AI Predicts How NSCLC Patients Will Respond to Chemotherapy

Researchers may soon be able to predict which lung cancer patients will respond to chemotherapy by using data from computed tomography (CT) images. Platinum-based chemotherapy is usually adopted as the first-line treatment of advanced-stage non–small cell lung cancer (NSCLC), although only about one out four patients responds well to this treatment.

There is presently no way to predict which patients can benefit the most from chemotherapy. CT exams are routinely used for tumor staging and monitoring treatment response. Researchers use a field of study called radiomics to extract quantitative, or measurable, data from CT images that can reveal disease characteristics not visible in the images alone. In the latest study, the researchers focused on identifying the role of radiomic texture features—both within and around the lung tumor—in predicting time to progression and overall survival, as well as response to chemotherapy in patients with NSCLC.

The researchers analyzed data from 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy. They randomly divided the patients into two sets with an equal number of responders and non-responders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients.

A computer analyzed the CT images of lung cancer to identify unique patterns of heterogeneity both inside and outside the tumor. These patterns were then compared between CT scans of patients who did and did not respond to chemotherapy. These feature patterns were then used to train a machine learning classifier in order to identify the likelihood that a lung cancer patient would respond to chemotherapy. The results showed that the radiomic features derived from within the tumor and the area around the tumor were able to distinguish patients who responded to chemotherapy from those who did not. Additionally, the radiomic features predicted time to progression and overall survival.

The radiomic data derived from CT images can also potentially help identify those patients who are at elevated risk for recurrence and who might benefit from more intensive observation and follow-up, according to Mohammadhadi Khorrami, M.S, a Ph.D. candidate from the Department of Biomedical Engineering, Case Western Reserve University School of Engineering in Cleveland, Ohio, who, along with Monica Khunger, M.D, from the Department of Internal Medicine at Cleveland Clinic, led the study.

“When we looked at patterns inside the tumor, we got an accuracy of 0.68. But when we looked inside and outside, the accuracy went up to 0.77,” said Khorrami. “Despite the large number of studies in the CT-radiomics space, the immediate surrounding tumor area, or the peritumoral region, has remained relatively unexplored. Our results showed clear evidence of the role of peritumoral texture patterns in predicting response and time to progression after chemotherapy.”

“This is the first study to demonstrate that computer-extracted patterns of heterogeneity, or diversity, from outside the tumor were predictive of response to chemotherapy,” said Dr. Khunger. “This is very critical because it could allow for predicting in advance of therapy which patients with lung cancer are likely to respond or not. This, in turn, could help identify patients who are likely to not respond to chemotherapy for alternative therapies such as radiation or immunotherapy.”

Related Links:
Case Western Reserve University School of Engineering
Cleveland Clinic