Objective: To investigate whether texture analysis based on contrast-enhanced MRI can predict pathological complete response of locally advanced breast cancer undergoing neoadjuvant chemotherapy(NAC). Methods: Forty-seven patients with breast cancer undergone neoadjuvant chemotherapy from January 2015 to February 2016 were divided into pathological complete response (pCR) group or non-pathological complete response (non-pCR) group based on surgical pathology. Their parameters of texture analysis based on MRI before neoadjuvant chemotherapy and after 2 cycles of treatment were analyzed. Parameters(Energy, Entropy, Inertia, Correlation, Inverse Difference Moment)before and after 2 cycles of NAC between pCR and non-pCR groups were compared using Student t or Wilcoxon rank sum test. The diagnostic performance of different parameters was judged by the receiver-operating characteristic (ROC) curve analysis. Results: The post-NAC value was significantly different from that of pre-NAC (all P<0.05). Pre-treatment parameters (Energy, Entropy, Inertia, Correlation, Inverse Difference Moment) were 78.58×10(-5)(55.64×10(-5), 135.23×10(-5)), 10.06 ± 1.02, 7 993.91±2 428.10, (4.76±0.99) ×10(-5) and (18.10±4.13) ×10(-3) in pCR group, and 76.84×10(-5) (48.68×10(-5), 154.15×10(-5)), 10.28±1.26, 7 184.77 (4 938.03, 9 974.04), (5.21±2.01) ×10(-5) and (17.68±5.87) ×10(-3) in non-pCR group. No significant difference was found between both groups. (P>0.05 for all). At the end of the second cycle of NAC, parameters(Energy, Entropy, Inertia, Correlation, Inverse Difference Moment) were (542.11±361.04) ×10(-5,) 7.95±1.28, 16 765.08±97 06.56, (0.43±0.07) ×10(-5,) and (12.18±9.82) ×10(-3) in pCR group, and 133.00×10(-5) (79.80×10(-5,) 239.00×10(-5)), 9.29±1.46, 7 916.64(6 418.89, 10 934.40), (0.38±0.08) ×10(-5) and (14.80±5.06) ×10(-3) in non-pCR group. At the end of the second cycle of NAC, there was significant difference in the parameters (Energy, Entropy, Inertia, Correlation) and Δparameters (ΔEnergy, ΔEntropy, ΔInertia, ΔInverse Difference Moment) between both groups (P<0.05 for all). The area under curve (AUC) of post-treatment ΔEntropy was 0.81, which was the largest one among parameters. Sensitivity of ΔEntropy for predicting pCR was 75.0% and specificity was 85.7%, respectively. Conclusion: Texture analysis based on dynamic contrast-enhanced MRI can predict early treatment response in primary breast cancer.
目的： 探讨动态增强磁共振成像(DCE-MRI)纹理分析早期预测乳腺癌新辅助化疗(NAC)疗效的可行性。 方法： 2015年1月至2016年2月间，47例行NAC的乳腺癌患者根据手术病理分为病理完全缓解(pCR)组和非病理完全缓解(non-pCR)组，分别测量NAC前和2个周期后的MRI纹理参数(能量、熵、惯量、相关和逆差距)，其中正态分布用x±s表示，非正态分布用中位数和四分位数[M(P(25)，P(75)]表示。比较NAC前和2个周期后的MRI纹理参数变化差异，并通过绘制受试者工作特征曲线(ROC)得到最佳预测参数及其诊断阈值。 结果： 47例乳腺癌患者NAC前和NAC 2个周期后的纹理参数变化明显，差异均有统计学意义(均P<0.001)。化疗前，pCR组的能量、熵、惯量、相关和逆差距分别为78.58×10(－5)(55.64×10(－5,) 135.23×10(－5))、10.06±1.02、7 993.91±2 428.10、(4.76±0.99)×10(－5)和(18.10±4.13)×10(－3)，non-pCR组分别为76.84×10(－5)(48.68×10(－5)，154.15×10(－5))、10.28±1.26、7 184.77(4 938.03，9 974.04)、(5.21±2.01)×10(－5)和(17.68±5.87)×10(－3)，两组间差异均无统计学意义(均P>0.05)。NAC 2个周期后，pCR组的能量、熵、惯量、相关和逆差距分别为(542.11±361.04)×10(－5)、7.95±1.28、16 765.08±9 706.56、(0.43±0.07)×10(－5)和(12.18±9.82)×10(－3)，non-pCR组分别为133.00×10(－5)(79.80×10(－5)，239.00×10(－5))、9.29±1.46、7 916.64(6 418.89，10 934.40)、(0.38±0.08)×10(－5)和(14.80±5.06)×10(－3)，除了逆差距外，能量、熵、惯量和相关间差异均有统计学意义(均P<0.05)。NAC 2个周期后，pCR组和non-pCR组的Δ能量、Δ熵、Δ惯量和Δ逆差距间差异均有统计学意义(均P<0.05)。ROC曲线分析显示，NAC 2个周期后Δ熵的AUC最大(0.81)，其预测pCR的敏感度为75.0%，特异度为85.7%。 结论： DCE-MRI纹理分析能够早期预测乳腺癌NAC疗效。.
Keywords: Breast neoplasms; Neoadjuvant chemotherapy; Pathological complete response; Texture analysis.