• Postgrad Med J · Apr 2024

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

    • Jing Chen, Zhibin Huang, Hui Luo, Guoqiu Li, Zhimin Ding, Hongtian Tian, Shuzhen Tang, Sijie Mo, Jinfeng Xu, Huaiyu Wu, and Fajin Dong.
    • Ultrasound Department, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China.
    • Postgrad Med J. 2024 Apr 22; 100 (1183): 309318309-318.

    BackgroundThe 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.PurposeThis study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI.MethodA 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.ResultsThe 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.ConclusionsThe calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.© The Author(s) 2024. Published by Oxford University Press on behalf of Postgraduate Medical Journal.

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