PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

Anaplastic thyroid carcinoma: diagnostic challenges,histopathologic features and ancillary testing

Anaplastic thyroid carcinoma (ATC) is an aggressive thyroid malignancy with high mortality rate. This malignancy arises in thyroid follicular cells either denovo or with an associated differentiated thyroid carcinoma component. Clinically, it usually presents as a rapidly enlarging mass, pain and locally compressive symptoms. Histopathologic variability and heterogeneity often pose diagnostic challenges, especially in scant and paucicellular specimens. This article describes the clinical, histopathologic and molecular features of ATC and also addresses the associated diagnostic limitations and challenges.     

Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19 mitigation measures

Health Services and Outcomes Research Methodology - To slow the spread of COVID-19, most countries implemented stay-at-home orders, social distancing, and other nonpharmaceutical mitigation...     

Frame Invariance and Scalability of Neural Operators for Partial Differential Equations

Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes. Solving these PDEs often requires prohibitively high computational costs, especially when multiple evaluations must be made for different parameters or conditions. After training, neural operators can provide PDEs solutions significantly faster than traditional PDE solvers. In this work, invariance properties and computational complexity of two neural operators are examined for transport PDE of a scalar quantity. Neural operator based on graph kernel network (GKN) operates on graph-structured data to incorporate nonlocal dependencies. Here we propose a modified formulation of GKN to achieve frame invariance. Vector cloud neural network (VCNN) is an alternate neural operator with embedded frame invariance which operates on point cloud data. GKN-based neural operator demonstrates slightly better predictive performance compared to VCNN. However, GKN requires an excessively high computational cost that increases quadratically with the increasing number of discretized objects as compared to a linear increase for VCNN.     

WNT10A variants: following the pattern of inheritance in tooth agenesis and self-reported family history of cancer

Clinical Oral Investigations - The aim of this study was the analysis of WNT10A variants in seven families of probands with various forms of tooth agenesis and self-reported family history of...     

Use of neuron-specific enolase to predict mild brain injury in motorcycle crash patients with maxillofacial fractures: A pilot study

Purpose

Mild traumatic brain injury (TBI) is common but accurate diagnosis and its clinical consequences have been a problem. Maxillofacial trauma does have an association with TBI. Neuron-specific enolase (NSE) has been developed to evaluate neuronal damage. The objective of this study was to investigate the accuracy of NSE serum levels to detect mild brain injury of patients with sustained maxillofacial fractures during motor vehicle accidents.