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Updated: Mar 15, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Integrating Generative AI into Nomograms for Breast Cancer Nodal Risk Predictions.

Anika Shah¹, Jordan A Love^1,2, Matthew Butler^1,2,3

¹Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.

Annals of Surgical Oncology

|March 13, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) can extract breast cancer sentinel lymph node (SLN) metastasis data but need specific prompts for accurate risk prediction. Current AI automation for nomogram-based risk may not outweigh optimization costs.

Keywords:

Artificial intelligence Breast cancer Nomograms Risk prediction

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Area of Science:

Oncology
Artificial Intelligence
Medical Informatics

Background:

Sentinel lymph node (SLN) metastasis nomograms aid early-stage breast cancer surgical decisions.
Nomogram use is limited by the effort required for variable review and input.
This study investigates the utility of large language models (LLMs) for SLN metastasis risk prediction.

Purpose of the Study:

To evaluate OpenAI's LLMs' ability to extract data for nomogram use.
To assess LLM performance in reproducing SLN metastasis rates using established nomograms.
To compare AI-generated risk estimates with physician-derived predictions.

Main Methods:

De-identified radiology and pathology notes from 20 early-stage breast cancer patients were analyzed.
Three LLM prompting strategies were tested, including direct estimation, nomogram use, and chain-of-thought reasoning.
AI-generated SLN metastasis risk predictions were compared against manual physician calculations.

Main Results:

OpenAI o1 extracted all clinical variables in 65-80% of cases, with tumor size frequently misidentified.
Initial LLM-physician agreement on risk prediction was low (0-10% exact matches), indicating moderate reliability (ICC, 0.57-0.62).
An optimized GPT-4o prompt significantly improved agreement (25% exact matches, 90% near agreement) and reliability (ICC, 0.95).

Conclusions:

LLMs can extract necessary nomogram inputs from clinical notes.
Task-specific prompt engineering is crucial for accurate AI-driven SLN metastasis risk estimation.
Current AI automation for nomogram-based risk prediction may require substantial resources for optimization.