Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

OBJECTIVE: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS: The final population comprised 743 patients (mean age 65  ±â€¯ 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION: Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Safety and Efficacy Outcomes From a Single-Center Study of Image-Guided Percutaneous Microwave Ablation for Primary and Metastatic Lung Malignancy.

Introduction: Image-guided percutaneous microwave ablation (MWA) is becoming a more common treatment option for patients with primary and metastatic lung malignancies. Nevertheless, there is limited literature on the safety and efficacy of MWA compared with standard-of-care therapy, including surgical resection and radiation. This study will report the long-term outcomes after MWA for pulmonary malignancies and investigate the factors related to efficacy, including lesion size, location, and ablation power. Methods: Retrospective single-center study analyzing 93 patients who underwent percutaneous MWA for primary or metastatic lung malignancies. Outcomes included immediate technical success, local tumor recurrence, overall survival, disease-specific survival, and complications. Results: At a single institution, 190 lesions (81 primary and 109 metastatic) were treated in 93 patients. Immediate technical success was achieved in all cases. Freedom from local recurrence was 87.6%, 75.3%, and 69.2% and overall survival was 87.7%, 76.2%, and 74.3% at 1 year, 2 years, and 3 years, respectively. Disease-specific survival was 92.6%, 81.8%, and 81.8%. The most common complication was pneumothorax, which occurred in 54.7% (104 of 190) of procedures, with 35.2% (67 of 190) requiring a chest tube. No life-threatening complications occurred. Conclusions: Percutaneous MWA seems safe and effective for treatment of primary and metastatic lung malignancies and should be considered for patients with limited metastatic burden and lesions less than 3 cm in size.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 ± 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 ± 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate the big data with an accuracy similar to the expert readers.

Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19.

AIM: We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19). METHODS: We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ±â€¯16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. RESULTS: In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (ß = 10.6, p = 0.005) and EAT attenuation (ß = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037). CONCLUSIONS: EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.

PURPOSE: To examine the independent and incremental value of CT-derived quantitative burden and attenuation of COVID-19 pneumonia for the prediction of clinical deterioration or death. METHODS: This was a retrospective analysis of a prospective international registry of consecutive patients with laboratory-confirmed COVID-19 and chest CT imaging, admitted to four centers between January 10 and May 6, 2020. Total burden (expressed as a percentage) and mean attenuation of ground glass opacities (GGO) and consolidation were quantified from CT using semi-automated research software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Logistic regression was performed to assess the predictive value of clinical and CT parameters for the primary outcome. RESULTS: The final population comprised 120 patients (mean age 64 ± 16 years, 78 men), of whom 39 (32.5%) experienced clinical deterioration or death. In multivariable regression of clinical and CT parameters, consolidation burden (odds ratio [OR], 3.4; 95% confidence interval [CI]: 1.7, 6.9 per doubling; P = .001) and increasing GGO attenuation (OR, 3.2; 95% CI: 1.3, 8.3 per standard deviation, P = .02) were independent predictors of deterioration or death; as was C-reactive protein (OR, 2.1; 95% CI: 1.3, 3.4 per doubling; P = .004), history of heart failure (OR 1.3; 95% CI: 1.1, 1.6, P = .01), and chronic lung disease (OR, 1.3; 95% CI: 1.0, 1.6; P = .02). Quantitative CT measures added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.93 vs 0.82, P = .006). The optimal prognostic cutoffs for burden of COVID-19 pneumonia as determined by Youden's index were consolidation of greater than or equal to 1.8% and GGO of greater than or equal to 13.5%. CONCLUSIONS: Quantitative burden of consolidation or GGO on chest CT independently predict clinical deterioration or death in patients with COVID-19 pneumonia. CT-derived measures have incremental prognostic value over and above clinical parameters, and may be useful for risk stratifying patients with COVID-19.

Interstitial lung abnormalities in patients with early rheumatoid arthritis: A pilot study evaluating prevalence and progression.

