Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.

SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions.

The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system's capabilities for improving the accuracy of categorical prediction of cancer subtypes.

Perioperative Internal Iliac Artery Balloon Occlusion, In the Setting of Placenta Accreta and Its Variants: The Role of the Interventional Radiologist.

Placenta accreta and its variants (increta and percreta) are conditions of abnormal placentation that are encountered with increasing frequency. The spectrum of placenta accreta (including placenta increta and percreta) involves an abnormal attachment of the placental chorionic villi to the uterine myometrium. This abnormal attachment leads to increased adherence of the placenta to the uterus and abnormal placental-uterine separation at the time of delivery.