Utilizing Patient-Derived Xenografts to Model Precision Oncology for Biliary Tract Cancer.

Purpose: Biliary tract cancers, which are rare and aggressive malignancies, are rich in clinically actionable molecular alterations. A major challenge in the field is the paucity of clinically relevant biliary tract cancer models that recapitulate the diverse molecular profiles of these tumors. The purpose of this study was to curate a collection of patient-derived xenograft (PDX) models that reflect the spectrum of genomic alterations present in biliary tract cancers to create a resource for modeling precision oncology.

Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations.

Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Co-clinical Trial of Novel Bispecific Anti-HER2 Antibody Zanidatamab in Patient-Derived Xenografts.

Unlabelled: Zanidatamab is a bispecific human epidermal growth factor receptor 2 (HER2)-targeted antibody that has demonstrated antitumor activity in a broad range of HER2-amplified/expressing solid tumors. We determined the antitumor activity of zanidatamab in patient-derived xenograft (PDX) models developed from pretreatment or postprogression biopsies on the first-in-human zanidatamab phase I study (NCT02892123). Of 36 tumors implanted, 19 PDX models were established (52.

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

Background: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity.

Methods: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort.

Comparing machine learning algorithms for non-invasive detection and classification of failure in piezoresistive bone cement via electrical impedance tomography.

At an estimated cost of $8 billion annually in the United States, revision surgeries to total joint replacements represent a substantial financial burden to the health care system and a tremendous mental and physical burden on patients and their caretakers. Fixation failures, such as implant loosening, wear, and mechanical instability of the poly(methyl methacrylate) (PMMA) cement, which bonds the implant to the bone, are the main causes of long-term implant failure. Early and accurate diagnosis of cement failure is critical for developing novel therapeutic strategies and reducing the high risk of a misjudged revision.