BACKGROUND:The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspiration biopsy (FNAB). Each category has an associated risk of malignancy, which is important in the management of a thyroid nodule. More accurate predictions of malignancy may help to reduce unnecessary surgery. A machine learning algorithm (MLA) was developed to evaluate thyroid FNAB via whole slide images (WSIs) to predict malignancy. METHODS: Files were searched for all thyroidectomy specimens with preceding FNAB over 8 years.All cytologic and surgical pathology diagnoses were recorded and correlated for each nodule. One representative slide from each case was scanned to create a WSI. An MLA was designed to identify follicular cells and predict the malignancy of the final pathology. The test set comprised cases blindly reviewed by a cytopathologist who assigned a TBSRTC category. The area under the receiver operating characteristic curve was used to assess the MLA performance. RESULTS: Nine hundred eight FNABs met the criteria. The MLA predicted malignancy with a sensitivity and specificity of 92.0% and 90.5%, respectively. The areas under the curve for the prediction of malignancy by the cytopathologist and the MLA were 0.931 and 0.932, respectively. CONCLUSIONS: The performance of the MLA in predicting thyroid malignancy from FNAB WSIs is comparable to the performance of an expert cytopathologist. When the MLA and electronic medical record diagnoses are combined, the performance is superior to the performance of either alone. An MLA may be used as an adjunct to FNAB to assist in refining the indeterminate categories. Cancer Cytopathol 2020;128:287-295.
Sixty percent of papillary thyroid cancers (PTC) have an oncogenic (V600E) BRAF mutation. Inhibitors of BRAF and its substrates MEK1/2 are showing clinical promise in PTC. PTC progression can be decades long, which is challenging in terms of toxicity and cost. We previously found that MEK1/2 require copper (Cu) for kinase activity and can be inhibited with the well-tolerated and economical Cu chelator tetrathiomolybdate (TM). We therefore tested TM for antineoplastic activity in -positive PTC. The efficacy of TM alone and in combination with current standard-of-care lenvatinib and sorafenib or BRAF and MEK1/2 inhibitors vemurafenib and trametinib was examined in -positive human PTC cell lines and a genetically engineered mouse PTC model. TM inhibited MEK1/2 kinase activity and transformed growth of PTC cells. TM was as or more potent than lenvatinib and sorafenib and enhanced the antineoplastic activity of sorafenib and vemurafenib. Activated ERK2, a substrate of MEK1/2, overcame this effect, consistent with TM deriving its antineoplastic activity by inhibiting MEK1/2. Oral TM reduced tumor burden and vemurafenib in a -positive mouse model of PTC. This effect was ascribed to a reduction of Cu in the tumors. TM reduced P-Erk1/2 in mouse PTC tumors, whereas genetic reduction of Cu in developing tumors trended towards a survival advantage. Finally, TM as a maintenance therapy after cessation of vemurafenib reduced tumor volume in the aforementioned PTC mouse model. TM inhibits -driven PTC through inhibition of MEK1/2, supporting clinical evaluation of chronic TM therapy for this disease..
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