US scientists are using Artificial Intelligence (AI) to predict which lung-cancer patients will benefit from expensive immunotherapy.
While chemotherapy uses drugs to directly kill cancer cells, immunotherapy uses drugs to help a patient’s immune system fight cancer.
Currently, only about 20% of all cancer patients are likely to benefit from immunotherapy.
Scientists at the Centre for Computational Imaging and Personalised Diagnostics (CCIPD) are teaching a computer to find and note the changes in texture, volume and shape of a given lesion, not just its size, in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment.
“This is important because when a doctor decides based on CT images alone whether a patient has responded to therapy, it is often based on the size of the lesion,” said Mohammadhadi Khorrami, a graduate student working at CCIPD. “We have found that textural change is a better predictor of whether the therapy is working.”
“Sometimes, for example, the nodule may appear larger after therapy because of another reason, say a broken vessel inside the tumour — but the therapy is actually working. Now, we have a way of knowing that,” added Khorrami.
The new research, led by co-authors Mohammadhadi Khorrami and Prateek Prasanna, along with Anant Madabhushi and 10 other collaborators from six different institutions was published this month in the journal Cancer Immunology Research.
CCIPD, established by Anant Madabhushi at Case Western Reserve University — a leading private research institution in the US — in 2012, has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. The lab now includes nearly 60 researchers.
Some of the lab’s most recent work, in collaboration with New York University and Yale University, has used AI to predict which lung cancer patients would benefit from adjuvant chemotherapy based on tissue-slide images. That advancement was named by Prevention Magazine as one of the top 10 medical breakthroughs of 2018.
According to Madabhushi, the recent work by his lab will help oncologists know which lung-cancer patients will actually benefit from expensive immunotherapy, and who will not.
“Even though immunotherapy has changed the entire ecosystem of cancer, it also remains extremely expensive — about $200,000 per patient, per year,” said Madabhushi.
Having a tool based on the research being done now by his lab would go a long way toward “doing a better job of matching up which patients will respond to immunotherapy instead of throwing $800,000 down the drain,” Madabhushi added, referencing the four patients out of five who will not benefit, multiplied by annual estimated cost.
Prateek Prasanna, a postdoctoral research associate in Madabhushi’s lab, said the study showed that the results were consistent across scans of patients treated at two different sites and with three different types of immunotherapy agents.
“This is a demonstration of the fundamental value of the programme, that our machine-learning model could predict response in patients treated with different immune checkpoint inhibitors,” added Prasanna.
According to Prasanna, the initial study used CT scans from 50 patients to train the computer and create a mathematical algorithm to identify the changes in the lesion. He said the next step would be to test the programme on cases obtained from other sites and across different immunotherapy agents. This research recently won an ASCO 2019 Conquer Cancer Foundation Merit Award.