Abstract: The established standard to ensure state-of-the-art cancer treatment is through multidisciplinary tumor boards (TBs), although resource- and time-intensive. In this validation study, the multiple myeloma (MM)-TB was reexamined, aiming to validate our previous (2012-2014) results, now using the TB data from March 2020 to February 2021. We assessed MM-TB protocols, physicians' documentation, patient, disease, remission status, progression-free survival (PFS), and overall survival (OS) as left-truncated survival times. Moreover, TB-adherence, level of evidence according to grade criteria, time requirements, study inclusion rates, and referral satisfaction were determined. Within a 1-year period, 312 discussed patients were documented in 439 TB protocols. Patient and disease characteristics were typical for comprehensive cancer centers. The percentages of patients discussed at initial diagnosis (ID), with disease recurrence or in need of interdisciplinary advice, were 39%, 28%, and 33%, respectively. Reasons for the MM-TB presentation were therapeutic challenges in 80% or staging/ID-defining questions in 20%. The numbers of presentations were mostly one in 73%, two in 20%, and three or more in 7%. The TB adherence rate was 93%. Reasons for non-adherence were related to patients' decisions or challenging inclusion criteria for clinical trials. Additionally, we demonstrate that with the initiation of TBs, that the number of interdisciplinarily discussed patients increased, that TB-questions involve advice on the best treatment, and that levels of compliance and evidence can be as high as ≥ 90%. Advantages of TBs are that they may also improve patients', referrers', and physicians' satisfaction, inclusion into clinical trials, and advance interdisciplinary projects, thereby encouraging cancer specialists to engage in them

Abstract : Abstract: The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images. Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test. Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall ( P

Abstract: Purpose To compare the diagnostic performance and raters ́confidence of radiography, radiography equivalent dose multi-detector computed tomography (RED-MDCT) and radiography equivalent dose cone beam computed tomography (RED-CBCT) for finger fractures. Methods Fractures were inflicted artificially and randomly to 10 cadaveric hands of body donors. Radiography as well as RED-MDCT and RED-CBCT imaging were performed at dose settings equivalent to radiography. Images were de-identified and analyzed by three radiologists regarding finger fractures, joint involvement and confidence with their findings. Reference standard was consensus reading by two radiologists of the fracturing protocol and high-dose multi-detector computed tomography (MDCT) images. Sensitivity and specificity were calculated and compared with Cochrane ́s Q and post hoc analysis. Rater ́s confidence was calculated with Friedman Test and post hoc Nemenyi Test. Results Rater ́s confidence, inter-rater correlation, specificity for fractures and joint involvement were higher in RED-MDCT and RED-CBCT compared to radiography. No differences between the modalities were found regarding sensitivity. Conclusion In this phantom study, radiography equivalent dose computed tomography (RED-CT) demonstrates a partly higher diagnostic accuracy than radiography. Implementing RED-CT in the diagnostic work-up of finger fractures could improve diagnostics, support correct classification and adequate treatment. Clinical studies should be performed to confirm these preliminary results

Abstract: PURPOSE Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles. METHODS The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions. RESULTS The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform. CONCLUSION The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data