Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial.

Background: Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.

Methods: This study was conducted according to TRIPOD + AI.

A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma.

The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available.

Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery.

This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data.

Variable selection for individualised treatment rules with discrete outcomes.

An individualised treatment rule (ITR) is a decision rule that aims to improve individuals' health outcomes by recommending treatments according to subject-specific information. In observational studies, collected data may contain many variables that are irrelevant to treatment decisions. Including all variables in an ITR could yield low efficiency and a complicated treatment rule that is difficult to implement.

Reporting of Participant Demographics in Clinical Trials Published in General Radiology Journals.

Objectives: The reporting of research participant demographics provides insights into study generalizability. Our study aimed to determine the frequency at which participant age, sex/gender, race/ethnicity, and socioeconomic status (SES) are reported and used for subgroup analyses in radiology randomized controlled trials (RCTs) and their secondary analyses; as well as the study characteristics associated with, and the classification systems used for demographics reporting.

Methods: RCTs and their secondary analyses published in 8 leading radiology journals between 2013 and 2021 were included.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.

Background: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations.

Development and Evaluation of a Digital HIV Risk Assessment Tool Incorporated Within an App-Based Self-Testing Program.

Background: Low-risk perception is an important barrier to the utilization of HIV services. In this context, offering an online platform for people to assess their risk of HIV and inform their decision to test can be impactful in increasing testing uptake. Using secondary data from the HIVSmart! quasirandomized trial, we aimed to identify predictors of HIV, develop a risk staging model for South African township populations, and validate it in combination with the HIVSmart! digital self-testing program.

Socioeconomic factors impact the risk of HIV acquisition in the township population of South Africa: A Bayesian analysis.

With a prevalence almost twice as high as the national average, people living in South African townships are particularly impacted by the HIV epidemic. Yet, it remains unclear how socioeconomic factors impact the risk of HIV infection within township populations. Our objective was to estimate the extent to which socioeconomic factors (dwelling situation, education, employment status, and monthly income) explain the risk of HIV in South African township populations, after controlling for behavioural and individual risk factors.

Considering strategies for SNP selection in genetic and polygenic risk scores.

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals.

Efficient penalized generalized linear mixed models for variable selection and genetic risk prediction in high-dimensional data.

Motivation: Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (PCs) adjustment to account for population structure and relatedness in high-dimensional penalized models. However, their use in binary trait GWAS rely on the invalid assumption that the residual variance does not depend on the estimated regression coefficients.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.

Purpose: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI).

Patients And Methods: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.

CT-based Bosniak classification of cystic renal lesions: is version 2019 an improvement on version 2005?

Objective: To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL).

Methods: This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up.

Novel mobility index tracks COVID-19 transmission following stay-at-home orders.

Considering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. Using a functional principal component analysis, we created a multidimensional mobility index (MI) using six metrics compiled by SafeGraph from all counties in Illinois, Ohio, Michigan and Indiana between January 1 to December 8, 2020. Changes in mobility were defined as a time-updated 7-day rolling average.

Strain Ultrasound Elastography of Aneurysm Sac Content after Randomized Endoleak Embolization with Sclerosing vs. Non-sclerosing Chitosan-based Hydrogels in a Canine Model.

Purpose: To compare the mechanical properties of aneurysm content after endoleak embolization with a chitosan hydrogel (CH) with that with a chitosan hydrogel with sodium tetradecyl sulfate (CH-STS) using strain ultrasound elastography (SUE).

Materials And Methods: Bilateral common iliac artery type Ia endoleaks were created in 9 dogs. Per animal, 1 endoleak was randomized to blinded embolization with CH, and the other, with CH-STS.

Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes.

Purpose: To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT.

Materials And Methods: In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm.

Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.

Objective: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm.

Methods: This dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up.

Assessment of long-term bowel dysfunction after restorative proctectomy for neoplastic disease: A population-based cohort study.

Background: The purpose of this study was to describe postoperative bowel dysfunction after restorative proctectomy, and to identify factors associated with its development.

Methods: Patients who underwent restorative proctectomy for rectal cancer between April 1998 and November 2018 were identified from the Hospital Episode Statistics database and linked to the Clinical Practice Research Datalink for postoperative follow-up. Bowel dysfunction was defined according to relevant symptom-based read codes and medication prescription-product codes.

Variable selection in regression-based estimation of dynamic treatment regimes.

Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that aim to recommend effective treatments for individual patients according to patient information history. DTRs can be estimated from models which include interactions between treatment and a (typically small) number of covariates which are often chosen a priori. However, with increasingly large and complex data being collected, it can be difficult to know which prognostic factors might be relevant in the treatment rule.

Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression.

Medical research increasingly includes high-dimensional regression modeling with a need for error-in-variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error-corrected cross-validation to enable error-in-variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high-dimensional data that are only partially corrupted by measurement error.

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction.

Interactive online informational and peer support application for patients with low anterior resection syndrome: patient survey and protocol for a multicentre randomized controlled trial.

Aim: Low anterior resection syndrome (LARS) refers to a constellation of bowel symptoms that affect the majority of patients following restorative proctectomy. LARS is associated with poorer quality of life (QoL), and can lead to distress, anxiety and isolation. Peer support could be an important resource for people living with LARS, helping them normalize and validate their experience.

Improved prediction of fracture risk leveraging a genome-wide polygenic risk score.

Background: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.

Polygenic Risk Score for Low-Density Lipoprotein Cholesterol Is Associated With Risk of Ischemic Heart Disease and Enriches for Individuals With Familial Hypercholesterolemia.

Background: The clinical implications of a polygenic risk score (PRS) for LDL-C (low-density lipoprotein cholesterol) are not well understood, both within the general population and individuals with familial hypercholesterolemia (FH).

Methods: We developed the LDL-C PRS using Least Absolute Shrinkage and Selection Operator regression in 377 286 White British participants from UK Biobank and tested its association with LDL-C according to FH variant carrier status in another 41 748 whole-exome sequenced individuals. Next, we tested for an enrichment of FH variant carriers among individuals with severe hypercholesterolemia and low LDL-C PRS.

Impact of Simulation Training on Radiology Resident Performance in Neonatal Head Ultrasound.

Rationale And Objectives: The aim of this study was to determine whether resident performance in head ultrasound on neonates improves following brain phantom simulation training.

Materials And Methods: Ten junior radiology residents with at least one year of radiology training were divided into two equal groups. Both groups received a detailed head ultrasound protocol sheet and observed a technologist perform a head ultrasound on a neonatal patient at the beginning of their first pediatric radiology rotation.