Primary lung adenocarcinoma usually presents as a mass with spiculated margins with post-contrast enhancement or an endobronchial lesion.The unusual presentation of lung adenocarcinoma in a nonsmoker without any preexisting lung disease are described here. 3cases in this series with presentation as organizing pneumonia, miliary mottling pattern, and pleural effusion. A diffuse pneumonic type can mimic the clinical presentation of an infectious lung disease Thus, it can represent a diagnostic challenge, especially in the setting of rapidly progressive respiratory failure
Background: For chronic obstructive pulmonary disease spirometry assessment of post-bronchodilator FEV1 is critical for establishing the diagnosis, staging of the disease, predicting the outcome, and planning treatment. The six-minute walk test (6MWT) is an endurance test used to evaluate functional status of patients with cardiopulmonary disorders. CAT score is a well-established tool for determining the impact of COPD on one's health. Objective: To find out correlation between spirometric indices FEV1, FVC, FEV1/FVC, 6MWT and CAT Scores in COPD patients. Design: Cross sectional study Setting: Conducted from September 2019 to September 2021, for a period of 24 months in a tertiary care hospital in India Materials and Methods: In this study, the correlation between spirometric indices FEV1, FVC, FEV1/FVC, 6MWT and CAT Scores was analyzed in 100 patients diagnosed with COPD. Main outcome measures: 6MWT, CAT Scores and Spirometry values in COPD Sample size: 100 Results: Most patients were males(75%) between the ages of 61-70(36%). Majority were in Stage 3 COPD(55%), followed by stage 2 of GOLD staging(25%). Most of them had CAT score range of 21–30(51%), followed by 10–20(48%) and on 6MWT majority walked a distance of 301-400 meters(43%), followed by > 400 meters(35%). The pulse rate and mean systolic blood pressure increased while mean SpO2 decreased post-6WMT with a statistically significant difference(P<.001). The correlation of 6MWT and spirometry parameters shows there was a strong positive correlation of FEV1, FVC, and FEV1/FVC with 6MWT and correlation of CAT score and spirometry parameters shows there was a strong positive correlation of FEV1, FVC, and FEV1/FVC with 6MWT with a statistically significant difference (P<.0001). There was a strong negative correlation between 6MWT and CAT score with a statistically significant difference (P<.0001) Conclusion: The 6MWT and CAT score can be used as an effective alternative to determine the severity of COPD, according to the findings of this study especially in rural settings where access to spirometry is limited. Limitations: Majority of the individuals in our study had mild–moderate COPD severity, emphasizing that the 6MWT's validity and efficacy should be assessed in all COPD grades. Conflict of interest: None
During the current pandemic of COVID-19, numerous manifestations and complications have developed. As seen after the Second wave patients with COVID-19 are at high risk of fungal infections, such as mucormycosis, that may result directly from COVID-19 infection and/or as a side effect of the drugs used in the COVID-19 treatment protocol. Rhino-orbito-cerebral mucormycosis is a fungal infection that can be fatal especially in immunocompromised patients. In this report, we described a series of 3 cases with frontal sinus osteomyelitis in post-COVID-19 diabetic patients diagnosed with mucormycosis.
Background Most early lung cancers appear as pulmonary nodules on medical imaging, however, radiologists frequently overlook these on chest radiographs. We assessed if a deep learning-based artificial intelligence model can help detect pulmonary nodules on chest radiographs and compared its performance with board-certified human readers. Methods For this retrospective study, 308 chest radiographs were obtained between January 2019 to December 2021 from a tertiary care hospital. All radiographs were analyzed using a deep learning AI model called DxNodule AI Screen. Two expert board-certified radiologists established the ground truth, and 11 test readers independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results The standalone model had an AUROC of 0.905 [0.87, 0.94] in detecting pulmonary nodules. The mean AUROC across the 11 readers improved from 0.798 [0.74, 0.86] for unaided interpretation to 0.846 [0.82, 0.880] for AI-aided interpretation. With DxNodule AI Screen, readers were able to identify nodules at the correct locations, which they otherwise missed. The mean specificity, accuracy, PPV, and NPV of the readers improved significantly from 0.87 [0.78, 0.96], 0.78 [0.72, 0.84], 0.77 [0.65, 0.88], and 0.86 [0.81, 0.90] in the unaided session to 0.89 [0.82, 0.96], 0.83 [0.80, 0.85], 0.82 [0.73, 0.9], and 0.89 [0.86, 0.92], respectively in the aided session. Conclusion DxNodule AI Screen outperformed human readers in nodule detection performance on chest radiographs, and enhanced human readers’ performances when used as an aid.
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