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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]

Background/aim: Diffuse large B-cell lymphoma of the central nervous system (CNS-DLBCL) is an aggressive B-cell lymphoma with clinical and molecular heterogeneity. Primary CNS-DLBCL (PCNSL) affects the brain, eyes, leptomeninges, or spinal cord without systemic involvement. Secondary CNS-DLBCL (SCNSL) manifests concurrently with systemic lymphoma or as an isolated CNS relapse with poor prognosis., Materials and Methods: Next-generation sequencing (NGS) was used to identify genomic alterations in 32 PCNSL and 9 SCNSL cases. Single nucleotide variants and copy number variations in addition to the clinicopathologic data and proposed risk predictive values were compared to aid in diagnostic differentiation between the two types of lymphomas., Results: The MCD genotype, characterized by mutations in MYD88 and CD79B, is the most common alteration in PCNSL and is associated with lower survival rates. The frequency of MYD88 mutation was significantly higher in PCNSL compared to SCNSL (75.0% vs. 33.3%; p=0.042). Recurrent copy number loss of 6p21 occurred in 56.1% of cases, more often in PCNSL (65.6%) than in SCNSL (22.2%) (p=0.028). Diagnostic positive predictive values (PPV) of MYD88 mutation and 6p21 loss for PCNSL were 89% and 91%, respectively. PPV of both alterations was 93% for the diagnosis of PCNSL., Conclusion: MYD88 mutation and 6p21 loss were significantly higher in PCNSL than in SCNSL, and novel risk prediction models based on these distinct genomic profiles can aid in the clinical differentiation of PCNSL and SCNSL., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)