Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Subventricular zone predicts high velocity of tumor expansion and poor clinical outcome in patients with low grade astrocytoma.

OBJECTIVE: The aim of this study is to clarify the association between subventricular zone (SVZ) involvement and velocity of diametric expansion(VDE) in patients with low-grade astrocytoma and also assessed the clinical outcome of those patients. MATERIALS AND METHODS: A total of 168 adult patients with newly diagnosed supratentorial low-grade astrocytoma were studied retrospectively. RESULTS: There were 73 patients had SVZ involvement. Patients with SVZ involvement(7.16 ±â€¯6.53 mm/y) had a higher VDE than patients without SVZ involvement(4.38 ±â€¯5.35 mm/y). VDE was modeled as a categorical variable(＜4, ≥4 and, ＜8, ≥8 and, ＜12, ≥12 mm/y). Logistic regression showed that SVZ involvement was associated with high VDE after adjusting by confounding variables. On the univariate analysis, the results showed that tumor involved with SVZ, VDE ≥ 4 mm/y, VDE ≥ 8 mm/y, and VDE ≥ 8 mm/y were significant predictors of a shorter OS, progression-free survival (PFS) and malignant progression-free survival (MFS)(all p ＜0.05). The categorical variables of VDE (＜4 mm/y, ≥4 mm/y and, ＜8 mm/y, ≥8 mm/y and, ＜12 mm/y, ≥12 mm/y) were adjusted by confounding variables in multivariate analysis, respectively. The results indicated that VDE ≥ 8 mm/y, VDE ≥ 12 mm/y were worse prognostic factors for OS, while VDE ≥ 4 mm/y, VDE ≥ 8 mm/y and VDE ≥ 12 mm/y were related to shorter PFS and MFS. In addition, SVZ involvement was prognostic factors in predicting OS and PFS except MFS. CONCLUSION: Our results demonstrated that SVZ involvement predicted high VDE and worse clinical outcome, and high VDE was associated with poor prognosis in patients with low-grade astrocytoma.