Objective <p>This study aimed to identify distinctive computed tomography (CT) features associated with human epidermal growth factor receptor 2 (HER2) status in gallbladder cancer (GBC) that could serve as noninvasive imaging biomarkers.</p> Materials and methods <p>This study included 213 patients with pathologically confirmed GBCs with availability of HER2 status (171 HER2-negative, 42 HER2-positive). Pre-treatment contrast-enhanced CT scans were evaluated by two radiologists blinded to HER2 status. Multivariate analysis was performed using logistic regression with L2 regularization. Model discrimination was assessed using receiver operating characteristic (ROC) analysis, and internal validation was performed using bootstrap resampling (1,000 iterations) to correct for optimism.</p> Results <p>HER2-positive tumors exhibited larger lymph nodes (1.93 ± 0.79&#xa0;cm vs. 1.61 ± 0.61&#xa0;cm, <i>p</i> = 0.015), less frequent gallstones (14.3% vs. 35.7%, <i>p</i> = 0.013), arterial phase hyperenhancement (20.0% vs. 44.1%, <i>p</i> = 0.026), mass-like morphology (35.7% vs. 55.6%, <i>p</i> = 0.033), and more frequent biliary compression by lymph nodes (19.0% vs. 4.1%, <i>p</i> = 0.002). Multivariate analysis identified biliary compression by lymph nodes as the strongest positive predictor of HER2 positivity [odds ratio (OR) 2.99, 95% CI: 1.25–7.04], while arterial phase hyperenhancement (OR 0.40, 95% CI: 0.19–0.75), gallstone presence (OR 0.40, 95% CI: 0.18–0.75), and mass-like morphology (OR 0.54, 95% CI: 0.29–0.95) were significant negative predictors. The model demonstrated good discrimination (area under the ROC curve 0.782) with sensitivity 75%, specificity 79.4%, and negative predictive value 96.2%.</p> Conclusion <p>HER2-positive GBCs display characteristic CT findings that can be utilized for noninvasive diagnosis with robust predictive performance.</p>

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Radiological signature of HER2-Positive gallbladder cancer: analysis of CT features

  • Pankaj Gupta,
  • Niharika Dutta,
  • Shravya Singh,
  • Nikita Pradhan,
  • Ruby Siddiqui,
  • Ajay Gulati,
  • Naveen Kalra,
  • Gaurav Prakash,
  • Thakur Yadav,
  • Lileswar Kaman,
  • Santosh Irrinki,
  • Harjeet Singh,
  • Parikshaa Gupta,
  • Uma Nahar,
  • Ritambhra Nada,
  • Divya Khosla,
  • Rakesh Kapoor,
  • Rajender Basher,
  • Rajesh Gupta,
  • Radhika Srinivasan,
  • Manavjit Sandhu,
  • Usha Dutta

摘要

Objective

This study aimed to identify distinctive computed tomography (CT) features associated with human epidermal growth factor receptor 2 (HER2) status in gallbladder cancer (GBC) that could serve as noninvasive imaging biomarkers.

Materials and methods

This study included 213 patients with pathologically confirmed GBCs with availability of HER2 status (171 HER2-negative, 42 HER2-positive). Pre-treatment contrast-enhanced CT scans were evaluated by two radiologists blinded to HER2 status. Multivariate analysis was performed using logistic regression with L2 regularization. Model discrimination was assessed using receiver operating characteristic (ROC) analysis, and internal validation was performed using bootstrap resampling (1,000 iterations) to correct for optimism.

Results

HER2-positive tumors exhibited larger lymph nodes (1.93 ± 0.79 cm vs. 1.61 ± 0.61 cm, p = 0.015), less frequent gallstones (14.3% vs. 35.7%, p = 0.013), arterial phase hyperenhancement (20.0% vs. 44.1%, p = 0.026), mass-like morphology (35.7% vs. 55.6%, p = 0.033), and more frequent biliary compression by lymph nodes (19.0% vs. 4.1%, p = 0.002). Multivariate analysis identified biliary compression by lymph nodes as the strongest positive predictor of HER2 positivity [odds ratio (OR) 2.99, 95% CI: 1.25–7.04], while arterial phase hyperenhancement (OR 0.40, 95% CI: 0.19–0.75), gallstone presence (OR 0.40, 95% CI: 0.18–0.75), and mass-like morphology (OR 0.54, 95% CI: 0.29–0.95) were significant negative predictors. The model demonstrated good discrimination (area under the ROC curve 0.782) with sensitivity 75%, specificity 79.4%, and negative predictive value 96.2%.

Conclusion

HER2-positive GBCs display characteristic CT findings that can be utilized for noninvasive diagnosis with robust predictive performance.