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Abstract：
This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers.
This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls.
FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001).
FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.
This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls.
FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001).
FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.
摘要：
这项研究调查了定量乳腺密度（BD）是否通过预测间隔作为更密集的乳腺癌筛查的成像生物标志物，和淋巴结阳性癌症。
这项对1204名47-73岁女性进行的病例对照研究包括599例癌症病例（302例筛查，297间隔；239节点阳性，360个节点-负)和605个控件。自动BD软件计算纤维腺体体积（FGV），体积乳腺密度（VBD）和密度等级（DG）。放射科医生使用视觉模拟量表（VAS）从0到100评估BD。Logistic回归和受试者工作特征曲线下面积（AUC）确定BD是否可以预测检测模式（屏幕检测或间隔）；淋巴结阴性癌症；淋巴结阳性癌症，和所有的癌症与controls.
FGV,VBD,VAS,和DG均与对照区分间隔癌(均p<0.01)。仅FGV-四分位数区分屏幕检测到的癌症(p<0.01)。基于AUC，FGV比VBD或VAS更好地区分所有癌症类型。FGV对间期癌症的辨别能力显著增强，AUC=0.65，比屏幕检测到的癌症，AUC=0.61（p<0.01），VBD（分别为0.63和0.53，p<0.001)。
FGV,VBD,VAS和DG区分间隔癌症与对照，反映了一些掩盖风险。只有FGV可以区分屏幕检测到的癌症，这可能会增加乳腺癌风险的独特组成部分。
这项对1204名47-73岁女性进行的病例对照研究包括599例癌症病例（302例筛查，297间隔；239节点阳性，360个节点-负)和605个控件。自动BD软件计算纤维腺体体积（FGV），体积乳腺密度（VBD）和密度等级（DG）。放射科医生使用视觉模拟量表（VAS）从0到100评估BD。Logistic回归和受试者工作特征曲线下面积（AUC）确定BD是否可以预测检测模式（屏幕检测或间隔）；淋巴结阴性癌症；淋巴结阳性癌症，和所有的癌症与controls.
FGV,VBD,VAS,和DG均与对照区分间隔癌(均p<0.01)。仅FGV-四分位数区分屏幕检测到的癌症(p<0.01)。基于AUC，FGV比VBD或VAS更好地区分所有癌症类型。FGV对间期癌症的辨别能力显著增强，AUC=0.65，比屏幕检测到的癌症，AUC=0.61（p<0.01），VBD（分别为0.63和0.53，p<0.001)。
FGV,VBD,VAS和DG区分间隔癌症与对照，反映了一些掩盖风险。只有FGV可以区分屏幕检测到的癌症，这可能会增加乳腺癌风险的独特组成部分。
