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.
Chromosome 4p deletions can lead to two distinct phenotypic outcomes: Wolf-–Hirschhorn syndrome (a terminal deletion at 4p16.3) and less frequently reported proximal interstitial deletions (4p11-p16). Proximal 4p interstitial deletions can result in mild to moderate intellectual disability, facial dysmorphisms, and a tall thin body habitus. To date, only 35 cases of proximal 4p interstitial deletions have been reported, and only two of these cases have been familial. The critical region for this syndrome has been narrowed down to 4p15.33-15.2, but the underlying causative genes remain unclear. In this study, we report the case of a 3-year-old female with failure to thrive, developmental and motor delays, and morphological features. The mother also had a 4p15.2-p14 deletion, and the proband was found to have a 13.4-Mb 4p15.2-p14 deletion by chromosome microarray analysis. The deleted region encompasses 16 genes, five of which have a high likelihood of contributing to the phenotype: PPARGC1A, DHX15, RBPJ, STIM2, and PCDH7. These findings suggest that multiple genes are involved in this rare proximal 4p interstitial deletion syndrome. This case highlights the need for healthcare providers to be aware of proximal 4p interstitial deletions and the potential phenotypic manifestations.
Myelofibrosis (MF) in the pediatric setting is uncommon and appears to
be pathogenically heterogeneous. MF due to intrinsic bone marrow
abnormality (IMF) is distinct from adult-type Primary myelofibrosis
(PMF) as they can lack the common genetic markers of clonality. To date,
all but two reported patients with pediatric MF and mutated MPIG6B have
been Arabic, and all reported cases have had a family history of
consanguinity. Here we report the first North American patient of
European ancestry with pediatric MF in whom novel compound heterozygous
mutations of MPIG6B were identified.
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