profile

Julian Conn Busch

at Univ of Chicago Medicine

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Publications (2)

Proceedings Article | 7 April 2023 Presentation + Paper

Novel integration of radiomics and deep transfer learning for diagnosis of indeterminate thyroid nodules on ultrasound

J. Cozzi, H. Li, J. Conn Busch, J. Williams, L. Lan, X. Keutgen, M. Giger

Proceedings Volume 12465, 1246507 (2023) https://doi.org/10.1117/12.2654264

KEYWORDS: Radiomics, Thyroid, Ultrasonography, Deep learning, Pathology, Particle filters, Tumor growth modeling, Education and training, Performance modeling, Feature extraction

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SPIE Journal Paper | 26 May 2022

A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features

Xavier Keutgen, Hui Li, Kelvin Memeh, Julian Conn Busch, Jelani Williams, Li Lan, David Sarne, Brendan Finnerty, Peter Angelos, Thomas Fahey, Maryellen Giger

JMI, Vol. 9, Issue 03, 034501, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.3.034501

KEYWORDS: Ultrasonography, Fine needle aspiration, Cancer, Surgery, Pathology, Medicine, Cell biology, Diagnostics, Algorithm development, Oncology

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Background: Ultrasound (US)-guided fine needle aspiration (FNA) cytology is the gold standard for the evaluation of thyroid nodules. However, up to 30% of FNA results are indeterminate, requiring further testing. In this study, we present a machine-learning analysis of indeterminate thyroid nodules on ultrasound with the aim to improve cancer diagnosis. Methods: Ultrasound images were collected from two institutions and labeled according to their FNA (F) and surgical pathology (S) diagnoses [malignant (M), benign (B), and indeterminate (I)]. Subgroup breakdown (FS) included: 90 BB, 83 IB, 70 MM, and 59 IM thyroid nodules. Margins of thyroid nodules were manually annotated, and computerized radiomic texture analysis was conducted within tumor contours. Initial investigation was conducted using five-fold cross-validation paradigm with a two-class Bayesian artificial neural networks classifier, including stepwise feature selection. Testing was conducted on an independent set and compared with a commercial molecular testing platform. Performance was evaluated using receiver operating characteristic analysis in the task of distinguishing between malignant and benign nodules. Results: About 1052 ultrasound images from 302 thyroid nodules were used for radiomic feature extraction and analysis. On the training/validation set comprising 263 nodules, five-fold cross-validation yielded area under curves (AUCs) of 0.75 [Standard Error (SE) = 0.04; P < 0.001] and 0.67 (SE = 0.05; P = 0.0012) for the classification tasks of MM versus BB, and IM versus IB, respectively. On an independent test set of 19 IM/IB cases, the algorithm for distinguishing indeterminate nodules yielded an AUC value of 0.88 (SE = 0.09; P < 0.001), which was higher than the AUC of a commercially available molecular testing platform (AUC = 0.81, SE = 0.11; P < 0.005). Conclusion: Machine learning of computer-extracted texture features on gray-scale ultrasound images showed promising results classifying indeterminate thyroid nodules according to their surgical pathology.

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