Reducción incongruente de cadera tras luxación traumática por interposición de ceja posterior acetabular. Miniabordaje anterior de cadera y artroscopía seca para retiro del fragmento y evaluación articular / Posterior acetabular rim interposed within the joint after reduction of a traumatic hip dislocation. Anterior minimally invasive approach

Se presenta el caso de un paciente de veinte años a quien le queda un fragmento de la ceja acetabular posterior interpuesto dentro de la articulación tras la reducción de una luxación traumática de cadera. Se planteó un tratamiento por vía miniinvasiva anterior, con artroscopía seca con el fin de retirar el fragmento interpuesto, y la limpieza de la avulsión del ligamento redondo. En mesa de tracción se pudo retirar el fragmento interpuesto y evaluar el estado articular. Se muestran excelentes resultados clínicos e imagenológicos que desafían el concepto del abordaje posterior para lesiones posteriores. Nivel de Evidencia: III

The case of a twenty-year-old patient who has a fragment of the posterior acetabular rim interposed within the joint after reduction of a traumatic hip dislocation is presented. Anterior minimally invasive treatment was proposed, with dry arthroscopy to remove the interposed fragment and clean the round ligament avulsion. On the traction table, the interposed fragment could be removed, and the joint status evaluated. Excellent clinical and imaging results are shown what challenges the concept of a posterior approach for posterior lesions. Level of Evidence: III

Systematic CT perfusion acquisition in acute stroke increases vascular occlusion detection and thrombectomy rates.

BACKGROUND: In patients with stroke, current guidelines recommend non-invasive vascular imaging to identify intracranial vessel occlusions (VO) that may benefit from endovascular treatment (EVT). However, VO can be missed in CT angiography (CTA) readings. We aim to evaluate the impact of consistently including CT perfusion (CTP) in admission stroke imaging protocols. METHODS: From April to October 2020 all patients admitted with a suspected acute ischemic stroke underwent urgent non-contrast CT, CTA and CTP and were treated accordingly. Hypoperfusion areas defined by time-to-maximum of the tissue residue function (Tmax) >6 s, congruent with the clinical symptoms and a vascular territory, were considered VO (CTP-VO). In addition, two experienced neuroradiologists blinded to CTP but not to clinical symptoms retrospectively evaluated non-contrast CT and CTA to identify intracranial VO (CTA-VO). RESULTS: Of the 338 patients included in the analysis, 157 (46.5%) presented with CTP-VO (median Tmax >6s: 73 (29-127) mL). CTA-VO was identified in 83 (24.5%) of the cases. Overall CTA-VO sensitivity for the detection of CTP-VO was 50.3% and specificity was 97.8%. Higher hypoperfusion volume was associated with increased CTA-VO detection (OR 1.03; 95% CI 1.02 to 1.04). EVT was performed in 103 patients (30.5%; Tmax >6s: 102 (63-160) mL), representing 65.6% of all CTP-VO. Overall CTA-VO sensitivity for the detection of EVT-VO was 69.9% and specificity was 95.3%. Among patients who received EVT, the rate of false negative CTA-VO was 30.1% (Tmax >6s: 69 (46-99.5) mL). CONCLUSION: Systematically including CTP in acute stroke admission imaging protocols may increase the diagnosis of VO and rate of EVT.

Chest x-ray automated triage: A semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures.

BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest x-ray interpretation might improve by using a method that can exploit diverse types of annotations. This work presents a Deep Learning method based on the late fusion of different convolutional architectures, that allows training with heterogeneous data with a simple implementation, and evaluates its performance on independent test data. We focused on obtaining a clinically useful tool that could be successfully integrated into a hospital workflow. MATERIALS AND METHODS: Based on expert opinion, we selected four target chest x-ray findings, namely lung opacities, fractures, pneumothorax and pleural effusion. For each finding we defined the most suitable type of ground-truth label, and built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool. The performance was measured on two test datasets: an external openly-available dataset, and a retrospective institutional dataset, to estimate performance on the local population. RESULTS: The external and local test sets had 4376 and 1064 images, respectively, for which the model showed an area under the Receiver Operating Characteristics curve of 0.75 (95%CI: 0.74-0.76) and 0.87 (95%CI: 0.86-0.89) in the detection of abnormal chest x-rays. For the local population, a sensitivity of 86% (95%CI: 84-90), and a specificity of 88% (95%CI: 86-90) were obtained, with no significant differences between demographic subgroups. We present examples of heatmaps to show the accomplished level of interpretability, examining true and false positives. CONCLUSION: This study presents a new approach for exploiting heterogeneous labels from different chest x-ray datasets, by choosing Deep Learning architectures according to the radiological characteristics of each pathological finding. We estimated the tool's performance on the local population, obtaining results comparable to state-of-the-art metrics. We believe this approach is closer to the actual reading process of chest x-rays by professionals, and therefore more likely to be successful in a real clinical setting.

