PurposeWe propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models.ApproachFourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features.ResultsOut of 111 radiomic features, 43% had excellent reliability (ICC > 0.90), and 55% had either good (ICC > 0.75) or moderate (ICC > 0.50) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations.ConclusionsFeatures were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.
As of 14 December 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), caused nearly 269 million confirmed cases and almost 5.3 million deaths worldwide. Chest computed tomography (CT) has high diagnostic sensitivity for the detection of pulmonary disease in COVID-19 patients. Toward timely and accurate clinical evaluation and prognostication, radiomic analyses of CT images have been explored to investigate the correlation of imaging and non-imaging clinical manifestations and outcomes. Delta (∆) radiomics optimally performed from pre-infection to the post-critical phase, requires baseline data typically not obtained in clinical settings; additionally, their robustness is affected by differences in acquisition protocols. In this work, we investigated the reliability, sensitivity, and stability of whole-lung radiomic features of CT images of nonhuman primates either mock-exposed or exposed to SARS-CoV-2 to study imaging biomarkers of SARS-CoV-2 infection. Images were acquired at a pre-exposure baseline and post-exposure days, and lung fields were segmented. The reliability of radiomic features was assessed, and the dynamic range of each feature was compared to the maximum normal intra-subject variation and ranked.
Estimation of lymph node size and location from computed tomography images is relevant for many clinical applications. However, no previous study has had an intra- and inter-subject, quantitative, repeated measures design to assess the axillary lymphadenopathy. During the course of filovirus infection marked increase in axillary lymph node volume occurs along with edema. Computed tomographic images from eight nonhuman primates exposed intramuscularly (triceps brachii) to either Ebola or Marburg virus were analyzed using radiomics features. Normal values of attenuation in the axillae and surrounding muscles were compared to several baseline acquisitions. While intra-subject variability remained constrained, inter-subject variability was large enough to encourage the use of subject-specific feature values. First and second order radiomics features including those from grey-level co-occurrence matrix and grey-level size zone matrix were investigated. Changes in axillary space volume, mean attenuation, and attenuation distribution during filovirus infection bilaterally (ipsilateral and contralateral to the exposure site) indicated that ipsilateral axillae were affected to a greater degree than contralateral axillae when compared to baseline. Use of subject-specific averaged baselines is necessary to establish normal variation and to determine if post-exposure measurements are significantly different from baselines. A model-based classification, a Gaussian mixture model, can be used to estimate the changes in fractional volume of different tissues (fat, lymph nodes, other tissues within axillae) from attenuation histograms. Radiomics features investigated were consistent with the other descriptors. This method has the potential to be used as a biomarker for the understanding of filovirus diseases and for monitoring and evaluating therapeutic options.
The main goal of this work is to evaluate R2 * as an imaging biomarker of Ebola virus disease progression in the liver. Ebola virus (EBOV) disease targets the liver among other organs, resulting in hepatocellular necrosis and degeneration, hemorrhage, and edema. In the liver, EBOV destroys cells required to produce coagulation proteins and other important components of plasma and damage to blood vessels. Impairment of vascular integrity leads to disseminated intravascular coagulation and multiorgan failure, including lungs, kidneys, and liver. Noninvasive endogenous imaging biomarkers (e.g., R2 * relaxivity from MRI) are attractive targets to monitor changes of paramagnetic substances that occur from hemorrhage and liver dysfunction during EBOV infection. R2 maps exhibit a decreased relaxivity in edematous tissue due to higher T2 relaxation time compared to that observed in nonedematous tissue. However, during later phases of infection, increased vascular congestion, hemorrhage, or thrombi may result in increased R2 * because of local field inhomogeneities caused by paramagnetic molecules such as deoxyhemoglobin. In this study, R2 * relaxivity was followed in rhesus monkeys at baseline and after exposure to a low lethality variant of EBOV through a prolonged disease course. Increases in R2 * relaxivity measured after the acute phase of EBOV infection reached a peak about 3 weeks after exposure and then slowly returned to normal. After the acute phase, R2 * curve roughly followed later changes in liver function tests. Lower variability of R2 * in paravertebral muscles, hematocrit, and oxygen saturation, suggests that R2 * changes may be liver-specific.
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