- Roche Launches Digital Pathology Platform for Lung Cancer
They aimed to show that 3D models are a “strikingly clearer method” to evaluate the distribution of COVID-19 related infection in the respiratory system.
"The full effect of COVID-19 on the respiratory system remains unknown, but the 3D digital segmented models provide clinicians a new tool to evaluate the extent and distribution of the disease in one encapsulated view," said Spieler.
"This is especially useful in the case where RT-PCR for SARS-CoV-2 is negative but there is strong clinical suspicion for COVID-19."
The 3D models all showed varying degrees of coronavirus-related infection in the respiratory tissues, particularly along the back of the lungs and bottom sections.
Specifically, Spieler and Schachner found that three patients who potentially had coronavirus underwent contrast enhanced thoracic CT when their symptoms worsened.
Two of the three individuals tested positive for SARS-CoV-2, while one was reverse transcription chain reaction (RT-PCR) negative, they said.
But this individual’s result was assumed to be a false negative because their clinical and imaging was convincing to experts.
RT-PCR test is a real-time test for the qualitative detection of nucleic acid from SARS-CoV-2 in upper and lower respiratory specimens, FDA said in press release.
"An array of RT-PCR sensitivities has been reported, ranging from 30-91%," said Spieler.
"This may be the result of relatively lower viral loads in individuals who are asymptomatic or experience only mild symptoms when tested. Tests performed when symptoms were resolving have also resulted in false negatives, which seemed to be the result in this case."
CT scans can range in form and structure to better correlate with disease progression. This allows for 3D segmentation of the data in which lung tissue can be volumetrically quantified or airflow patterns to be modeled.
The patients’ CT scans were segmented into 3D digital surface models using the scientific visualization program Avizo from Thermofisher Scientific and other techniques that the LSU’s Schachner Lab uses for anatomy research.
The project specifically focused on the visualization of lung damage in the 3D models compared to previous methods.
"Previously published 3D models of lungs with COVID-19 have been created using automated volume rendering techniques," said Schachner. "Our method is more challenging and time consuming, but results in a highly accurate and detailed anatomical model where the layers can be pulled apart, volumes quantified, and it can be 3D printed."
Schachner and Spieler stated that they are now segmenting more models for a larger follow up project.
Using CT scans to diagnose COVID-19 can significantly help hospitals quickly isolate patients and prevent further spreading.
At the end of May, researchers at Mount Sinai were the first in the country to use artificial intelligence to detect COVID-19 in patients based on CT scans of the chest combined with clinical data.
The study used scans of more than 900 patients that Mount Sinai received from institutional collaborators at hospitals in China. The scans included 419 confirmed COVID-19-positive cases and 486 COVID-19-negative scans.
The results showed that the algorithm had statistically significantly higher sensitivity, at 84 percent, compared to radiologists’ sensitivity of 75 percent when evaluating the images and clinical data.
Additionally, the algorithm recognized 68 percent of COVID-19-positive cases, when radiologists found these cases negative due to the negative CT appearance.
While CT scans are not widely used for diagnosis in the US, imaging can still play a critical role because it gives a rapid and accurate diagnosis, said Zahi Fayad, PhD, director of the biomedical engineering and imaging institute (BMEII) at the Icahn School of Medicine at Mount Sinai.
By 