Faculty Collaboration Database

AI-Derived Electronic Tumor Marker For Cancer Antigen 19-9 Nonproducers With Pancreatic Ductal Adenocarcinoma. JAMA Surg 2026 May 01;161(5):508-516

Date

03/18/2026

Pubmed ID

41848749

Pubmed Central ID

PMC13000744

DOI

10.1001/jamasurg.2026.0291

Scopus ID

2-s2.0-105034154038 (requires institutional sign-in at Scopus site)   4 Citations

Abstract

IMPORTANCE: Cancer antigen 19-9 (CA19-9) is used to assess treatment response among patients with pancreatic ductal adenocarcinoma (PDAC); however, nearly 30% of patients with PDAC do not produce elevated CA19-9.

OBJECTIVE: To develop, validate, and apply an electronic tumor marker (e19-9) derived from routine laboratory data available in the electronic health record to assess treatment response and predict outcomes among patients with PDAC who do not produce CA19-9.

DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, an artificial intelligence (AI) model was trained using routinely collected serum laboratory data from patients with PDAC and elevated CA19-9. The model was externally validated and then applied to a separate cohort of CA19-9 nonproducers. Model development and internal testing were conducted at a single institution using patient data from 2010 to 2022. External validation used a deidentified patient network across 58 health care organizations over the same period. The training cohort included 3239 patients with pancreatic cancer and elevated CA19-9. The external validation cohort included 4384 similar patients. The model was applied to 121 patients with resectable or borderline resectable PDAC who did not produce elevated CA19-9 and received neoadjuvant therapy with curative intent. These data were analyzed from November 2021 through March 2025.

MAIN OUTCOMES AND MEASURES: Model performance was assessed using root mean square error and R2. Clinical outcomes included completion of all neoadjuvant treatment and surgery, metastatic progression, and overall survival (OS).

RESULTS: The final fitted model demonstrated stable performance across both internal and external validation cohorts. Among 121 patients (59 female and 62 male) with localized PDAC who did not produce elevated CA19-9, a 50% or more decline in e19-9 (area under the curve [AUC], 0.79) and e19-9 level of less than 100 (AUC, 0.84) were objectively determined cut points associated with prognosis. A total of 93 patients (77%) completed all planned neoadjuvant therapy and surgery. A 50% or more decline in e19-9 levels and an e19-9 level less than 100 was associated with completion of all intended therapy (odds ratio [OR], 5.00; 95% CI, 1.60-15.66; P = .006 and OR, 19.31; 95% CI, 5.80-64.26; P < .001). An e19-9 level less than 100 was independently associated with OS (hazard ratio, 0.49; 95% CI, 0.25-0.97; P = .04).

CONCLUSIONS AND RELEVANCE: In this study, e19-9 was a noninvasive AI-derived marker that may provide accurate and relevant information to assess treatment response for the approximately 30% of patients with PDAC who do not produce CA19-9 at elevated levels. The development and validation of scalable, noninvasive screening methods using machine-learning algorithms may pave the way for early detection, prognostication, and treatment of cancers.

Author List

Thalji SZ, Aldakkak M, Ramamurthi A, Jaraczewski TJ, Belbahri M, SenthilKumar G, Shaik T, Merrill JR, Banerjee A, Taylor BW, Kamgar M, George B, Erickson B, Hall WA, Lytle NK, Seo YD, Christians KK, Clarke CN, Evans DB, Tsai S, Kothari AN

Authors

Mohammed Aldakkak MD Assistant Professor in the Surgery department at Medical College of Wisconsin
Anjishnu Banerjee PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Callisia N. Clarke MD Chief, Associate Professor in the Surgery department at Medical College of Wisconsin
Beth A. Erickson MD Professor in the Radiation Oncology department at Medical College of Wisconsin
William Adrian Hall MD Chair, Professor in the Radiation Oncology department at Medical College of Wisconsin
Mandana Kamgar MPH, MD Associate Professor in the Medicine department at Medical College of Wisconsin
Anai N. Kothari MD Associate Professor in the Surgery department at Medical College of Wisconsin
Nikki K. Lytle PhD Assistant Professor in the Surgery department at Medical College of Wisconsin
Bradley W. Taylor Chief Research Informatics Officer in the Clinical and Translational Science Institute department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adenocarcinoma
Aged
Area Under Curve
Artificial Intelligence
Biomarkers, Tumor
CA-19-9 Antigen
Carcinoma, Pancreatic Ductal
Electronic Health Records
Female
Humans
Male
Middle Aged
Pancreatic Neoplasms
Prognosis
Reproducibility of Results
Retrospective Studies
Software Validation