Background/Objective: This study aims to better characterize the clinical presentation, histology, and imaging features of breast sarcomas on mammography, ultrasound, and MRI, in addition to analyzing the effectiveness of AI DS in detecting breast sarcomas.Methods: A retrospective review from 2008–2024 yielded 18 patients with histologically proven breast sarcomas with imaging available for review. Mammography was available for 13 lesions, ultrasound for 19 lesions, and MRI for 9 lesions. Imaging features were classified according to the BI-RADS 5th edition lexicon. Images were reviewed by two radiologists, and consensus was obtained regarding imaging features. AI DS was retrospectively applied to the breast masses identified on ultrasound. Data analysis was performed using descriptive statistics.Results: 17 females and 1 male were included in this study. Mammographic findings varied from solitary masses (3/13 [23.1%]), asymmetries (3/13 [23.1%]), architectural distortion (1/13 [7.7%]), skin thickening (3/13 [23.1%]), focal asymmetry with calcifications (1/13 [7.7%]), or no suspicious findings (2/13 [15.4%]). Sonography often revealed masses with an irregular shape (13/16 [81.2%]), non-circumscribed margins (15/16 [93.7%]), hypoechoic echo pattern (10/16 [62.5%]), and vascular flow (12/16 [75%]). MRI showed heterogeneously enhancing masses (6/9 [66.7%]) or isolated skin enhancement (3/9 [33.3%]). AI DS analyzed 16 masses on ultrasound and identified 15 (93.8%) as suspicious.Conclusions: Breast sarcomas had a variable appearance on breast imaging, ranging from a solitary mass to isolated skin findings. Awareness of how breast sarcomas can present across imaging modalities while using AI DS as an aid may help radiologists in making the correct diagnosis of this rare and aggressive disease.
背景/目的：本研究旨在更全面地描述乳腺肉瘤在乳腺X线摄影、超声及磁共振成像中的临床表现、组织学及影像学特征，并分析人工智能辅助检测系统在识别乳腺肉瘤方面的有效性。 方法：通过回顾性分析2008年至2024年的病例，共纳入18例经组织学证实且具有可评估影像资料的乳腺肉瘤患者。其中13个病灶有乳腺X线摄影资料，19个病灶有超声资料，9个病灶有磁共振成像资料。影像特征依据第五版BI-RADS词典进行分类。由两名放射科医师独立评估影像并达成共识。对超声检出的乳腺肿块回顾性应用人工智能辅助检测系统进行分析。数据分析采用描述性统计方法。 结果：本研究共纳入17例女性患者和1例男性患者。乳腺X线摄影表现多样：孤立性肿块（3/13 [23.1%]）、不对称致密影（3/13 [23.1%]）、结构扭曲（1/13 [7.7%]）、皮肤增厚（3/13 [23.1%]）、伴钙化的局灶性不对称（1/13 [7.7%]）或无异常发现（2/13 [15.4%]）。超声常显示肿块形态不规则（13/16 [81.2%]）、边缘不清（15/16 [93.7%]）、低回声（10/16 [62.5%]）及血流信号（12/16 [75%]）。磁共振成像表现为不均匀强化肿块（6/9 [66.7%]）或孤立性皮肤强化（3/9 [33.3%]）。人工智能辅助检测系统分析了16个超声检出的肿块，其中15个（93.8%）被判定为可疑病变。 结论：乳腺肉瘤在影像学上表现多样，可从孤立性肿块到孤立性皮肤改变。在运用人工智能辅助检测系统辅助诊断的同时，充分认识乳腺肉瘤在不同影像模式中的表现特征，有助于放射科医师对这种罕见侵袭性疾病做出准确诊断。
肿瘤(癌症)患者之家