Antone Nicoleta Zenovia, Pintican Roxana, Manole Simona, Fodor Liviu-Andrei, Lucaciu Carina, Roman Andrei, Trifa Adrian, Catana Andreea, Lisencu Carmen, Buiga Rares, Vlad Catalin, Achimas Cadariu Patriciu
Department of Oncological Surgery and Oncological Gynecology, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania.
Breast Cancer Center, Prof. Dr Ion Chiricuta Oncology Institute, 400015 Cluj-Napoca, Romania.
Cancers (Basel). 2025 Mar 18;17(6):1019. doi: 10.3390/cancers17061019.
Breast cancer (BC) is the most frequently diagnosed cancer in women and the leading cause of cancer-related deaths in women globally. Carriers of P/LP variants in the , , , , , , and genes have an increased risk of developing BC, which is why more and more guidelines recommend prophylactic mastectomy in this group of patients. Because traditional genetic testing is expensive and can cause delays in patient management, radiomics based on diagnostic imaging could be an alternative. This study aims to evaluate whether ultrasound-based radiomics features can predict P/LP variant status in BC patients. : This retrospective study included 88 breast tumors in patients tested with multigene panel tests, including all seven above-mentioned genes. Ultrasound images were acquired prior to any treatment, and the tumoral and peritumoral areas were used to extract radiomics data. The study population was divided into P/LP and non-P/LP variant groups. Radiomics features were analyzed using machine learning models, alone or in combination with clinical features, with the aim of predicting the genetic status of BC patients. : We observed significant differences in radiomics features between P/LP- and non-P/LP-variant-driven tumors. The developed radiomics model achieved a maximum mean accuracy of 85.7% in identifying P/LP variant carriers. Including features from the peritumoral area yielded the same maximum accuracy. : Radiomics models based on ultrasound images of breast tumors may provide a promising alternative for predicting P/LP variant status in BC patients. This approach could reduce dependence on costly genetic testing and expedite the diagnostic process. However, further validation in larger and more diverse populations is needed.
乳腺癌（BC）是女性中最常被诊断出的癌症，也是全球女性癌症相关死亡的主要原因。在 、 、 、 、 、 和 基因中携带致病性/可能致病性（P/LP）变异的个体患 BC 的风险增加，这就是越来越多的指南建议对这组患者进行预防性乳房切除术的原因。由于传统基因检测成本高昂且可能导致患者管理延迟，基于诊断成像的放射组学可能是一种替代方法。本研究旨在评估基于超声的放射组学特征是否能够预测 BC 患者的 P/LP 变异状态。：这项回顾性研究纳入了 88 例接受多基因检测的患者的乳腺肿瘤，检测基因包括上述所有七个基因。在任何治疗之前采集超声图像，并使用肿瘤及瘤周区域提取放射组学数据。研究人群被分为 P/LP 变异组和非 P/LP 变异组。使用机器学习模型单独或结合临床特征分析放射组学特征，目的是预测 BC 患者的基因状态。：我们观察到 P/LP 变异驱动的肿瘤和非 P/LP 变异驱动的肿瘤在放射组学特征上存在显著差异。所开发的放射组学模型在识别 P/LP 变异携带者方面的最大平均准确率达到了 85.7%。纳入瘤周区域的特征也得到了相同的最大准确率。：基于乳腺肿瘤超声图像的放射组学模型可能为预测 BC 患者的 P/LP 变异状态提供一种有前景的替代方法。这种方法可以减少对昂贵基因检测的依赖，并加快诊断过程。然而，需要在更大且更多样化的人群中进行进一步验证。