OBJECTIVE: Pulmonary disease is a leading cause of morbidity and mortality in rheumatoid arthritis (RA). In this study, we investigated the prevalence and progression of interstitial lung abnormalities (ILA) in a prospective cohort study of 18 subjects with early RA. METHODS: Eighteen adults diagnosed with anti-citrullinated protein-antibody-positive RA within the prior year underwent baseline high-resolution computed tomography (HRCT), symptom assessment, and pulmonary function and laboratory testing. The follow-up HRCT and clinical assessment were completed after 1 year. RESULTS: Seven of the 18 patients (39%) had baseline HRCT abnormalities including septal thickening, honeycombing, ground glass opacities, and/or traction bronchiectasis. At follow-up, 6 out of the 7 subjects (86%) with ILAs at baseline exhibited progression, while 10 out of 11 (91%) without ILAs at baseline remained stable. A higher Clinical Chronic Obstructive Pulmonary Disease Questionnaire score was associated with both the presence and progression of HRCT abnormalities (10 vs 2, p=0.045; 10 vs 2, p=0.009, respectively). C-reactive protein (CRP) trended higher in patients with radiologic abnormalities (3.5 mg/L vs 1.1 mg/L, p=0.08) and was significantly higher in those with progression (3.5 mg/L vs 1 mg/L, p=0.024). Smoking, pulmonary function, and autoantibodies were not associated with HRCT abnormalities. CONCLUSION: ILAs are prevalent in patients with early RA. If identified at baseline, radiographic progression of ILAs after 1 year is likely, while those without ILAs at baseline are unlikely to develop new ILAs. In addition, early respiratory symptoms and higher CRP levels may correlate with the presence and progression of underlying ILAs.

Pulmonary capillary hemangiomatosis: a lesson learned.

Pulmonary capillary hemangiomatosis (PCH) is a rare and controversial entity that is known to be a cause of pulmonary hypertension and is microscopically characterized by proliferation of dilated capillary-sized channels along and in the alveolar walls. Clinically, it is mostly seen in adults. Clinical features are characterized by nonspecific findings such as shortness of breath, cough, chest pain, and fatigue. It can be clinically indistinguishable from pre-capillary pulmonary arterial hypertension disorders such as primary pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension. However, the diagnostic distinction, which usually requires a multidisciplinary approach, is crucial in order to avoid inappropriate treatment with vasodilator medications usually used for PAH treatment. Prognosis of PCH remains poor with lung transplant being the only definitive treatment. We report an autopsy case of pulmonary capillary hemangiomatosis unmasked at autopsy that was treated with a prostacyclin analog, usually contraindicated in such patients. We emphasize that this entity should always be on the differential diagnosis in a patient with pulmonary hypertension and requires great vigilance on the part of the clinician, radiologist and pathologist to make the diagnosis and guide appropriate management.

Pulmonary capillary hemangiomatosis: a lesson learned

Pulmonary capillary hemangiomatosis (PCH) is a rare and controversial entity that is known to be a cause of pulmonary hypertension and is microscopically characterized by proliferation of dilated capillary-sized channels along and in the alveolar walls. Clinically, it is mostly seen in adults. Clinical features are characterized by nonspecific findings such as shortness of breath, cough, chest pain, and fatigue. It can be clinically indistinguishable from pre-capillary pulmonary arterial hypertension disorders such as primary pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension. However, the diagnostic distinction, which usually requires a multidisciplinary approach, is crucial in order to avoid inappropriate treatment with vasodilator medications usually used for PAH treatment. Prognosis of PCH remains poor with lung transplant being the only definitive treatment. We report an autopsy case of pulmonary capillary hemangiomatosis unmasked at autopsy that was treated with a prostacyclin analog, usually contraindicated in such patients. We emphasize that this entity should always be on the differential diagnosis in a patient with pulmonary hypertension and requires great vigilance on the part of the clinician, radiologist and pathologist to make the diagnosis and guide appropriate management.