Atezolizumab in Combination With Carboplatin and Survival Outcomes in Patients With Metastatic Triple-Negative Breast Cancer: The TBCRC 043 Phase 2 Randomized Clinical Trial.

Importance: Agents targeting programmed death ligand 1 (PD-L1) have demonstrated efficacy in triple-negative breast cancer (TNBC) when combined with chemotherapy and are now the standard of care in patients with PD-L1-positive metastatic disease. In contrast to microtubule-targeting agents, the effect of combining platinum compounds with programmed cell death 1 (PD-1)/PD-L1 immunotherapy has not been extensively determined.

Objective: To evaluate the efficacy of atezolizumab with carboplatin in patients with metastatic TNBC.

UNesT: Local spatial representation learning with hierarchical transformer for efficient medical segmentation.

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

Objectives: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

RAP-NET: COARSE-TO-FINE MULTI-ORGAN SEGMENTATION WITH SINGLE RANDOM ANATOMICAL PRIOR.

Performing coarse-to-fine abdominal multi-organ segmentation facilitates extraction of high-resolution segmentation minimizing the loss of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models to perform single organ segmentation. We propose a coarse-to-fine pipeline RAP-Net, which starts from the extraction of the global prior context of multiple organs from 3D volumes using a low-resolution coarse network, followed by a fine phase that uses a single refined model to segment all abdominal organs instead of multiple organ corresponding models.

Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans.

Deep learning is a promising technique for spleen segmentation. Our study aims to validate the reproducibility of deep learning-based spleen volume estimation by performing spleen segmentation on clinically acquired computed tomography (CT) scans from patients with myeloproliferative neoplasms. As approved by the institutional review board, we obtained 138 de-identified abdominal CT scans.

High-resolution 3D abdominal segmentation with random patch network fusion.

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed tomography (CT) is a challenging topic, in part due to the limited memory provide by graphics processing units (GPU) and large number of parameters and in 3D fully convolutional networks (FCN). Two prevalent strategies, lower resolution with wider field of view and higher resolution with limited field of view, have been explored but have been presented with varying degrees of success. In this paper, we propose a novel patch-based network with random spatial initialization and statistical fusion on overlapping regions of interest (ROIs).

Learning from dispersed manual annotations with an optimized data weighting policy.

Deep learning methods have become essential tools for quantitative interpretation of medical imaging data, but training these approaches is highly sensitive to biases and class imbalance in the available data. There is an opportunity to increase the available training data by combining across different data sources (e.g.

Quantitative Comparison of Prone and Supine PERCIST Measurements in Breast Cancer.

Positron emission tomography (PET) is typically performed in the supine position. However, breast magnetic resonance imaging (MRI) is performed in prone, as this improves visibility of deep breast tissues. With the emergence of hybrid scanners that integrate molecular information from PET and functional information from MRI, it is of great interest to determine if the prognostic utility of prone PET is equivalent to supine.

Contrast Phase Classification with a Generative Adversarial Network.

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contrast enhanced CT is that phase discrepancies are latent in different tissues due to contrast protocols, vascular dynamics, and metabolism variance. Previous studies with deep learning frameworks have been proposed for classifying contrast enhancement with networks inspired by computer vision.

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision.

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores (e.g.

Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives.

Segmentation of abdominal computed tomography (CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing.

Outlier Guided Optimization of Abdominal Segmentation.

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary greatly among the population and within an individual over time. While continuous integration of novel datasets into the training set provides potential for better segmentation performance, collection of data at scale is not only costly, but also impractical in some contexts.

TBCRC 032 IB/II Multicenter Study: Molecular Insights to AR Antagonist and PI3K Inhibitor Efficacy in Patients with AR Metastatic Triple-Negative Breast Cancer.

Purpose: Preclinical data demonstrating androgen receptor (AR)-positive (AR) triple-negative breast cancer (TNBC) cells are sensitive to AR antagonists, and PI3K inhibition catalyzed an investigator-initiated, multi-institutional phase Ib/II study TBCRC032. The trial investigated the safety and efficacy of the AR-antagonist enzalutamide alone or in combination with the PI3K inhibitor taselisib in patients with metastatic AR (≥10%) breast cancer.

Patients And Methods: Phase Ib patients [estrogen receptor positive (ER) or TNBC] with AR breast cancer received 160 mg enzalutamide in combination with taselisib to determine dose-limiting toxicities and the maximum tolerated dose (MTD).

Stochastic tissue window normalization of deep learning on computed tomography.

Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT).

Anti-PD-1-Induced Pneumonitis Is Associated with Persistent Imaging Abnormalities in Melanoma Patients.

Pneumonitis may complicate anti-programmed death-1 (PD-1) therapy, although symptoms usually resolve with steroids. The long-term effects on respiratory function, however, are not well defined. We screened melanoma patients treated with anti-PD-1, with and without ipilimumab (anti-CTLA-4), and identified 31 patients with pneumonitis.

Translating preclinical MRI methods to clinical oncology.

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center.

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline.

Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.

Genomic landscape of a metastatic malignant proliferating tricholemmal tumor and its response to PI3K inhibition.

Proliferating tricholemmal tumors (PTTs) are rare benign neoplasms that arise from the outer sheath of a hair follicle. Occasionally, these PTTs undergo malignant transformation to become malignant proliferating tricholemmal tumors (MPTTs). Little is known about the molecular alterations, malignant progression, and management of MPTTs.

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to: 1) large anatomical and spatial variations of splenomegaly; 2) large inter- and intra-scan intensity variations on multi-modal MRI; and 3) limited numbers of labeled splenomegaly scans.

Automated Characterization of Body Composition and Frailty with Clinically Acquired CT.

Quantification of fat and muscle on clinically acquired CT scans is critical for determination of body composition, a key component of health. Manual tracing has been regarded as the gold standard method of body segmentation; however, manual tracing is time-consuming. Many semi-automated/automated algorithms have been proposed to avoid the manual efforts.

SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth.

A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.

Creating Value through Incremental Innovation: Managing Culture, Structure, and Process.

While the looming threat of large-scale disruptive innovation consumes disproportionate attention, incremental innovation remains an important tool for preserving and growing radiology practices within a dynamic marketplace. Incremental innovation, defined as the process of making improvements or additions to an organization while maintaining the organization's core product or service model, is accessible to practices of all sizes and must not be overlooked if practices are to maintain their competitive advantage. This article explores cultural, structural, and process enablers for incremental innovation.

Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.

Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.