A team of scientists at the Case Western Reserve University digital imaging lab is already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. Once again, the team is doing so by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. As with previous work, those changes have been discovered both inside and outside the tumor, a signature of the lab’s recent research.
Anant Madabhushu, from the Centre for Computational Imaging and Personalized Diagnostics (CCIPD), explains that this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that’s information oncologists do not currently have. The center 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.
Currently, only about 20 percent of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute. The team said that the recent work by his lab would help oncologists know which patients would actually benefit from the therapy, and who would not.
The team says that even though immunotherapy has changed the entire ecosystem of cancer, it also remains extremely expensive, about USD 200,000 per patient, per year. That’s a part of the financial toxicity that comes along with cancer and results in about 42 percent of all newly diagnosed cancer patients losing their life savings within a year of diagnosis.
Having a tool based on the latest research being done now by his lab would go a long way towards doing a better job of matching up which patient will respond to immunotherapy instead of throwing tons of money down the drain, referencing the four patients out of five who will not benefit, multiplies by the annual estimated cost.
The team says that one of the more significant advances in the research was the ability of the computer program to note the changes in texture, volume, and shape of a given lesion, not just its size. 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. They have found that textural change is a better predictor of whether the therapy is working. Sometimes, for instance, the nodule may appear larger after the therapy because of another reason, probably a broken vessel inside the tumor, but the therapy is actually working, now there is a way to know that.
The team also claimed 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 program, that the machine learning model could predict response in patients treated with different immune checkpoint inhibitors.
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. The next step will be to test the program on cases obtained from other sites and across different immunotherapy agents. Additionally, the team was able to show that the patterns on the CT scans which were most associated with a positive response to treatment and with overall patient survival were also found to be closely associated with the arrangement of immune cells on the original diagnostic biopsies of those patients.
This suggests that the CT scans actually appear to be capturing the immune response elicited by the tumors against the invasion of cancer and that the ones with the strongest immune response were showing the most significant textural change and most importantly, would best respond to the immunotherapy.