Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy

Abstract

Abstract Background The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. Purpose This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. Method A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. Results The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88–0.95), 0.92 (95% CI: 0.89–0.95), and 0.97 (95% CI: 0.96–0.99) for Models 1–3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91–0.98), 0.89 (95% CI: 0.83–0.96), and 0.97 (95% CI: 0.95–0.99) for Models 1–3. Conclusions The calibration curves demonstrate that the model’s predictions agree with the actual values. Decision curve analysis suggests a good clinical application.