Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
Department of Electrical and Computer Eng., Duke University, Durham, North Carolina, USA.
J Magn Reson Imaging. 2019 Aug;50(2):456-464. doi: 10.1002/jmri.26636. Epub 2019 Jan 16.
Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development.
To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer.
Case-control study.
In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years.
FIELD STRENGTH/SEQUENCE: 5 T or 3.0 T T -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences.
Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available.
Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared.
The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers.
Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI.
3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.
初步研究表明，在接受高风险筛查 MRI 的女性中，放射科医生评估的背景实质增强（BPE）可预测未来的乳腺癌。与人工读者相比，算法评估的 BPE 测量值更精确、更具可重复性，因此可能会提高未来癌症发展的预测性能。
确定高风险女性筛查性乳腺 MRI 上 BPE 的算法提取成像特征是否与随后发生的癌症有关。
病例对照研究。
共有 133 名患有乳腺癌高风险的女性；在 2 年的随访期间，其中 46 名患者随后患上了乳腺癌。
磁场强度/序列：5T 或 3.0T T1 加权对比前脂肪饱和和非脂肪饱和序列及对比后非脂肪饱和序列。
使用计算机算法提取 BPE 的自动特征。还获得了 5 位乳腺放射科医生（对临床结果不知情）的主观 BPE 评分。
使用自动特征和接收器工作特征（ROC）分析进行的多元逻辑回归模型的留一法交叉验证，以计算曲线下面积（AUC）。使用广义回归模型比较自动特征和主观特征，并获得 P 值。比较自动特征和主观特征的优势比。
该多变量模型能够区分患有癌症的患者和未患有癌症的患者，AUC 为 0.70（95%置信区间：0.60-0.79，P<0.001）。与读者的主观 BPE 评估相比，这些影像学特征仍然是预测癌症后续发展的独立因素（P<0.003）。
自动提取的 BPE 测量值可能可用于进一步分层接受高风险筛查 MRI 的患者的风险。
3 技术功效：第 5 阶段 J. 磁共振成像 2019；50:456-464。
