Scholarlink

Purpose <p>This study aims to improve hepatocellular carcinoma (HCC) diagnostic accuracy in non-high-risk populations by utilizing GPTs that incorporate integrated risk coefficients, and to explore its feasibility.</p> Material and methods <p>Between August 2016 and June 2019, patients with focal liver lesions (FLLs) in non-high-risk populations, confirmed by histopathology or clinical/imaging evidence, were retrospectively included. A logistic regression model was developed using baseline characteristics and contrast-enhanced ultrasound (CEUS) features to identify independent HCC risk factors. Three ChatGPT-based models were evaluated: ChatGPT 4o (a general-purpose model developed by OpenAI), BaseGPT (a customized model with HCC diagnostic knowledge), and RiskGPT (a further customized model integrating HCC knowledge and identified risk factors). Their intra-agreement and diagnostic performance were compared.</p> Results <p>Logistic regression identified male, obesity, HBcAb or HBeAb positivity, elevated alpha-fetoprotein, and mild washout on CEUS as associated with HCC. RiskGPT achieved the highest area under a receiver operating characteristic curve (AUC) (0.89) and demonstrated superior accuracy (90.3%) in HCC identification; significantly outperforming both ChatGPT 4o (AUC 0.79, <i>P</i> = 0.002; accuracy 83.1%, <i>P</i> = 0.02) and BaseGPT (AUC 0.81, <i>P</i> = 0.008; accuracy 80.6%, <i>P</i> = 0.002). RiskGPT demonstrated superior sensitivity compared to ChatGPT 4o (85.5% vs. 66.3%) and outperformed BaseGPT in specificity (92.7% vs. 80.6%) and positive predictive value (85.5% vs. 67.7%) (all <i>P</i> &lt; 0.001). Additionally, RiskGPT showed substantial intra-consistency in diagnosing FLLs, with a κ value of 0.78.</p> Conclusion <p>RiskGPT improves HCC diagnostic accuracy in non-high-risk patients by integrating clinical, imaging features, and risk coefficients, demonstrating significant diagnostic potential.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing hepatocellular carcinoma diagnosis in non-high-risk patients: a customized ChatGPT model integrating contrast-enhanced ultrasound

  • 期刊论文

  • 发表日期：

    2025年4月15日

查看全文
  • Meng-Fei Xian,
  • Wen-Tong Lan,
  • Zhe Zhang,
  • Ming-De Li,
  • Xin-Xin Lin,
  • Yang Huang,
  • Hui Huang,
  • Li-Da Chen,
  • Qing-Hua Huang,
  • Wei Wang

摘要

Purpose

This study aims to improve hepatocellular carcinoma (HCC) diagnostic accuracy in non-high-risk populations by utilizing GPTs that incorporate integrated risk coefficients, and to explore its feasibility.

Material and methods

Between August 2016 and June 2019, patients with focal liver lesions (FLLs) in non-high-risk populations, confirmed by histopathology or clinical/imaging evidence, were retrospectively included. A logistic regression model was developed using baseline characteristics and contrast-enhanced ultrasound (CEUS) features to identify independent HCC risk factors. Three ChatGPT-based models were evaluated: ChatGPT 4o (a general-purpose model developed by OpenAI), BaseGPT (a customized model with HCC diagnostic knowledge), and RiskGPT (a further customized model integrating HCC knowledge and identified risk factors). Their intra-agreement and diagnostic performance were compared.

Results

Logistic regression identified male, obesity, HBcAb or HBeAb positivity, elevated alpha-fetoprotein, and mild washout on CEUS as associated with HCC. RiskGPT achieved the highest area under a receiver operating characteristic curve (AUC) (0.89) and demonstrated superior accuracy (90.3%) in HCC identification; significantly outperforming both ChatGPT 4o (AUC 0.79, P = 0.002; accuracy 83.1%, P = 0.02) and BaseGPT (AUC 0.81, P = 0.008; accuracy 80.6%, P = 0.002). RiskGPT demonstrated superior sensitivity compared to ChatGPT 4o (85.5% vs. 66.3%) and outperformed BaseGPT in specificity (92.7% vs. 80.6%) and positive predictive value (85.5% vs. 67.7%) (all P < 0.001). Additionally, RiskGPT showed substantial intra-consistency in diagnosing FLLs, with a κ value of 0.78.

Conclusion

RiskGPT improves HCC diagnostic accuracy in non-high-risk patients by integrating clinical, imaging features, and risk coefficients, demonstrating significant diagnostic potential.