ArticleAnnals of surgical oncology2026
Integrating Generative AI into Nomograms for Breast Cancer Nodal Risk Predictions.
Article in Annals of surgical 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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNomograms predicting the likelihood of sentinel lymph node (SLN) metastasis in early-stage breast cancer can aid surgical decision-making but are underused due to the burden of variable review and input. This study evaluated whether OpenAI's large language models (LLMs) can extract information required for nomogram use and reproduce the estimated rate of SLN metastasis using the Memorial Sloan Kettering and MD Anderson nomograms.
methodsThe study analyzed the de-identified radiology and pathology notes for 20 patients. Three prompts were tested: (1) o1 prompted to generate SLN metastasis estimates without nomograms, (2) o1 prompted to use the nomogram from online calculators with and without serial corrections, and (3) GPT-4o prompted to use chain-of-thought reasoning from nomogram variables and the corresponding point values from a pictorial nomogram. Artificial intelligence (AI) estimates of SLN metastasis rates were compared with a physician-expert's manual use of nomograms.
resultsOpenAI o1 captured all clinical variables in 65% of cases without serial correction (94-95% of individual variables) and 80% of cases with serial correction (96-97% of individual variables), with tumor size most frequently misidentified. Agreement between LLM-generated and physician-calculated risk prediction was low (exact matches in 0-10% of cases, near agreement in 20-45% of cases), indicating moderate rater reliability (intraclass correlation coefficient [ICC], 0.57-0.62). The optimized GPT-4o prompt demonstrated greater agreement (exact matches in 25% and near agreement in 90% of cases) and reliability (ICC, 0.95).
conclusionLarge language models can reliably extract nomogram inputs from clinical notes but require task-specific prompt engineering for accurate SLN metastasis risk estimation. Currently, automating nomogram-based risk estimators with AI may not justify the significant resources required for optimization.
Indexed as
Identifiers
41824209What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.