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2 results on '"Kelvin Memeh"'

1. Metastatic well-differentiated pancreatic neuroendocrine tumors to the liver: a narrative review of systemic and surgical management

Author

Tanaz Vaghaiwalla, Kelvin Memeh, Chih-Yi Liao, and Xavier M. Keutgen

Subjects
Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Abstract. Pancreatic neuroendocrine tumors (PNETs) are a rare group of neoplasms originating from the endocrine pancreas. PNETs are classified as functional or non-functional tumors. PNETs are more often diagnosed at a higher stage with distant metastases or advanced locoregional disease. The majority of individuals with hepatic metastases will ultimately die of liver failure; therefore, the treatment of liver tumor burden is critical to providing a survival impact. While surgical resection remains the only chance of cure for disease confined to the pancreas or for locoregional disease, the treatment of advanced or metastatic PNETs is more complex and often requires a multimodal approach. This review focuses on treatment options for well and moderately differentiated PNETs with metastatic disease to the liver. These include surgery, liver-directed therapies including ablative and intra-arterial therapies, and systemic therapies such as somatostatin analogues, targeted therapies, chemotherapy, and peptide receptor radionuclide therapy. Developing an individualized treatment strategy requires careful assessment of liver tumor burden and predicted biological behavior. Aggressive surgical resection of hepatic metastases secondary to PNET primary tumors is associated with improved survival in multiple retrospective studies. General goals of treatment for metastatic disease include prolonging overall survival and progression free survival, improving quality of life, and control of symptoms.

Published
2021
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2. A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features

Author

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

Subjects
Radiology, Nuclear Medicine and imaging, Computer-Aided Diagnosis
Abstract

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; [Formula: see text]] and 0.67 (SE = 0.05; [Formula: see text]) 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; [Formula: see text]), which was higher than the AUC of a commercially available molecular testing platform (AUC = 0.81, SE = 0.11; [Formula: see text]). 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.

Published
2022

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