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A Breast Cancer Risk Prediction Model Based on the Clinical Characteristics and Sonographic Features
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摘要:
目的 通过分析乳腺病灶超声征象及部分临床特征建立乳腺癌的风险预测模型。 方法 回顾性研究2007年7月至2009年1月于本院进行乳腺病灶切除活检术的连续性病例116例, 用多因素Logistic回归得到超声及部分临床征象(包括患者年龄、乳腺癌家族史、病灶硬度、活动度、形状、边界、方向、后方回声及钙化)中的独立危险因素, 提出乳腺癌风险预测模型, 并用受试者工作特征曲线评价模型效果。 结果 116例乳腺病灶中, 52例最终诊断为乳腺癌, 其中年龄大于50岁(OR=6.61, 95%可信区间1.07~40.72)、临床触诊质硬肿物(OR=6.56, 95%可信区间1.32~32.58)、超声声像图形态不规则(OR=19.93, 95%可信区间2.49~159.45)、边界模糊(OR=21.32, 95%可信区间1.98~230.14)、边缘成角或毛刺状(OR=31.33, 95%可信区间2.61~376.02)为乳腺癌的独立危险因素(P < 0.05)。据此建立乳腺癌风险预测模型, 该模型整体预测的准确性达96.7%。 结论 本研究建立的乳腺癌风险预测模型并提出的患乳腺癌风险独立危险因素, 在临床实践中具有较高的客观性和可操作性。 -
关键词:
- 乳腺癌 /
- 乳腺影像报告和数据系统 /
- 阳性预测值 /
- 相对危险度 /
- 风险预测模型
Abstract:Objective To propose a breast cancer risk prediction model by analyzing the clinical characteristics and sonographic features of breast lesions. Methods A total of 116 consecutive breast lesion samples obtained by biopsy in our hospital from July 2007 to January 2009 were retrospectively examined. Open biopsies were performed on each patient. The pathological results were used as the golden standard of diagnosis.Multivariate logistic regression analysis was used to identify the independent risk factors of breast cancer including age, family history of breast cancer, the hardness, mobility, shape, margin, orientation, posterior acoustic features, and calcification of the masses. The prediction model was developed and a receiver operating characteristic (ROC) curve was used to evaluate the efficacy of the prediction model. Results Of the 116 breast lesions examined, 52 breast lesions were diagnosed as breast cancer. The independent risk factors included the patient's age ofmore than 50 years old (OR=6.61, 95%CI 1.07-40.72), hard mass (OR=6.56, 95%CI 1.32-32.58), irregular shape (OR=19.93, 95%CI 2.49-159.45), instinct margins(OR=21.32, 95%CI 1.98-230.14) and angular or speculated margins (OR=31.33, 95%CI 2.61-376.02). The whole accuracy of this prediction model was 96.7%. Conclusions We developed a breast cancer risk prediction model and proposed independent risk factors, which can help predict the risk of breast cancer in clinical practices. -
图 1 37岁女性患者，超声检查发现左乳下象限实性肿物，质韧，将此超声特征代入风险预测模型，危险因素只有形态不规则，得出结论为非乳腺癌，该患者病理结果为乳腺纤维腺瘤伴腺病
A.灰阶超声显示低回声肿物，大小2.1 cm×1.3 cm，形态不规则，边界清晰；B.彩色多普勒血流显像示肿物内可见较丰富条状血流信号
图 2 56岁女性患者，超声检查发现右乳外上象限实性肿物，质硬，将此超声特征代入风险预测模型，危险因素有年龄大于50岁、质硬、形态不规则，得出结论为乳腺癌，该患者病理结果为浸润性导管癌
A.灰阶超声显示低回声肿物，2.9 cm×2.6 cm，形态不规则，边缘成角；B.彩色多普勒血流显像示肿物内可见较粗大穿入血流信号
表 1 116例乳腺病变声像图特征及BI-RADS分类的阳性预测值
项目 总例数 乳腺癌 阳性预测值
(%)是(例) 否(例) BI-RADS分类 BI-RADS 3 46 1 45 2.2% BI-RADS 4 48 29 19 60.4% BI-RADS 5 22 22 0 100% 年龄 ＜50岁 73 23 50 43.4% ≥50岁 43 29 14 67.4% 乳腺癌家族史 有 10 5 5 50.0% 无 106 47 59 44.3% 硬度 质软或质韧 52 11 41 21.2% 质硬 50 38 12 76.0% 活动度 好 63 14 49 22.2% 差 39 35 41 89.7% 回声类型 低回声 99 43 56 43.4% 混合回声 17 5 12 29.4% 形状 圆形或卵圆形 51 2 49 3.9% 不规则形 65 50 15 76.9% 方向 平行 101 39 62 38.6% 垂直 15 13 2 86.7% 边界 清晰 66 8 58 12.1% 模糊 17 14 3 82.4% 成角* 12 11 1 91.7% 毛刺状* 11 11 0 100% 小分叶状 10 8 2 80.0% 强回声晕 有 14 13 1 92.9% 无 102 39 63 38.2% 后方回声特征 增强 36 9 27 25.0% 衰减 18 12 6 66.7% 无变化 56 27 29 48.2% 复合型 6 4 2 66.7% 钙化 无或粗大 87 29 58 33.3% 微小 29 23 6 79.3% BI-RADS:乳腺影像报告和数据系统；*统计时将二者合并 
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表 2 多元Logistic回归分析预测乳腺癌可能性
危险因素 OR值 95%可信区间 P值 年龄大于50岁 6.61 1.07~40.72 0.042 质硬 6.56 1.32~32.58 0.021 形态不规则 19.93 2.49~159.45 0.005 边界模糊 21.32 1.98~230.14 0.012 边界成角或毛刺状 31.33 2.61~376.02 0.007 边缘呈小分叶状 4.59 0.48~43.67 0.185
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