Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients.Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3–5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives.Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups.Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted.
背景/目的：乳腺癌是女性最常见的恶性肿瘤，早期发现对改善预后至关重要。乳腺影像报告和数据系统（BI-RADS）标准化了报告流程，但BI-RADS 4类诊断面临重大挑战，其恶性风险范围为2%至95%。因此，该类别中大多数女性接受了最终被证明不必要的活检。本研究旨在评估呼气分析能否区分BI-RADS 4患者的恶性与良性病变。
方法：招募麦吉尔大学健康中心乳腺中心诊断为BI-RADS 3-5级的参与者，收集多份呼气样本。采用基于电子鼻技术的呼气分析仪获取呼吸图谱，所有诊断均通过影像学和病理学确认。研究构建了基于自编码器的模型，融合呼气数据与BI-RADS评分以预测恶性病变。通过重复交叉验证与集成投票评估模型性能，并优先保证灵敏度以降低假阴性率。
结果：研究分析了85名参与者的呼气样本，其中68例为活检确诊的良性病变患者，17例为活检确诊的乳腺癌患者（均属BI-RADS 4队列）。模型平均灵敏度达88%，特异性为75%，负预测值达97%。各BI-RADS 4亚组结果一致，高风险亚组中灵敏度表现尤为突出。
肿瘤(癌症)患者之家