Nivolumab Switch Maintenance Therapy After Tyrosine Kinase Inhibitor Induction in Metastatic Renal Cell Carcinoma: A Randomized Clinical Trial by the Interdisciplinary Working Group on Renal Tumors of the German Cancer Society (NIVOSWITCH; AIO-NZK-0116ass).

Background: The role of immune checkpoint inhibitor (ICI) maintenance therapy in metastatic renal cell carcinoma (mRCC) is undefined.

Objective: To determine whether switch maintenance therapy with nivolumab improves clinical outcomes in patients with mRCC with tyrosine kinase inhibitor (TKI) sensitivity.

Design, Setting, And Participants: This open-label phase 2 trial randomized patients with a partial response or stable disease after 10-12-wk TKI induction therapy to either TKI or nivolumab maintenance.

Mammography Image Quality Assurance Using Deep Learning.

Objective: According to the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) image quality in mammography is assessed by recording and analyzing a set of images of the CDMAM phantom. The EUREF procedure applies an automated analysis combining image registration, signal detection and nonlinear fitting. We present a proof of concept for an end-to-end deep learning framework that assesses image quality on the basis of single images as an alternative.

Determination of contrast-detail curves in mammography image quality assessment by a parametric model observer.

A novel approach is proposed for the determination of contrast-detail curves in mammography image quality assessment. The approach is compared with current practice using virtual mammography. A binary parametric model observer is applied to images of the CDMAM phantom.

A simple parametric model observer for quality assurance in computer tomography.

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC).

Probing lactate metabolism variations in large-scale bioreactors.

Lactate metabolism variations are frequently encountered in mammalian cell culture processes, especially during process scale-up. In this study, we took a multipronged approach to investigate the impact of pH, pCO , osmolality, base addition, and mixing conditions on the observed lactate variations in a Chinese Hamster Ovary (CHO) fed-batch process at 2,000 L scale. Two cultivating methods, CO -controlled and pH-controlled, were used to decouple the individual and synergistic effects from those factors.