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Background: The incidence of thyroid cancer in the United States has risen dramatically since the 1970s, driven by an increase in the diagnosis of small tumors. There is a paucity of published New Mexico (NM) specific data regarding thyroid cancer. We hypothesized that due to New Mexico's unique geographic and cultural makeup, the incidence of thyroid cancer and tumor size at diagnosis in this state would differ from that demonstrated on a national level., Methods: The New Mexico Tumor Registry (NMTR) was queried to include all NM residents diagnosed with thyroid cancer between 1992 and 2019. For 2010 to 2019, age-adjusted incidence rates were calculated via direct method using the 2000 United States population as the adjustment standard. Differences in incidence rate and tumor size by race/ethnicity and residence (metropolitan vs non-metropolitan) were assessed with rate ratios between groups. For 1992 to 2019, temporal trends in age-adjusted incidence rates for major race/ethnic groups in NM [Non-Hispanic White (NHW), Hispanic, and American Indian (AI)] were assessed by joinpoint regression using National Cancer Institute software., Results: Our study included 3,161 patients for the time period 2010 to 2019, including NHW (1518), Hispanic (1425), and AI (218) cases. The overall incidence rates for NM AIs were lower than those for Hispanics and NHWs because of a decreased incidence of very small tumors (<1.1 cm). The incidence rates for large tumors (>5.1 cm) was equivalent among groups. In the early 2000s, Hispanics also had lower rates of small tumors when compared to NHWs but this trend disappeared over time., Conclusion: AIs in New Mexico have been left out of the nationwide increase in incidental diagnosis of small thyroid tumors. This same pattern was noted for Hispanics in the early 2000s but changed over time to mirror incidence rates for NHWs. These data are illustrative of the health care disparities that exist among New Mexico's population and how these disparities have changed over time., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objectives: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data., Methods: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis., Results: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively., Conclusion: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

Background: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone., Methods: 174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups., Results: The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases., Conclusion: A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone., (Copyright © 2021 Elsevier Inc. All rights reserved.)