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Background: Investigate the ability of frailty status to predict post-surgical outcomes in patients with cutaneous malignancies of the scalp and neck undergoing flap reconstruction. Methods: National Surgical Quality Improvement Program database was used to isolate patients with cutaneous malignancies of the scalp and neck who underwent surgical resection between 2015 to 2019. Univariate and multivariate analyses were performed to determine if frailty score correlated with negative post-operative outcomes. Receiver operating characteristic (ROC) curves allowed testing of the discriminative performance of age versus frailty. Results: This study demonstrated an independent correlation between frailty and major complications as well as non-home discharge. In ROC curve analysis, frailty demonstrated superior discrimination compared to age for predicting major complications. Conclusion: Our study demonstrated an association between increasing frailty and major complications as well as the likelihood of a non-home discharge. When compared to age, frailty was also shown to be a better predictor of major complications. [ABSTRACT FROM AUTHOR]

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]