ArticleESMO real world data and digital oncology2026
Estimate renal cell carcinoma recurrence rates using electronic health records.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Lack of readily available recurrence data has limited the use of electronic health records (EHR) for risk assessment of cancer recurrence and optimal patient management. This study aims to derive high-quality EHR recurrence data and estimate recurrence rates in overall population and specific subgroups. Materials and methods: Using EHR data between 1 January 2000 and 1 September 2022, we developed a computational tool for automatically annotating the renal cell carcinoma (RCC) recurrence outcome and a natural language processing (NLP) tool for extracting key RCC characteristics. Using data constructed from stage I-III RCC patients who underwent nephrectomy at Mass General Brigham (2000-2022), we analyzed recurrence rates by TNM (tumor-node-metastasis) stage, grade, and histological subtype. Analyses were conducted from 1 September 2022 to 16 August 2024. Results: A total of 5603 patients whose EHR met the eligibility criteria were included in the study [3590 (64%) men, 2013 (36%) women; median age at baseline 62 years (range 36-87 years); 4225 (75%) non-Hispanic white, 1378 (25%) other race-ethnicity. Tumor stage was as follows: 3324 (59%) stage I, 778 (14%) stage II, and 128 (2%) stage III, 1373 (25%) missing stage information]. Among patients with TNM stage T1-3 N0M0 clear-cell RCC any grade, EHR-derived recurrences were indicative for true recurrence with area under the receiver operating characteristic curve (AUC) of 0.914 for 5-year recurrence status cross-validated against expert annotated gold standard recurrence times. The estimated overall 5-year recurrence rate was 11.1%. We observe a substantially higher recurrence risk for T3 group (48.8%) versus T1 (2.8%) or T2 (14.2%) and G4 group (45.3%) versus G1 (3.7%), G2 (6.8%), or G3 (18.9%). Conclusions: Our computational approach demonstrates that high-quality recurrence data can be reliably extracted from EHR systems, providing a scalable solution for real-world RCC risk determination. These tools enable health care systems to better identify high-risk patients and potentially guide personalized follow-up strategies and adjuvant treatment options.
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