Facebook as a tool to promote radiology education: expanding from a local community of medical students to all of South America

Objective

To assess the feasibility of Facebook to promote a radiology education project and to expand it from our university community of medical students to a wider audience.

Materials and Methods

A group of 12 medical students created a Facebook page in June 2015, to contribute to radiology education in our university. From August 2015, clinical cases, including a brief explanation of clinical findings, along with different imaging modalities, were posted weekly and subscribers were encouraged to choose the most appropriate diagnosis. All cases were followed by the appropriate answer and an explanation to highlight imaging findings and diagnosis. Aiming to reach a larger audience, we also shared cases to a public Latin-American Facebook group, comprising a collective total of 28,182 physicians and medical students. Using the Facebook Insights tracking tool, we prospectively analyzed subscriber interaction with our page for 14 months.

Results

During the period analyzed, 35 cases were posted. The most common imaging modalities were X-ray (n = 15) and computed tomography (n = 13). Before we began posting the weekly cases, our page had 286 likes. By October 2016, that number had grown to 4244, corresponding to an increase of 1484% and eight times the size of the medical student community at our institution (n = 530). Medical students made up most (76%) of the subscribers, followed by radiology residents (6%). An excellent or moderate contribution to personal image interpretation skills was reported by 65.3% and 33.1% of the users, respectively.

Conclusion

Creating a Facebook page and posting weekly clinical cases proved to be an effective method of promoting radiology education.

Air trapping in usual interstitial pneumonia pattern at CT: prevalence and prognosis

This study was conducted to evaluate the presence of air trapping in patients with idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases (ILDs) (non-IPF), showing the radiological pattern of usual interstitial pneumonia (UIP). Retrospectively, we included 69 consecutive patients showing the typical UIP pattern on computed tomography (CT), and 15 final diagnosis of IPF with CT pattern “inconsistent with UIP” due to extensive air trapping. Air trapping at CT was assessed qualitatively by visual analysis and quantitatively by automated-software. In the quantitative analysis, significant air trapping was defined as >6% of voxels with attenuation between −950 to −856 HU on expiratory CT (expiratory air trapping index [ATIexp]) or an expiratory to inspiratory (E/I) ratio of mean lung density >0.87. The sample comprised 51 (60.7%) cases of IPF and 33 (39.3%) cases of non-IPF ILD. Most patients did not have air trapping (E/I ratio ≤0.87, n = 53, [63.1%]; ATIexp ≤6%, n = 45, [53.6%]). Air trapping in the upper lobes was the only variable distinguishing IPF from non-IPF ILD (prevalence, 3.9% vs 33.3%, p < 0.001). In conclusion, air trapping is common in patients with ILDs showing a UIP pattern on CT, as determined by qualitative and quantitative evaluation, and should not be considered to be inconsistent with UIP. On subjective visual assessment, air trapping in the upper lobes was associated with a non-IPF diagnoses.

Advances in Imaging and Automated Quantification of Pulmonary Diseases in Non-neoplastic Diseases

Histological examination has always been the gold standard for the detection and quantification of lung remodeling. However, this method has some limitations regarding the invasiveness of tissue acquisition. Quantitative imaging methods enable the acquisition of valuable information on lung structure and function without the removal of tissue from the body; thus, they are useful for disease identification and follow-up. This article reviews the various quantitative imaging modalities used currently for the non-invasive study of chronic obstructive pulmonary disease, asthma, and interstitial lung diseases. Some promising computer-aided diagnosis methods are also described.