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Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria.

This non-interventional, prospective phase IV trial evaluated trabectedin in patients with soft tissue sarcoma (STS) in real-life clinical practice across Germany. The primary endpoints were progression-free survival (PFS) rates at 3 and 6 months, as defined by investigators. Overall, 128 patients from 19 German sites were evaluated for efficacy and 130 for safety. Median age was 58.5 years (range: 23–84) and leiomyosarcoma was the most frequent histotype (n = 45; 35.2%). Trabectedin was mostly used as second/third-line treatment (n = 91; 71.1%). Median PFS was 5.2 months (95% CI: 3.3–6.7), with 60.7% and 44.5% of patients free from progression at 3 and 6 months, respectively. Median overall survival was 15.2 months (95% CI: 9.6–21.4). One patient achieved a complete and 14 patients a partial response, conferring an objective response rate of 11.7%. Decreases in white blood cells (27.0% of patients), platelets (16.2%) and neutrophils (13.1%) and increased alanine aminotransferase (10.8%) were the most common trabectedin-related grade 3/4 adverse drug reactions. Two deaths due to pneumonia and sepsis were considered trabectedin-related. Trabectedin confers clinically meaningful activity in patients with multiple STS histotypes, comparable to that previously observed in clinical trials and other non-interventional studies, and with a manageable safety profile.

Simple Summary Molecular tumor signatures are becoming increasingly important in the treatment of metastatic adenocarcinomas of the gastroesophageal junction (GEJ) and stomach (GC). There are few studies available analyzing data from the Caucasian population regarding molecular signatures and biomarkers. In the presented study, we investigated the distribution of gene variants in outpatients with advanced disease at the onset of diagnosis and correlated them with clinically relevant biomarkers according to ESCAT levels. In addition, we compared the results of conventional diagnostics (IHC/ISH) with NGS findings of gene amplifications. We were able to detect clinically relevant biomarkers according to ESCAT level I in approximately one-third of our patients, which have immediate therapeutic implications. The study highlights the importance of comprehensive molecular profiling for precision treatment of GEJ/GC and indicates that a biomarker evaluation should be performed for all patients with metastatic adenocarcinomas before the initiation of first-line treatment and during second-line or subsequent treatment. Abstract After several years of negative phase III trials in gastric and esophageal cancer, a significant breakthrough in the treatment of metastatic adenocarcinomas of the gastroesophageal junction (GEJ) and stomach (GC) is now becoming evident with the emerging of precision oncology and implementation of molecular targets in tumor treatment. In addition, new generation studies such as umbrella and basket trials are focused on these molecular targets, which makes an early molecular diagnosis based on IHC/ISH and NGS necessary. The required companion diagnostics of Her2neu overamplification or PD-L1 expression is based on immunohistochemistry (IHC) or additionally in situ hybridization (ISH) in case of an IHC Her2neu score of 2+. However, there are investigator-dependent differences in the assessment of Her2neu amplification and different PD-L1 scoring systems obtained by IHC/ISH. The use of high-throughput technologies such as next-generation sequencing (NGS) holds the potential to standardize the analysis and thus make them more comparable. In the presented study, real-world multigene sequencing data of 72 Caucasian patients diagnosed with metastatic adenocarcinomas of GEJ and stomach were analyzed. In the clinical companion diagnostics, we found ESCAT level I molecular targets in one-third of our patients, which directly determined the therapy. In addition, we found potential targets in 14/72 patients (19.4%) who potentially qualify for precision therapies in corresponding molecular studies. The study highlights the importance of comprehensive molecular profiling for precision treatment of GEJ/GC and indicates that a biomarker evaluation should be performed for all patients with metastatic adenocarcinomas before the initiation of first-line treatment and during second-line or subsequent treatment.