Results of two methods, conventional open reduction-internal plating and minimally invasive plating osteosynthesis (MIPO), in the treatment of mid-distal humeral shaft fractures were compared. Thirty-three patients were retrospectively analysed and divided into two groups. Group A (n=17) patients were treated by MIPO and group B (n=16) by conventional plating. The mean operation time in group A was 92.35±57.68 minutes and 103.12±31.08 minutes in group B (P=0.513). Iatrogenic radial nerve palsy in group A was 0% (0/17) and 31.3% in group B (5/16 (P=0.012). The mean fracture union time in group A was 15.29±4.01 weeks (range 8-24 weeks), and 21.25±13.67 weeks (range 10-58 weeks) in group B (P=0.095). The mean UCLA end-result score in group A was 34.76±0.56 points (range 33-35), and 34.38± 1.41 points (range 30-35) in group B (P=0.299). The mean MEPI in group A was 99.41±2.43 points (range 90-100) and 99.69±1.25 points (range 95-100) in group B (P= 0.687). When compared to the conventional plating techniques, MIPO offers advantages in terms of reduced incidence of iatrogenic radial nerve palsies and accelerated fracture union and a similar functional outcome with respect to shoulder and elbow function.
Langerhans cell histiocytosis (LCH) and Erdheim-Chester disease (ECD) share similar clinical features and mechanisms. In very rare circumstances, the two diseases coexist in the same patient. Here we report such a patient, who was first diagnosed with Hand-Schüller-Christian disease (HSC), a type of LCH. Several years later, the patient presented with severe exophthalmos and osteosclerosis on radiograph. New biopsy revealed ECD. We also analyze 54 cases of LCH and 6 cases of ECD diagnosed in our hospital, as well as their progression during a follow-up period of 8 years. In five cases of HSC (9.3% of LCH), a triad of central diabetes insipidus, hyperprolactinemia, and pituitary stalk thickening on magnetic resonance imaging (MRI) preceded the typical bone lesions by 4 -9 years. In addition, LCH was featured as elevated plasma alkaline phosphatase (ALP), which was normal in ECD. Combined with a literature review, several features are summarized to differentiate ECD from HSC. In patients with diabetes insipidus, concomitant hyperprolactinemia and pituitary stalk thickening on MRI indicate a possible HSC. Additionally, if osteosclerosis is observed in a patient with LCH, the coexistence of ECD should be considered. The Oncologist 2013;18:19 -24 Implications for Practice: Central diabetes insipitus (CDI) is usually the first or one of the first symptoms of Hand-Schüller-Christian disease (HSC). It is difficult to determine whether CDI is part of HSC at its onset. We propose a new triad of symptoms including central diabetes insipitus, hyperprolactinemia, and pituitary stalk thickening on MRI. If a patient is present with the triad, HSC should be considered. Bone scans are very useful to reveal HSC in the absence of bone pain. Langerhans cell histiocytosis (LCH) and Erdheim-Chester disease (ECD) are featured with osteolytic lesions and osteosclerosis, respectively. If osteosclerosis is observed in a patient with LCH, coexistence of ECD should be considered. A new biopsy is helpful for the diagnosis.
Background Artificial intelligence can assist in interpreting chest X-ray radiography (CXR) data, but large datasets require efficient image annotation. The purpose of this study is to extract CXR labels from diagnostic reports based on natural language processing, train convolutional neural networks (CNNs), and evaluate the classification performance of CNN using CXR data from multiple centers Methods We collected the CXR images and corresponding radiology reports of 74,082 subjects as the training dataset. The linguistic entities and relationships from unstructured radiology reports were extracted by the bidirectional encoder representations from transformers (BERT) model, and a knowledge graph was constructed to represent the association between image labels of abnormal signs and the report text of CXR. Then, a 25-label classification system were built to train and test the CNN models with weakly supervised labeling. Results In three external test cohorts of 5,996 symptomatic patients, 2,130 screening examinees, and 1,804 community clinic patients, the mean AUC of identifying 25 abnormal signs by CNN reaches 0.866 ± 0.110, 0.891 ± 0.147, and 0.796 ± 0.157, respectively. In symptomatic patients, CNN shows no significant difference with local radiologists in identifying 21 signs (p > 0.05), but is poorer for 4 signs (p < 0.05). In screening examinees, CNN shows no significant difference for 17 signs (p > 0.05), but is poorer at classifying nodules (p = 0.013). In community clinic patients, CNN shows no significant difference for 12 signs (p > 0.05), but performs better for 6 signs (p < 0.001). Conclusion We construct and validate an effective CXR interpretation system based on natural language processing.
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