[PHARAO study: drug treatment of inflammatory rheumatic diseases : Guideline-conform treatment with biologics follows availability of biosimilars]. / PHARAO-Studie: Arzneimittelversorgung entzündlich rheumatischer Erkrankungen : Leitliniengerechte Versorgung mit Biologika folgt Verfügbarkeit von Biosimilars.

BACKGROUND: With the introduction of tumor necrosis factor (TNF) alpha inhibitors, the treatment of inflammatory rheumatic diseases (IRD) has undergone a fundamental change. Several of the originally high-priced biologics are now accessible as lower cost biosimilars, removing a significant impediment to prescription. OBJECTIVE: The present study investigated whether the availability of biosimilars is associated with an improvement in the care of IRD. Moreover, the subjective acceptance of biosimilars by physicians and patients was investigated and compared with objectifiable parameters. MATERIAL AND METHODS: Pseudonymized claims data of the Bavarian Association of Statutory Health Insurance Physicians from 2014 to 2019 as well as a paper and pencil survey of patients and rheumatologists formed the data basis of the study. RESULTS: During the observation period, the proportion of diagnosed patients receiving drug therapy increased from 38.5% to 43.2%. Also, the care changed in terms of the prescribed agents. Conventional drug therapy declined overall and, in particular, glucocorticoid prescriptions decreased from 39.3% in 2014 to 34.3% in 2019. At the same time, the proportion of targeted treatments increased from 12.3% to 20.4%. The median duration of basic treatment before first-time bDMARD use dropped from 3.15 years in 2014 to 2.17 years in 2019. CONCLUSION: Over the observation period, in which three biosimilars entered the market, the care of patients with IRD improved both quantitatively and qualitatively. The market share of biosimilars increased in parallel with this development. With an overall high acceptance of biosimilars, the assessment of the disease course by physicians and patients indicates a slight subjectively perceived advantage of therapy with originals compared to biosimilars, which, however, is not confirmed when standardized scores are applied. A possible explanation for this might be a nocebo effect, which could be minimized by suitable communication strategies.

Use of prognostic gene expression profiling tests in primary breast cancer treatment: a German real-world patient survey.

Aims: In primary breast cancer, gene expression profiling tests can support adjuvant chemotherapy treatment decisions. Real-world test use in Germany was investigated in an online survey of female breast cancer patients (n = 475). Materials & methods: Relationships between three groups were examined for clinical and statistical relevance: no test indication (n = 353), test indication and tested (n = 65), and test indication but not tested (n = 57). Results: A total of 47% of participants with a test indication were not tested. Test rates increased by 23% from 2012-2018 (49%) to 2019-2021 (60%). A total of 65% of patients without testing received chemotherapy, whereas only 38% of tested patients received chemotherapy. Conclusion: The use of gene expression profiling tests correlates with a real-world chemotherapy reduction. Gene expression profiling testing may improve patient confidence in the decision for or against chemotherapy.

In many cases, breast cancer can be removed by surgery. In addition to surgery, breast cancer patients may also receive chemotherapy; however, chemotherapy is not always useful. A gene expression profiling test can help physicians and patients decide if chemotherapy should be used. In a survey, 475 breast cancer patients in Germany were asked if they received such a test and chemotherapy. A total of 65% of patients who were not tested received chemotherapy compared with 38% of patients who were tested. Patients who received a test also felt more certain about their treatment decision. However, four of ten patients who were diagnosed between 2019 and 2021 and for whom a test would have helped in the treatment decision did not receive a test. Therefore, there is still room to increase the use of gene expression profiling tests for the benefit of breast cancer patients in Germany.

Low-Cost Multispectral System Design for Pigment Analysis in Works of Art.

To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need.

Leveraging expert feature knowledge for predicting image aesthetics.

The ability to rank images based on their appearance finds many real-world applications such as image retrieval or image album creation. Despite the recent dominance of deep learning methods in computer vision which often result in superior performance, they are not always the methods of choice because they lack interpretability. In this work, we investigate the possibility of improving image aesthetic inference of convolutional neural networks with hand-designed features that rely on domain expertise in various fields. We perform a comparison of hand-crafted feature sets in their ability to predict fine-grained aesthetics scores on two image aesthetics datasets. We observe that even feature sets published earlier are able to compete with more recently published algorithms and, by combining the algorithms together, one can obtain a significant improvement in predicting image aesthetics. By using a tree-based learner, we perform feature elimination to understand the best performing features overall and across different image categories. Only roughly 15 % and 8 % of the features are needed to achieve full performance in predicting a fine-grained aesthetic score and binary classification respectively. By combining hand-crafted features with meta-features that predict the quality of an image based on CNN features, the model performs better than a baseline VGG16 model. One can, however, achieve more significant improvement in both aesthetics score prediction and binary classification by fusing the hand-crafted features and the penultimate layer activations. Our experiments indicate an improvement up to 2.2 % achieving current state-of-the-art binary classification accuracy on the AVA dataset when the hand-designed features are fused with activation from VGG16 and ResNet50 networks.