Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization

Ebrahim Rasromani; Stella K. Kang; Yanqi Xu; Beisong Liu; Garvit Luhadia; Wan Fung Chui; Felicia L. Pasadyn; Yu Chih Hung; Julie Y. An; Edwin Mathieu; Zehui Gu; Carlos Fernandez‐Granda; Ammar A. Javed; Greg D. Sacks; Tamas Gonda; Chenchan Huang; Yiqiu Shen

doi:10.2214/ajr.25.34076

May 6, 2026American Journal of Roentgenology

Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization

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Key Points

This research aims to utilize large language models to enhance feature extraction and risk categorization for pancreatic cystic lesions (PCLs).
Developed fine-tuned large language models for parsing radiology reports.
Created structured registries from extracted data to facilitate population-level research.
Evaluated the models' performance in identifying and categorizing features of PCLs.
Demonstrated improved accuracy in feature extraction from radiology reports using language models.
Identified significant risk factors associated with PCLs, enhancing risk categorization capabilities.

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Implication

Demonstrates risk categorization of pancreatic cystic lesions using language models, suggesting potential for improved clinical decision-making.

Abstract

LLMs have the potential to enable creation of large structured registries from existing radiology reports to support population-level research on PCLs.

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Rasromani et al. (2026) studied this question.

synapsesocial.com/papers/69fd7e23bfa21ec5bbf065ae https://doi.org/10.2214/ajr.25.34076

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Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization

Key Points

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Implication

Abstract

Discussion

Cite This Study

Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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