<div>A new computer programme developed by scientists including those of Indian origin, has outperformed physicians at spotting brain cancer. The programme, developed by Case Western Reserve University in the US, is nearly twice as accurate as two neuroradiologists in determining whether abnormal tissue seen on magnetic resonance images (MRI) were dead brain cells caused by radiation, called radiation necrosis, or if brain cancer had returned.<br /><br />"One of the biggest challenges with the evaluation of brain tumour treatment is distinguishing between the confounding effects of radiation and cancer recurrence. On an MRI, they look very similar," said leader of the study Pallavi Tiwari, assistant professor at Case Western Reserve.<br /><br />However, treatments for radiation necrosis and cancer recurrence are far different. Quick identification can help speed prognosis, therapy and improve patient outcomes, the researchers said.<br /><br />With further confirmation of its accuracy, radiologists using their expertise and the programme may eliminate unnecessary and costly biopsies, Tiwari added. Brain biopsies are currently the only definitive test but are highly invasive and risky, causing considerable morbidity and mortality.<br /><br />To develop the programme, the researchers employed machine learning algorithms in conjunction with radiomics, the term used for features extracted from images using computer algorithms.<br /><br />The engineers, scientists and physicians trained the computer to identify radiomic features that discriminate between brain cancer and radiation necrosis, using routine follow-up MRI scans from 43 patients.<br /><br />The team then developed algorithms to find the most discriminating radiomic features, in this case, textures that can not be seen by simply eyeballing the images. "What the algorithms see that the radiologists do not are the subtle differences in quantitative measurements of tumour heterogeneity and breakdown in microarchitecture on MRI, which are higher for tumour recurrence," said Tiwari.<br /><br />More specifically, while the physicians use the intensity of pixels on MRI scans as a guide, the computer looks at the edges of each pixel, said Anant Madabhushi, professor at Case Western Reserve. "If the edges all point to the same direction, the architecture is preserved," said Madabhushi.<br /><br />In the direct comparison, two physicians and the computer programme analysed MRI scans from 15 patients from University of Texas Southwest Medical Centre in the US.<br /><br />One neuroradiologist diagnosed seven patients correctly, and the second physician correctly diagnosed eight patients. The computer programme was correct on 12 of the 15. The study appears in the American Journal of Neuroradiology.</div>
<div>A new computer programme developed by scientists including those of Indian origin, has outperformed physicians at spotting brain cancer. The programme, developed by Case Western Reserve University in the US, is nearly twice as accurate as two neuroradiologists in determining whether abnormal tissue seen on magnetic resonance images (MRI) were dead brain cells caused by radiation, called radiation necrosis, or if brain cancer had returned.<br /><br />"One of the biggest challenges with the evaluation of brain tumour treatment is distinguishing between the confounding effects of radiation and cancer recurrence. On an MRI, they look very similar," said leader of the study Pallavi Tiwari, assistant professor at Case Western Reserve.<br /><br />However, treatments for radiation necrosis and cancer recurrence are far different. Quick identification can help speed prognosis, therapy and improve patient outcomes, the researchers said.<br /><br />With further confirmation of its accuracy, radiologists using their expertise and the programme may eliminate unnecessary and costly biopsies, Tiwari added. Brain biopsies are currently the only definitive test but are highly invasive and risky, causing considerable morbidity and mortality.<br /><br />To develop the programme, the researchers employed machine learning algorithms in conjunction with radiomics, the term used for features extracted from images using computer algorithms.<br /><br />The engineers, scientists and physicians trained the computer to identify radiomic features that discriminate between brain cancer and radiation necrosis, using routine follow-up MRI scans from 43 patients.<br /><br />The team then developed algorithms to find the most discriminating radiomic features, in this case, textures that can not be seen by simply eyeballing the images. "What the algorithms see that the radiologists do not are the subtle differences in quantitative measurements of tumour heterogeneity and breakdown in microarchitecture on MRI, which are higher for tumour recurrence," said Tiwari.<br /><br />More specifically, while the physicians use the intensity of pixels on MRI scans as a guide, the computer looks at the edges of each pixel, said Anant Madabhushi, professor at Case Western Reserve. "If the edges all point to the same direction, the architecture is preserved," said Madabhushi.<br /><br />In the direct comparison, two physicians and the computer programme analysed MRI scans from 15 patients from University of Texas Southwest Medical Centre in the US.<br /><br />One neuroradiologist diagnosed seven patients correctly, and the second physician correctly diagnosed eight patients. The computer programme was correct on 12 of the 15. The study appears in the American Journal of Neuroradiology.</div>