Researchers discover AI-powered solution to assess impact of chemotherapy
A recent study claims to have discovered a more personalised “smart” diagnostic-imaging machine using AI technology which will help identify whether or not a patient will respond well to chemotherapy.
The details of the study were published in the Journal of JAMA Network Open.
"And it is further evidence that information gleaned by computational interrogation of the region outside the tumors on MRI (magnetic resonance images) and CAT (computed tomography) scans is extremely valuable and can predict response and benefit of chemotherapy in lung and breast cancer patients," said Madabhushi, a Professor involved in the study.
The research focuses on markers on tissue outside a breast tumour that can indicate whether a patient will respond to targeted chemotherapy.
The researchers have been able to classify patients with breast cancer into molecular subtypes, corresponding to those who are likely to respond to targeted chemotherapy and those who won't, simply by analysing an initial tissue sample.
"The work provides insights into the ability of radio mic analyses to capture clinically-significant tumor biology. It justifies additional studies to assess the clinical utility of such noninvasive approaches to guide therapeutic strategies in this disease," explained Varadan, co-author of the study.
The markers are not found on images made from tissue slides, but outside the tumour itself. They cannot be seen by the human eye, but are revealed by a process known as radiomics, which extracts relevant data from medical images like MRIs.
"Right now, these patients receive 'one-size-fits-all' treatment despite being quite diverse. What we're trying to do here is identify before treatment which patients will actually benefit from specific therapies. This could give doctors and patients information they did not have before," said Braman, another researcher.
Another study, published in March, also evaluates whether computer-extracted image patterns (or radiomics) outside a tumour can indicate whether a lung cancer patient will respond to targeted chemotherapy.
"The problem at the start is that only one in four lung cancer patients will respond favorably to chemotherapy, but virtually everyone gets that treatment," said Mohammadhadi Khorrami, one of the lead researchers.
"By looking both inside and outside the tumor, we achieved an accuracy of 77 per cent in determining which patients would benefit from chemotherapy--far better (68 per cent) than just looking at the tumor itself," Khorrami said.
"This can change the game, not only for the patient when it comes to outcome, but when it comes to cost overall for the health-care field. It costs about USD 30,000 or more a year for chemotherapy, so it's important to know who will respond to chemotherapy, and we're getting closer to a true biomarker to do that," he added.
