RADIOLOGIA最新文献

Objective

To describe persistent pulmonary abnormalities detected on HRCT after 18 months of SARS-CoV-2 pneumonia, and to determine their extension and correlation with pulmonary function.

Patients and methods

A prospective cross-sectional study with an initial cohort of 90 patients in follow-up due to persisting lung abnormalities on imaging, functional respiratory impairment and/or respiratory symptoms. Of these, 31 (34%) were selected for analysis due to the persistence of their lung abnormalities on HRCT at 18 months after infection. A double reading was performed for each HRCT (62 observations).

Results

Of the 31 patients included: 20 (65%) were men; mean age was 67 years; 17 (55%) were smokers/ex-smokers. The mean hospitalisation time was 38 days. Eighteen (58%) patients were admitted to intensive care units. Five patients (16%) suffered an acute pulmonary thromboembolism and three (9.7%) had a pneumothorax. The mean time between the onset of pneumonia and the follow-up HRCT was 20.34 months. Extension of total pulmonary abnormalities, ground-glass opacities and reticulation was 19%, 12% and 4.5% respectively. The findings of the 62 readings were: ground-glass opacities (100%), reticulation (83%), subpleural curvilinear lines (62%), parenchymal bands (34%), traction bronchiectasis (69%), displacement of vessels/fissures (46%) and honeycombing (4.9%). Pulmonary function 18 months after the acute episode revealed a mean FVC of 92% of predicted value, with an FVC < 80% of predicted value in 11 patients (35.4%). Mean DLCO was 71% of predicted value, with a DLCO < 80% in 22 patients (70%). We observed a statistically significant relationship between total extension of abnormalities on HRCT and FVC (P < .05), and a trend towards statistical significance with DLCO (P = .051); there was a statistically significant relationship between the presence of ground-glass opacities and FEV1/FVC (P < .01). The relationships between reticulation and FVC, FVC%, FEV1, FEV1% and DLCO% were also considered statistically significant (P < .05).

Conclusion

Persistent interstitial lung abnormalities are seen on HRCT for a subset of patients infected with SARS-CoV-2 pneumonia. Seventy percent of these patients suffered a slight decrease in DLCO.

Radiology is now an essential part of clinical medicine, but undergraduate training does not reflect its importance in medical practice. In the current course, there are 46 medical schools in our country. According to the information published on the institutional websites, the study plans are very different in terms of the presence of diagnostic radiology and the organization of teaching. The estimated number of teaching hours in diagnostic radiology (mean ± standard deviation) is 61.3 ± 22.2 h (range from 26 h to 137 h). There is a great shortage of clinical university professors, and a generational change is essential. The current situation poses various challenges, including adapting to new teaching methods and technologies and promoting the presence of radiology in medical study plans, paying special attention to hospital practices, the final degree project (FDP) and the objective structured clinical examination (OSCE).

Objective

To review the different types of urinary diversion surgeries (UDS) in order to recognize the expected findings in a postoperative study, using different imaging techniques. To recognize the main postoperative complications, both early and late.

Conclusion

UDS are surgical procedures whose purpose is to redirect urine flow after cystectomy, generally in an oncologic context. The imaging evaluation of urological surgeries is often a radiological challenge, with CT being the most commonly used image modality. Therefore, it is essential to know the main surgical techniques, the expected postoperative findings and the optimization of imaging techniques for early diagnosis and correct evaluation of postoperative complications.

Purpose

To evaluate if nonlinear supervised learning classifiers based on non-contrast cerebral CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma (HIE).

Methods

Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with non-contrast CT performed within the first 24 hours of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30%, respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.

Results

105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC: 0.798, 0.752 and 0.742, respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (95% CI: 0.778-1), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.

Conclusion

The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

Objective

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit Insight CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

Methods

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the 2 CXRs was assessed using the AI tool.

Results

One hundred fourteen patients (57.4 ± 14.2 years; 65 of them were men, 57%) were retrospectively collected; and 15 (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time.

Conclusions

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Progressive population aging and improved healthcare have led to a significant increase in patients with hip arthroplasty (HA). In this patient group, the proportion of those who require a new arthroplasty (prosthetic replacement or secondary revision of the hip), has also increased. For this subgroup of patients in whom surgical prosthetic replacement should be considered but is contraindicated, a new technique has been developed since 2010: percutaneous injection of periprosthetic cement under fluoroscopic or CT control (“femoroplasty; FMP”) as an alternative and less invasive treatment compared to surgery to stabilize the HA without replacing it, with excellent results on patients’ quality of life. In this brief communication, we describe our positive experience regarding FMP, which we have performed for the first time in Spain on 4 patients (age range between 74-83 years, 2 female and 2 male patients, 3 right HA and 1 left HA), without post-complications. We highlight both the relative simplicity of this technique, which can be incorporated into radiological intervention even in regional hospitals, and the significant clinical improvement observed in all patients. In conclusion, we hope that our experience can contribute to the increased adoption of this innovative technique within the scientific community.

Placenta accreta spectrum (PAS) disorders (with increasing order of the depth of invasion: accreta, increta, percreta) are quite challenging for the purpose of diagnosis and treatment. Pathological examination or imaging evaluation are not very dependable when considered as stand-alone diagnostic tools. On the other hand, timely diagnosis is of great importance, as maternal and fetal mortality drastically increases if patient goes through the third phase of delivery in a not well-suited facility. A multidisciplinary approach for diagnosis (incorporating clinical, imaging, and pathological evaluation) is mandatory, particularly in complicated cases. For imaging evaluation, the diagnostic modality of choice in most scenarios is ultrasound (US) exam; patients are referred for MRI when US is equivocal, inconclusive, or not visualizing placenta properly. Herewith, we review the reported US and MRI features of PAS disorders (mainly focusing on MRI), going over the normal placental imaging and imaging pitfalls in each section, and lastly, covering the imaging findings of PAS disorders in the first trimester and cesarean section pregnancy (CSP).