The size of Herafill
  granules has significant impact on the development of periosteal-like structures around the defect using Masquelet's induced membrane technique. Small granules show significantly increased regrowth of periosteum and improved bone formation adjacent to the induced membrane.
 The size of Herafill® granules has significant impact on the development of periosteal-like structures around the defect using Masquelet's induced membrane technique. Small granules show significantly increased regrowth of periosteum and improved bone formation adjacent to the induced membrane.
  To improve quality of trauma room management, intra- and inter-hospital benchmarking are important tools. However, primary data quality is crucial for benchmarking reliability. In this study, we analyzed the effect of a medical documentation assistant on documentation completeness in trauma room management in comparison to documentation by physicians involved in direct patient treatment.
 
  We included all patients treated in the trauma room from 2016/01/01 to 2016/12/31 that were documented with the trauma module of the German Emergency Department Medical Record V2015.1. We divided the data into documentation by medical documentation assistant (DA, 0700 to 1700), physician in daytime (PD, 0700 to 1700), and physician at night (PN, 1700 to 0700). Data were analyzed for completeness (primary outcome parameter) as well as diagnostic intervals.
 
  There was a significant increase in complete recorded data for DA (74.5%; IQR 14.5%) compared to PD (26.9%; IQR 18.7%; p < 0.001) and PN (30.8%; IQR 18.9; p < 0.001). The time to whole-body computed tomography (WBCT) significantly decreased for DA (19min; IQR 8.3) compared to PD (24min; IQR 12.8; p = 0.007) or PN (24.5min; IQR 10.0; p = 0.001).
 
  In presence of a qualified medical documentation assistant, data completeness and time to WBCT improved significantly. Therefore, utilizing a professional DA in the trauma room appears beneficial for data quality and time management.
 In presence of a qualified medical documentation assistant, data completeness and time to WBCT improved significantly.  https://www.selleckchem.com/products/pyrotinib.html  Therefore, utilizing a professional DA in the trauma room appears beneficial for data quality and time management.
  Clear cell sarcoma-like tumor of the gastrointestinal tract (CCSLTGT) is extremely rare. It is a mesenchymal neoplasm that usually forms in the small intestine of adolescents and young adults, is prone to local recurrence and metastasis, and has a high mortality rate. We report a patient with CCSLTGT with lymph node- and liver metastases, who continues to survive 6years after initial surgical resection.
 
  A 38-year-old woman presented with lightheadedness. Laboratory analysis revealed anemia (hemoglobin, 6.7g/dL), and enhanced computed tomography (CT) demonstrated a mass in the small intestine, about 6cm in diameter, with swelling of 2 regional lymph nodes. Double-balloon small intestine endoscopic examination revealed a tumor accompanied by an ulcer; the biopsy findings suggested a primary cancer of the small intestine. She was admitted, and we then performed a laparotomy for partial resection of the small intestine with lymph node dissection. Pathologic examination revealed CCSLTGT with regional lymph node metastases. About 3years later, follow-up CT revealed a single liver metastasis. Consequently, she underwent a laparoscopic partial liver resection. Histopathologic examination confirmed that the liver metastasis was consistent with CCSLTGT. It has now been 3years without a recurrence.
 
  Repeated radical surgical resection with close follow-up may be the only way to achieve long-term survival in patients with CCLSTGT.
 Repeated radical surgical resection with close follow-up may be the only way to achieve long-term survival in patients with CCLSTGT.
  Radiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy.
 
  For this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55-82 years; 64 men [mean age, 68 years; range, 55-82 years] and 36 women [mean age, 65 years; range, 60-72 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset.
 
  A support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set.
 
  The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.
 The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images.