Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia.

In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain reaction results were included in this study. Totally 19 CT scans were analyzed. Lesion density, lesion volume, and lesion load were higher in the severe group than in the mild group. A significantly positive correlation was noted between major laboratory prognosticators with lesion volume and load. Lesion load at the first week of disease was significantly higher in severe group (p = 0.03). Our study revealed that several CT features were significantly different between severely and mildly infected forms of COVID-19 pneumonia. The CT lesion load value at the first week of infection may be applied as an outcome predictor of the disease.

Quantitative glioma grading using transformed gray-scale invariant textures of MRI.

BACKGROUND: A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings. METHOD: In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement. RESULTS: The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334. CONCLUSIONS: More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.