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We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com. Back to Healio Key takeaways: About a third of patients do not have a biomarker used to track treatment progress in pancreatic cancer. An AI biomarker derived from routinely collected data predicted treatment completion and survival. Approximately a third of patients with pancreatic ductal adenocarcinoma do not have elevated or detectable levels of a biomarker commonly used to measure response to therapy. An AI tumor marker derived from routinely collected data could provide prognostic information on treatment completion and survival similar to that seen in patients who have measurable cancer antigen 19-9 (CA19-9).
Data derived from Thalji SZ, et al. JAMA Surg. 2026;doi:10.1001\/jamasurg.2026.0291.
Anai N. Kothari \u201cThis behaves just like CA19-9 does clinically,\u201d Anai N. Kothari, MD, MS, assistant professor of surgical oncology at Medical College of Wisconsin, told Healio. \u201cIt tracks response, predicts outcomes and can help guide treatment decisions. This really is a step forward using AI, not just to predict outcomes, but to fill in some critical gaps in clinical care using data we already have.\u201d \u2018A big gap\u2019 Pancreatic ductal adenocarcinoma (PDAC) tumors can exhibit significant changes on imaging, so clinicians use biomarker CA19-9, a serum blood value, to measure response to therapy and make prognostic assessments, according to study background. However, about 30% of patients with PDAC do not produce elevated levels of CA19-9, including 10% who do not have any detectable CA19-9. \u201cThere\u2019s no high-quality biomarker in that patient population to follow,\u201d Kothari said. \u201cInstead, [clinicians use] a lot of intermittent screening tests, imaging that ends up being costly and challenging for the patients to do. They may miss an opportunity to change treatment in someone who\u2019s not responding. This is a big gap that needed to be filled.\u201d Kothari and colleagues investigated whether AI could use other common laboratory data routinely collected along with CA19-9 to find a surrogate biomarker, which they called electronic CA19-9 (e19-9). They trained the model on 3,239 patients with PDAC and elevated C19-9. The cohort had a combined 21,555 unique CA19-9 values. Researchers then conducted external validation on 4,384 similar patients with 16,487 unique CA19-9 values. Finally, they tested the model on 121 patients with localized PDAC (median age, 65 years; interquartile range, 14; 51% men; 89% white) who did not have elevated CA19-9 (less than 35 U\/mL). Completion of all neoadjuvant treatment and surgery, metastatic progression and OS served as primary endpoints. \u2018Highly usable tool\u2019 Overall, 77% of patients in the test cohort completed neoadjuvant therapy, including surgery. Individuals who finished treatment had similar e19-9 values before therapy as those who did not complete treatment (median, 121 vs. 134.1). Kothari and colleagues found patients who had at least a 50% decline in e19-9 during treatment had a significantly higher likelihood of finishing (OR = 5; 95% CI, 1.6-15.66). Additionally, patients who had posttreatment e19-9 levels less than 100 had nearly 20 times the likelihood of completing therapy, including surgery (OR = 19.31; 95% CI, 5.8-64.26). Metastatic progression occurred significantly less in patients who had any decline in e19-9 (47% vs. 80%; P = .01), those who had a 50% decrease (24% vs. 56%; P = .01), and those with levels less than 100 (29% vs. 80%; P < .001). OS significantly increased among patients who had any decline in e19-9 (median, 49 months vs. 22 months; P = .03), those with a 50% reduction (median, 53 months vs. 32 months; P = .007), and those with posttreatment levels less than 100 (median, 60 months vs. 16 months; P < .001). E19-9 less than 100 had a significant association with improved OS (HR = 0.49; 95% CI, 0.25-0.97). \u201cThis is a highly usable tool, and it sets the stage for a lot more innovation and impact by using AI the right and responsible way,\u201d Kothari said. \u201cBefore, for this 30% of patients who have pancreas cancer, we didn\u2019t have a reliable way of making these estimates over time. Now, the data show we have a biomarker for that 30%. We have something that can guide treatment decision-making, to look at things over the course of their therapy, and to be able to give them some idea of the probability that they would make it to surgery, or what their survival could look like.\u201d Researchers acknowledged study limitations, including e19-9 being based on a predictive model. Kothari noted the importance of further prospective analysis at different institutions. He also highlighted the impact this could have on other malignancies. \u201cWhy stop at pancreas cancer?\u201d he asked. \u201cWe know that tumor biomarkers are important parts of other cancers, and similar to the problem in pancreas cancer, there\u2019s a subset of patients who don\u2019t have a meaningful ability to use that biomarker. There are other populations we can help with this same methodology. The key is that we\u2019re not drawing any new information, new labs. We\u2019re just using the data we already have to be able to provide this information.\u201d This is the benefit AI can provide. \u201cI\u2019ve been studying artificial intelligence for over a decade,\u201d Kothari said. \u201cThere\u2019s always this promise that AI was going to identify information that the human eye could not see. For an individual patient with cancer, we now gather up to a million data points as part of their treatment. We can use AI to see patterns, then augment that with our human expertise to improve not just cancer outcomes, but health outcomes overall. Instead of treating everyone exactly the same, we really are homing in on what\u2019s personal and unique about that individual.\u201d For more information: Anai N. Kothari, MD, MS, can be reached at akothari@mcw.edu. 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