Background/Objectives: The Wavelia Microwave Breast Imaging (MWBI) technology aims to increase sensitivity in dense breasts, where X-ray mammography is of limited value. Its potential contribution to the reduction in the false positives in breast cancer diagnosis, by developing MWBI image descriptors supporting malignant-to-benign lesion discrimination, is also being investigated. After a First-In-Human (FiH) study with interesting findings on a small dataset of 24 symptomatic breast lesions, an upgraded 2nd prototype of Wavelia was manufactured and tested on a larger and more diverse dataset, including 62 patients and a balanced distribution of malignant and benign symptomatic breast lesions. Methods: A set of technological and methodological evolutions, outlined in this article, was implemented in Wavelia#2 to handle the diversity in larger patient datasets. Multi-modal MWBI imaging is employed to parameterize the interaction mechanisms between the microwaves and the imaged breast at varying geometrical and tissue consistency conditions. MWBI Region-Of-Interest (ROI) extraction and characterization based on multidimensional radiomic feature vectors is implemented to expand the malignant-to-benign lesion diagnostics potential of MWBI compared to the limited scope of the FiH study with Wavelia#1, which employed three specific preselected features. Results: This study demonstrates significant diagnostic accuracy of multiple texture-based and intensity-based features to discriminate between malignant and benign breast lesions with Wavelia#2 MWBI. A phenomenological qualitative assessment of the false positive rate on healthy breasts is also presented for the MWBI technology for the first time. Conclusions: The analysis contributes to the rationalization of the MWBI imaging and image analysis outputs towards standardization, objective interpretability, and ultimate clinical acceptance.
背景/目的：Wavelia微波乳腺成像技术旨在提升对致密型乳腺的检测灵敏度，而X线乳腺摄影在此类乳腺中的诊断价值有限。该技术还通过开发支持恶性与良性病灶鉴别的微波乳腺成像图像描述符，探索其在降低乳腺癌诊断假阳性率方面的潜在贡献。在首次人体研究中，针对24例有症状乳腺病灶的小型数据集获得了具有启发性的发现，随后研发了升级版Wavelia第二代原型机，并在更大规模、更多样化的数据集上进行了测试，该数据集包含62例患者，且恶性与良性有症状乳腺病灶分布均衡。方法：本文概述了一系列技术和方法学演进，这些改进已应用于Wavelia#2系统，以应对更大规模患者数据集的多样性。研究采用多模态微波乳腺成像技术，在不同几何形态和组织一致性条件下参数化微波与受检乳腺的相互作用机制。通过基于多维影像组学特征向量的微波乳腺成像感兴趣区域提取与表征，拓展了该技术在恶性与良性病灶鉴别诊断方面的潜力，相较于采用三个特定预设特征的Wavelia#1首次人体研究的有限范围实现了显著提升。结果：本研究证明Wavelia#2微波乳腺成像技术中多种基于纹理和强度的特征在鉴别恶性与良性乳腺病灶方面具有显著诊断准确性。同时首次对微波乳腺成像技术在健康乳腺上的假阳性率进行了现象学定性评估。结论：该分析有助于推动微波乳腺成像及其图像分析输出结果的合理化进程，促进技术标准化、客观可解释性及最终的临床认可。
肿瘤(癌症)患者之家