Study of the X-Ray Diagnosis of Unstable Pelvic Fracture Displacements in Three-Dimensional Space and its Application in Closed Reduction

Objective: To study the method of X-ray diagnosis of unstable pelvic fractures displaced in three-dimensional (3D) space and its clinical application in closed reduction. Methods: Five models of hemipelvic displacement were made in an adult pelvic specimen. Anteroposterior radiographs of the pelvis were analyzed in PACS. The method of X-ray diagnosis was applied in closed reductions. From February 2012 to June 2016, 23 patients (15 men, 8 women; mean age, 43.4 years) with unstable pelvic fractures were included. All patients were treated by closed reduction and percutaneous cannulate screw fixation of the pelvic ring. According to Tile's classification, the patients were classified into type B1 in 7 cases, B2 in 3, B3 in 3, C1 in 5, C2 in 3, and C3 in 2. The operation time and intraoperative blood loss were recorded. Postoperative images were evaluated by Matta radiographic standards. Results: Five models of displacement were made successfully. The X-ray features of the models were analyzed. For clinical patients, the average operation time was 44.8 min (range, 20–90 min) and the average intraoperative blood loss was 35.7 (range, 20–100) mL. According to the Matta standards, 7 cases were excellent, 12 cases were good, and 4 were fair. Conclusions: The displacements in 3D space of unstable pelvic fractures can be diagnosed rapidly by X-ray analysis to guide closed reduction, with a satisfactory clinical outcome

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

Objectives: The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images. Methods: A dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms. Results: This study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set. Conclusions: The automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies

Combined Hydrotreating and Fluid Catalytic Cracking Processing for the Conversion of Inferior Coker Gas Oil: Effect on Nitrogen Compounds and Condensed Aromatics

Inferior coker gas oil (ICGO) derived from Venezuelan vacuum residue delayed coking is difficult to process using fluid catalytic cracking (FCC) or hydrocracking (HDC). The high content of nitrogen and condensed aromatics leads to major coking and readily deactivates the acid catalyst. In this work, a sequence of hydrotreating (HDT) and FCC processing is used to effectively convert ICGO to a high-value light oil product. The results show a higher overall conversion and a significant increase in the yield of gasoline compared to FCC processing. Molecular level characterization of the nitrogen compounds and condensed aromatics before and after HDT confirms that the nitrogen content and the 2+-ring aromatic content decreased, whereas the single-ring aromatics increased. The nitrogen compounds were mainly N₁, N₁O₁, N₁O₂, and N₁S₁ class species in basic nitrogen and N₁, N₁O₁, N₁O₂, N₂, and N₂O₁ class species in non-basic nitrogen. Moreover, the double bond equivalent of these species shifted to lower values. The decrease in the nitrogen compounds with a high heteroatom content reduces coking on the FCC catalyst. Subsequently, FCC unit performance and conversion to light oil increased. Moreover, the decrease in the size of N₁ class compounds and the ease of their cracking following HDT improved the performance of the FCC unit. Partial saturation of condensed aromatics following HDT also made it easier to crack these compounds