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.

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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..

Background. Anaplastic thyroid cancer (ATC) is rare, accounting for 1-2% of thyroid malignancies. Median survival is only 3-10 months, and the optimal therapeutic approach has not been established. This study aimed to evaluate outcomes in ATC based on treatment modality. Methods. Retrospective review was performed for patients treated at a single institution between 1990 and 2015. Demographic and clinical covariates were extracted from the medical record. Overall survival (OS) was modeled using Kaplan Meier curves for different treatment modalities. Univariate and multivariate analyses were conducted to assess the relationships between treatment and disease characteristics and OS. Results. 28 patients with ATC were identified (n = 16 female, n = 12 male; n = 22 Caucasian, n = 6 African-American; median age 70.9). Majority presented as Stage IVB (71.4%). Most patients received multimodality therapy. 19 patients underwent local surgical resection. 21 patients received locoregional external beam radiotherapy (EBRT) with a median cumulative dose of 3,000 cGy and median number of fractions of 16. 14 patients received systemic therapy (n = 11 concurrent with EBRT), most commonly doxorubicin (n = 9). 16 patients were never disease free, 11 patients had disease recurrence, and 1 patient had no evidence of disease progression. Median OS was 4 months with 1-year survival of 17.9%. Regression analysis showed that EBRT (HR: 0.174; 95% CI: 0.050–0.613; p=0.007) and surgical resection (HR: 0.198; 95% CI: 0.065–0.598; p=0.004) were associated with improved OS. Administration of chemotherapy was not associated with OS. Conclusions. Anaplastic thyroid cancer patients receiving EBRT to the thyroid area/neck and/or surgical resection had better OS than patients without these therapies, though selection bias likely contributed to improved outcomes since patients who can undergo these therapies tend to have better performance status. Prognosis remains poor overall, and new therapeutic approaches are needed to improve outcomes.

The recent literature on molecular, radiographic and cytologic characteristics of NIFTP are building our understanding of this neoplasm and support its indolent nature.

Context.— The use of whole slide images (WSIs) in diagnostic pathology presents special challenges for the cytopathologist. Informative areas on a direct smear from a thyroid fine-needle aspiration biopsy (FNAB) smear may be spread across a large area comprising blood and dead space. Manually navigating through these areas makes screening and evaluation of FNA smears on a digital platform time-consuming and laborious. We designed a machine learning algorithm that can identify regions of interest (ROIs) on thyroid fine-needle aspiration biopsy WSIs. Objective.— To evaluate the ability of the machine learning algorithm and screening software to identify and screen for a subset of informative ROIs on a thyroid FNA WSI that can be used for final diagnosis. Design.— A representative slide from each of 109 consecutive thyroid fine-needle aspiration biopsies was scanned. A cytopathologist reviewed each WSI and recorded a diagnosis. The machine learning algorithm screened and selected a subset of 100 ROIs from each WSI to present as an image gallery to the same cytopathologist after a washout period of 117 days. Results.— Concordance between the diagnoses using WSIs and those using the machine learning algorithm–generated ROI image gallery was evaluated using pairwise weighted κ statistics. Almost perfect concordance was seen between the 2 methods with a κ score of 0.924. Conclusions.— Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads.

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IntroductionThe Bethesda System for the Reporting of Thyroid Cytopathology (TBSRTC) is used to categorize and diagnose thyroid nodules by fine needle aspiration biopsy (FNAB). Each category in TBSRTC is associated with an estimated risk of malignancy (ROM). A subset of noninvasive encapsulated follicular variant of papillary thyroid carcinoma (niEFVPTC) was reclassified as a nonmalignant tumor: noninvasive follicular thyroid neoplasm with papillary‐like nuclear features (NIFTP). We studied the impact of this reclassification on the reported ROM in TBSRTC.Material and MethodsWe searched our institutional files for thyroid FNAB with surgical follow‐up. ROM for each TBSRTC category was calculated. Subsequently, cases of niEFVPTC were reviewed and reclassified as NIFTP, if appropriate. ROM for each category was then recalculated after the reclassification.ResultsTwenty‐six NIFTP were identified; the corresponding FNABs were distributed among all six TBSRTC categories. The majority of NIFTP FNAB were in the AUS/FLUS and suspicious for malignancy (SUSP) categories, 12 (46.2%) and 8 (30.8%), respectively. While the ROM changed for all diagnostic categories, the greatest change in ROM after reclassification was seen in these two categories. Absolute ROM for AUS/FLUS decreased from 25.0% to 21.0% and SUSP, from 71.7% to 58.3%, changes that were statistically significant.ConclusionsThe reclassification of niEFVPTC to NIFTP has significantly impacted the ROM in the TBSRTC at our institution. While there was a decrease in ROM for all categories, the greatest reduction to ROM was in the categories of AUS/FLUS and FN. These changes to the ROM should help guide surgical approach moving forward.