Optimizing Breast Cancer Diagnosis with Photoacoustic Imaging: An Analysis of Intratumoral and Peritumoral Radiomics

Abstract

BackgroundThe differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. PurposeThis study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and MethodsFrom January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3-5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2mm, 5mm, and 8mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. ResultsThe developed nomogram, combining 5mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5mm peritumoral region proving most effective in identifying malignant nodules. ConclusionThis research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.