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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. [ABSTRACT FROM AUTHOR]

Abstract: Linear poly(ethylenimine) (LPEI) was modified by attachment of 3-(tetramethylferrocenyl)propyl groups to ca. 17% of its nitrogen atoms to form a new redox polymer, FcMe4-C3-LPEI, for use as an anodic mediator in glucose/O2 biofuel cells. Electrochemical properties of this polymer were compared to those of 3-(dimethylferrocenyl)propyl-modified LPEI (FcMe2-C3-LPEI). When FcMe4-C3-LPEI was complexed with glucose oxidase (GO x ) and cross-linked with ethylene glycol diglycidyl ether (EGDGE) to form hydrogels on planar, glassy carbon electrodes, limiting catalytic bioanodic current densities of up to ∼1.4mA/cm2 at 37°C were produced. The use of tetramethylferrocene moieties in place of dimethylferrocene moieties lowered the E 1/2 of the films by ca. 85mV. FcMe4-C3-LPEI was shown to be the more effective polymer for use in biofuel cells and, when coupled with a stationary O2 cathode comprised of laccase and cross-linked poly[(vinylpyridine)Os(bipyridyl)2Cl2+/3+] (PVP-Os) as a mediator, produced power densities of up to 57μW/cm2 at 37°C. Power density increased to 126μW/cm2 when a rotating biocathode was used. The power densities of biofuel cells made with either FcMe2-C3-LPEI or FcMe4-C3-LPEI were comparable. The FcMe2-C3-LPEI biofuel cells gave somewhat higher maximum currents, but the operating voltage and the stability of biofuel cells constructed with FcMe4-C3-LPEI was higher than that of cells using FcMe2-C3-LPEI. These polymers have immediate applications as amperometric glucose sensors as well as in biofuel cell materials and have the potential to be used in a wide range of small implantable electronic devices. [Copyright &y& Elsevier]