Hallazgos por imágenes en COVID-19: actualización y guía práctica / Imaging findings in COVID-19: update and practical guide

El cuadro clínico de la enfermedad conocida como COVID-19, causada por el nuevo coronavirus SARS-CoV-2 puede variar desde síntomas respiratorios leves hasta una insuficiencia respiratoria severa. Sus efectos en el organismo, especialmente la afección pulmonar, pueden ser visualizados a través de los estudios por imágenes. Si bien el diagnóstico de certeza se confirma mediante la reacción en cadena de la polimerasa con transcriptasa reversa (RT-PCR), los estudios por imágenes, especialmente la radiografía y la tomografía computarizada (TC) de tórax, desempeñan un papel fundamental en el manejo clínico de estos pacientes. Conocer su utilidad, casos de uso y hallazgos esperables brinda herramientas para el equipo de salud, temas que serán abordados en esta actualización y guía práctica. (AU)

The clinical pattern of the disease known as COVID-19, caused by the new coronavirus SARS-Cov-2 can range from mild respiratory symptoms to severe respiratory failure. Its effects on the body, especially the lung condition, can be visualized through imaging studies. While the diagnosis of certainty is confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR), imaging studies, especially chest xray and computed tomography (CT), play a critical role in the clinical management of these patients. Knowing their usefulness, use cases, and expected findings provides tools for the health care team, topics that will be addressed in this update, and practical guide. (AU)

Validation of an informatics tool to assess resident's progress in developing reporting skills.

BACKGROUND: Diagnostic radiology residency programs pursuits as main objectives of the development of diagnostic capabilities and written communication skills to answer clinicians' questions of referring clinicians. There has been also an increasing focus on competencies, rather than just education inputs. Then, to show ongoing professional development is necessary for a system to assess and document resident's competence in these areas. Therefore, we propose the implementation of an informatics tool to objectively assess resident's progress in developing diagnostics and reporting skills. We expect to found decreased preliminary report-final report variability within the course of each year of the residency program. RESULTS: We analyzed 12,162 evaluations from 32 residents (8 residents per year in a 4-year residency program) in a 7-month period. 73.96% of these evaluations belong to 2nd-year residents. We chose two indicators to study the evolution of evaluations: the total of discrepancies over the total of preliminary reports (excluding score 0) and the total of likely to be clinically significant discrepancies (scores 2b, 3b, and 4b) over the total of preliminary reports (excluding score 0). With the analysis of these two indicators over the evaluations of 2nd-year residents, we found a slight decrease in the value of the first indicator and relative stable behavior of the second one. CONCLUSIONS: This tool is useful for objective assessment of reporting skill of radiology residents. It can provide an opportunity for continuing medical education with case-based learning from those cases with clinically significant discrepancies between the preliminary and the final report.

Pneumo-PET-CT: Initial Results of This Novel Technique on the Evaluation of Esophageal and Gastric Tumors with Anatomic-Surgical Correlation.

We present the initial results of a novel hybrid scanning-based technique that combines pneumo-computed tomography (PNCT) with positron emission tomography (PET) using 2-(fluorine-18) fluoro-2-deoxy-D-glucose (FDG). We denominate it pneumo-PET-CT. The focus of our discussion will be on the description of the pneumo-PET-CT technique and the interpretation criteria with emphasis on its benefits and applications in the presurgical and postneoadjuvant therapy evaluation of esophageal, esophagogastric junction (EGJ), and gastric tumors.